Reviewerly/exHarmony huggingface dataset:
Enabling Machine Learning in Software Architecture Frameworks

Several architecture frameworks for software, systems, and enterprises have been proposed in the literature. They have identified various stakeholders and defined architecture viewpoints and views to frame and address stakeholder concerns. However, the Machine Learning (ML) and data science-related concerns of data scientists and data engineers are yet to be included in existing architecture frameworks. We interviewed 65 experts from around 25 organizations in over ten countries to devise and validate the proposed framework that addresses the mentioned shortcoming.
Automatically Resolving Data Source Dependency Hell in Large Scale Data Science Projects

Dependency hell is a well-known pain point in the development of large software projects and machine learning (ML) code bases are not immune from it. In fact, ML applications suffer from an additional form of dependency hell, namely, data source dependency hell. This term refers to the central role played by data and its unique quirks that often lead to unexpected failures of ML models which cannot be explained by code changes. In this paper, we present an automated data source dependency mapping framework that allows MLOps engineers to monitor the whole dependency map of their models in a fast paced engineering environment and thus mitigate ahead of time the consequences of any data source changes. Our system is based on a unified and generic approach, employing techniques from static analysis, from which data sources can be identified on a wide range of source artifacts. Our framework is currently deployed within Microsoft and used by Microsoft MLOps engineers in production.
Use of Blockchain Technology for Building a Hierarchical Structure on Most State Registries for the Purpose of Protection Against Forgery of Information

The work deals with the problem of ensuring the security of information storage in various registers, including state ones. First of all, we focused on security against attacks aimed at changing information in the registry, which is a constant and real threat today. The method proposed by us to prevent such attacks on changing information in the registry is based on the use of results from a relatively new direction in the field of information technology – blockchain technology. We propose building a so-called cascade structure of registers on blockchains using one of the newest consensus protocols called Proof-of-Proof. Such a cascade structure of registers, with the correct parameters of its construction and use, provides all the properties of correct information storage, such as non-failure, immutability, sequence of placement in the register, and others. After formulating the basic idea of building a cascade registry, we present a list of problems that need to be solved so that the probability of success of a substitution information attack is negligibly small.
Evaluation method of English online course teaching effect based on ResNet algorithm

In order to improve the accuracy of English online course teaching effect evaluation results, a paper proposed an English online course teaching effect evaluation method based on ResNet algorithm. The effect of College English online teaching was evaluated from five aspects: pre-class preparation, teaching content, basic skills, ability training, and teaching methods. Each evaluation item was set with seven levels of scoring standards. An evaluation model of the classroom teaching effect was constructed based on convolutional neural network according to the internal relationship between facial expression recognition and classroom teaching effect evaluation. The problem of network depth deepening affecting the accuracy of evaluation in convolutional neural network models was innovatively solved by utilizing the ResNet algorithm. The evaluation of the effectiveness of English online course teaching was achieved. The experimental results showed that this method could effectively improve the effect of English online course teaching evaluation and improve the teaching quality of English online courses.
Missing and not found

Abstract This paper offers novel insights on articlelessness in noun phrases in Dutch and German headlines. Modified noun phrases that lack a determiner in headlines exhibit adjectival agreement that cannot be explained if one assumes an article that is phonologically null or that has been PF-deleted. We describe the pattern, consider different analytical options and eventually conclude that the interpretation, distribution as well as the observed adjectival agreement characteristic of articlelessness noun phrases calls for an account in which the article is never projected to begin with.
INFORMACIONI SISTEM O ISTRAŽIVAČKOJ DJELATNOSTI U BOSNI I HERCEGOVINI: E-CRIS.BH I PERSONALNE BIBLIOGRAFIJE ISTRAŽIVAČA / CURRENT RESEARCH INFORMATION SYSTEM IN BOSNIA AND HERZEGOVINA: E-CRIS.BH AND PERSONAL BIBLIOGRAPHIES

The aim of this paper is to present the Information System on Research Activity in Bosnia and Herzegovina - E-CRIS.BH, and the total number of registered researchers, research organizations, departments and projects, but also the number of bibliographic records to which the typology of documents/works has been assigned within the mutual COBISS.BH database. E-CRIS.BH is an information system that represents an electronic database of researchers, research organizations, research departments and research projects. It is an information system on research activities in Bosnia and Herzegovina, which originated from the Register of Scientific Institutions, Scientific Staff, Scientific and Research Projects and Infrastructure Investments in the Federation of Bosnia and Herzegovina and the Register of Scientific Institutions, Scientific Staff, Scientific and Research Projects and Infrastructure Investments in Canton of Sarajevo. The Register of the Federation of Bosnia and Herzegovina is conceived as an umbrella system and the registers of the canton as its subsystems. The project was implemented by the Academy of Sciences and Arts of Bosnia and Herzegovina. The connection of E-CRIS.BH with the bibliographic database of COBISS.BH provides insight into the bibliographies of researchers, which are an integral part of every scientific work, a condition for synthesizing knowledge and data that lead to the required scientific results. The COBISS users have the opportunity to record researchers, research organizations, departments and projects necessary for the creation of a bibliography of researchers and organizations within their national library information systems in the COBISS.Net network (regional network). The results show that 2.230 researchers and 86 organizations have been registered so far in E-CRIS.BH. A total of 73.657 bibliographic records have been created so far within the COBISS.BH common database, which a typology of documents/works has been assigned to. In the first group – articles and other components – a typology was determined for 21.122 bibliographic records. In the second group – monographs and other completed works - a typology was determined for 52.372 bibliographic records. In the third group – performed works - (events) – a typology was determined for 163 bibliographic records.
Evaluating the use of Instagram images color histograms and hashtags sets for automatic image annotation

Color similarity has been a key feature for content-based image retrieval by contemporary search engines, such as Google. In this study, we compare the visual content information of images, obtained through color histograms, with their corresponding hashtag sets in the case of Instagram posts. In previous studies, we had concluded that less than 25% of Instagram hashtags are related to the actual visual content of the image they accompany. Thus, the use of Instagram images' corresponding hashtags for automatic image annotation is questionable. In this study, we are answering this question through the computational comparison of images' low-level characteristics with the semantic and syntactic information of their corresponding hashtags. The main conclusion of our study on 26 different subjects (concepts) is that color histograms and filtered hashtag sets, although related, should be better seen as a complementary source for image retrieval and automatic image annotation.
ESN-Observer-Based Adaptive Stabilization Control for Delayed Nonlinear Systems with Unknown Control Gain

This paper investigates the observer-based adaptive stabilization control problem for a class of time-delay nonlinear systems with unknown control gain using an echo state network (ESN). In order to handle unknown functions, a new recurrent neural network (RNN) approximation method called ESN is utilized. It improves accuracy, reduces computing cost, and is simple to train. To address the issue of unknown control gain, the Nussbaum function is used, and the Lyapunov–Krasovskii functionals are used to address the delay term. The backstepping strategy and command filtering methodology are then used to create an adaptive stabilization controller. All of the closed-loop system’s signals are predicted to be confined by the Lyapunov stability theory. Finally, a simulation example is used to demonstrate the effectiveness of the suggested control mechanism.
VR Training System to Help Improve Photography Skills

People aspiring to enhance their photography skills face multiple challenges, such as finding subjects and understanding camera-specific parameters. To address this, we present the VR Photo Training System. This system allows users to practice photography in a virtual space and provides feedback on user-taken photos using machine-learning models. These models, trained on datasets from the virtual environment, evaluate the aesthetics, composition, and color of photos. The system also includes a feature offering composition advice, which further aids in skill development. The evaluation and recommendation functions of our system have shown sufficient accuracy, proving its effectiveness for photography training.
Multi-View Learning-Based Fast Edge Embedding for Heterogeneous Graphs

Edge embedding is a technique for constructing low-dimensional feature vectors of edges in heterogeneous graphs, which are also called heterogeneous information networks (HINs). However, edge embedding research is still in its early stages, and few well-developed models exist. Moreover, existing models often learn features on the edge graph, which is much larger than the original network, resulting in slower speed and inaccurate performance. To address these issues, a multi-view learning-based fast edge embedding model is developed for HINs in this paper, called MVFEE. Based on the “divide and conquer” strategy, our model divides the global feature learning into multiple separate local intra-view features learning and inter-view features learning processes. More specifically, each vertex type in the edge graph (each edge type in HIN) is first treated as a view, and a private skip-gram model is used to rapidly learn the intra-view features. Then, a cross-view learning strategy is designed to further learn the inter-view features between two views. Finally, a multi-head attention mechanism is used to aggregate these local features to generate accurate global features of each edge. Extensive experiments on four datasets and three network analysis tasks show the advantages of our model.
Semantic-Aware Path Planning with Hexagonal Grids and Vehicle Dynamic Constraints

The article presents a navigation system that utilizes a semantic map created on a hexagonal grid. The system plans the path by incorporating semantic and metric information while considering the vehicle’s dynamic constraints. The article concludes by discussing a low-level control algorithm used in the system. This solution’s advantages include using a semantic map on a hexagonal grid, which enables more efficient and accurate navigation. Creating a map of allowable speeds based on the semantic map provides an additional layer of information that can help optimize the vehicle’s trajectory. Incorporating both semantic and metric information in the path-planning process leads to a more precise and tailored navigation solution that accounts for the vehicle’s capabilities and the environment it is operating in. Finally, the low-level control algorithm ensures that the vehicle follows the planned trajectory while considering real-time sensor data and other factors affecting its movement. Through this article, we aim to provide insights into the cutting-edge advancements in path planning techniques and shed light on the potential of combining hexagonal grids, vehicle dynamics constraints, and semantic awareness. These innovations have the potential to revolutionize autonomous navigation systems, enabling vehicles to navigate complex environments with greater efficiency, safety, and adaptability.
The Impact of Group Control on the Effectiveness of Enterprise Innovation: An Empirical Study

Innovation has been elevated beyond the traditional forces of production, by the emergence of a new wave of industrial upgrading and the technological revolution, to become a significant force in the advancement of human society. Can an enterprise group, a significant type of industrial organization, improve the effectiveness of enterprise innovation? Here, a quantitative analysis approach was used to systematically analyze the impact of group control on enterprise innovation effectiveness and its transmission path based on the logical framework of the “policy environment-influence effect-influence path”. The study found that group control significantly improves the effectiveness of enterprise innovation compared to independently listed enterprises. The impact path showed that group control can reduce financing constraints through internal capital markets. It increases the investment in innovation and thus enhances the effectiveness of enterprise innovation. Meanwhile, internal information exchange is accelerated through the internal knowledge market, improving enterprise innovation’s effectiveness. The results of this study were still valid after robustness tests, such as propensity score matching and accounting for lag effects. According to the paper’s findings, to enhance financial support for innovation, financial market reform should be intensified. The growth of manufacturing enterprise groups should also be encouraged. Additionally, the ability of businesses to innovate while improving the internal benefits of enterprise groups and their innovation paths should be strengthened.
Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion

This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure.
Activities and a Semantic Network Analysis Based on Core Competencies and their Contents

In this study, the pattern of core competency-based activities, as well as the contents of the project in core competency in educational institutions (including universities) were reviewed. Data were derived from the search results of the service contract list in the Korea ON-line e-Procurement System (KONEPS) for the past 10 years (2012.9 - 2022.8). Research questions were as follows: (1) What is the pattern of activities based on core competencies in educational institutions including universities? (2) How were the contents and scope of the project in the activities of university core competencies presented in terms of semantic network analysis? A total of 240 contracts, excluding overlapping ones, are worth about 6.076 billion KRW, and since 2018, the number of contracts and the contract amount have increased in earnest compared to the previous period. According to the framework of analysis, first of all, in terms of area, education training accounted for 58.3% with 140 cases, followed by diagnosis (tool development and diagnosis implementation) with 49 cases (20.4%), and the two areas occupied for nearly 80% of the total. In terms of demand for core competencies, higher education accounted for the highest number of contracts (37) in the area of diagnosis (tool development and diagnosis implementation). Next, In terms of the frequency of individual words in the co-occurrence network, development, extra-curricular programs, diagnosis, diagnosis tool, university, analysis, and system were the top keywords. In terms of the frequency of words appearing simultaneously with the word “core competency”, development, diagnosis, university, diagnosis tool, ideal talent, result, analysis, extra-curricular programs, management, and competency based appeared in the above order. At the time of the study, 8 communities were identified along with degree centrality. On the other hand, reliability-validity, education-core competency based, program-linkage, subject-content, and subcompetence-behavioral indicators ranked high in the phi coefficient in terms of the correlation network between the words, diagnosis, system, curriculum, derivation. Ideal talent ranked high in the phi coefficient as words that form pairs with a fixed term core competency. 24 communities were identified along with degree centrality. In light of the research results, discussions centered on the strengthening of alignment with university strategy, along with the consideration of competencies based on the future rather than the present. Lastly, directions for future research were suggested based on the limitations of the current study.
A Study on Error Analysis by Re-entry Overseas Korean Learners

This study aims to identify the interlanguage used by overseas Koreans who re-entered Korea for the purpose of studying Korean and entering domestic college through the methodology of error analysis. In particular, this study intends to systematically analyze the Korean language errors of overseas Koreans according to proficiency variables (beginner, intermediate, and advanced) and discourse medium variables (written and spoken language). To this end, this study made a well-balanced corpus of overseas Korean learners with 13,238 words. The results of this study are as follows: First, as a result of the distribution of relative error frequency, the frequency of errors increases as the proficiency increases, and the frequency of errors in written discourse are about five times higher than in spoken discours. Second, as a result of analyzing the frequency of errors by error location, the errors of general nouns shows the highest frequency even in advanced levels without decreasing. Also, the subjective marker was not a top 5 error in the written discourse, although it was a top 2 error in the spoken discourse. Third, as a result of analyzing the frequency of errors by error type, replacement errors were the most common, which was followed by misform, Addition, and Omission errors. At the intermediate level, the replacement error decreased, while the misform error increased. While the misform errors are rather reduced in spoken discourse, addition and omission errors appear more frequently than in written discourse.
A Study on the Educational Needs and Perception of the Liberal Arts Core Curriculum

The purpose of this study is to explore the direction of reorganization of S University's liberal arts core curriculum by analyzing the educational needs assessment and perceptions of members inside and outside S University to foster “H+ future talents” with future capabilities to lead a diversifying society. To this end, a total of 234 internal and external members (teachers, students, graduates, and industries) of S University were surveyed on the educational needs assessment and perceptions of S University's liberal arts core curriculum. After combining the collected data with t-verification, Borich education needs, the Locus for Focus model, and frequency analysis, the current liberal arts core curriculum was reorganized in the order of ｢Computing Thinking and Coding｣, ｢Critical Reading and Writing｣, ｢EnglishⅠ(Listening &amp; Speaking)｣, and ｢English II (Reading &amp; Writing)｣. In addition, through a survey on the perception of the liberal arts core curriculum, the need for class options for Christian liberal arts subjects and classes by English level were confirmed. This study is meaningful in that it can be used as useful basic data for the reorganization of the liberal arts core curriculum of S University and can be a basis for the return of the direction of liberal arts education and the development of liberal arts subjects in the future.
A Study on the Development of Convergence Online Liberal Arts Education in the Post-COVID Era

This study aims to examine the importance of convergence education in addressing global issues like the COVID-19 pandemic. The study also explores the features of online lectures and analyzes a case study of an online liberal arts lecture titled “Transformative Civilization in the Post COVID-19 Era” produced by K Cyber- University in 2020.The study analyzes the pre-production process and the results of a pre-survey to identify the characteristics of the lecture. The study categorizes the OX quiz, which is a measure of learner participation, and analyzes the lecture composition and content. Lastly, the students' changed perceptions and attitudes toward knowledge were investigated through post-mortem surveys.The study finds that “Transformative Civilization in the Post COVID-19 Era” has the following characteristics as a convergence course: student participation, conversational lectures, visualized summary, and interdisciplinary reflection on social phenomena. The lecture was designed to maximize interaction between the instructor and the learners by identifying and reflecting on the students' needs.Overall, the study presents a direction for online lectures during a period of transition in the history of civilization and is a significant case study of convergence in online education. Online education has become a crucial paradigm due to the COVID-19 pandemic, and this study offers valuable insights into creating effective online courses that engage learners and addresses global issues through convergence education.