Reviewerly/exHarmony huggingface dataset:
The Role and Tasks of Trade Insurance for Revitalizing Exports in New Growth Industries

Purpose - This study evaluated the role of trade insurance as a plan for revitalizing exports in new growth industries in the era of the 4th industrial revolution, and suggested ways to utilize trade insurance at a higher level. The role of trade insurance for the revitalization of export items related to new growth industries and the growth of new growth industries is very important. Design/Methodology/Approach - This study was conducted based on a literature review based on previous studies and secondary data. Findings - In order to revitalize exports of items related to new growth industries, the following are suggested improvements and effective policy measures for the trade insurance system. First, the Korea Trade Insurance Corporation should establish a comprehensive support system for the export support of new growth industries through multilateral or bilateral cooperation with export-related organizations. Second, it is necessary to operate special programs, such as premium discounts and preferential policies. Third, it is necessary to further develop exclusive insurance items for new growth industries. Fourth, it is necessary to make insurance products exclusively for digital platforms so that they can be used without time and space constraints. Fifth, active promotion is needed so that companies related to new growth industries can actively utilize the trade insurance system when exporting. Research Implications - This study is expected to provide important policy implications in terms of export support and the export revitalization of new growth industries for the trade insurance system, an exceptional export support system that can be legally operated under the WTO system.
Evaluasi Pelaksanaan Webinar Pusdiklat Perdagangan Dengan Menggunakan Text Mining: Analisis Saran Peserta Webinar

The Trade Education and Training Center (Pusdiklat Perdagangan) participates in the development of competencies and skills of the Indonesian people. One of contribution is organizing webinars through the learning management system (LMS) Kudagang. As an education and training center that organizes infographic webinars, Pusdiklat Perdagangan hopes to provide the best service. Therefore, evaluation is a mandatory thing that must be done. The infographic became a webinar topic that was given to participants, both academics and professionals. To provide optimal service, Pusdiklat Perdagangan evaluates webinar activities through participant suggestions. The purpose of this study is to identify things that need to be addressed and improved in the implementation of future webinars so that the quality of services provided is optimal and identify topics and webinar materials that are really needed by the community. Suggestions from participants in the form of text are processed and analyzed using text mining. The data analysis consisted of two parts: (1) analysis of suggestions regarding the webinar activities; (2) suggestions for the next webinar topic. The results of the analysis of participant suggestions indicate several things that need to be improved in the implementation of the webinar, including the audio quality, the practice duration, the technique in delivering material, and the role of the host in guiding the webinar activities. The webinar topic suggestions given by the participants to Pusdiklat Perdagangan are advanced infographics, social media management, public speaking, effective communication, and how to write scientific articles
Planar k-Uniform States: a Generalization of Planar Maximally Entangled States

Recently, Doroudiani and Karimipour [Phys. Rev. A \textbf{102} 012427(2020)] proposed the notation of planar maximally entangled (PME) states which are a wider class of multipartite entangled states than absolutely maximally entangled (AME) states. There they presented their constructions in the multipartite systems but the number of particles is restricted to be even. Here we first solve the remaining cases, i.e., constructions of planar maximally entangled states on systems with odd number of particles. In addition, we generalized the PME to the planar $k$-uniform states whose reductions to any adjacent $k$ parties along a circle of $N$ parties are maximally mixed. We presented a method to construct sets of planar $k$-uniform states which have minimal support.
Detection of Deepfake Videos Using Long Distance Attention

With the rapid progress of deepfake techniques in recent years, facial video forgery can generate highly deceptive video contents and bring severe security threats. And detection of such forgery videos is much more urgent and challenging. Most existing detection methods treat the problem as a vanilla binary classification problem. In this paper, the problem is treated as a special fine-grained classification problem since the differences between fake and real faces are very subtle. It is observed that most existing face forgery methods left some common artifacts in the spatial domain and time domain, including generative defects in the spatial domain and inter-frame inconsistencies in the time domain. And a spatial-temporal model is proposed which has two components for capturing spatial and temporal forgery traces in global perspective respectively. The two components are designed using a novel long distance attention mechanism. The one component of the spatial domain is used to capture artifacts in a single frame, and the other component of the time domain is used to capture artifacts in consecutive frames. They generate attention maps in the form of patches. The attention method has a broader vision which contributes to better assembling global information and extracting local statistic information. Finally, the attention maps are used to guide the network to focus on pivotal parts of the face, just like other fine-grained classification methods. The experimental results on different public datasets demonstrate that the proposed method achieves the state-of-the-art performance, and the proposed long distance attention method can effectively capture pivotal parts for face forgery.
Analytic Insights into Structure and Rank of Neural Network Hessian Maps

The Hessian of a neural network captures parameter interactions through second-order derivatives of the loss. It is a fundamental object of study, closely tied to various problems in deep learning, including model design, optimization, and generalization. Most prior work has been empirical, typically focusing on low-rank approximations and heuristics that are blind to the network structure. In contrast, we develop theoretical tools to analyze the range of the Hessian map, providing us with a precise understanding of its rank deficiency as well as the structural reasons behind it. This yields exact formulas and tight upper bounds for the Hessian rank of deep linear networks, allowing for an elegant interpretation in terms of rank deficiency. Moreover, we demonstrate that our bounds remain faithful as an estimate of the numerical Hessian rank, for a larger class of models such as rectified and hyperbolic tangent networks. Further, we also investigate the implications of model architecture (e.g.~width, depth, bias) on the rank deficiency. Overall, our work provides novel insights into the source and extent of redundancy in overparameterized networks.
Developing Sustainable Solutions for Waste Management in Smart Cities Using Blockchain

The objectives of smart cities primarily focus on Enhancing the standard of living of citizens through upgrading and seamless integration of infrastructure with analytics and sensor networks Increasing the economic competitiveness for talent acquisition and industrial investments Developing a sustainable and eco-friendly environment. Various domains such as e-governance, education, health care, water management, waste management, energy management, urban mobility, incubation/trade facilitation centers and skill development centers are included in the smart city mission. Waste management includes the activities and actions required to manage waste from its inception to its final disposal. This includes the collection, transport, treatment and disposal of waste, together with monitoring and regulation of the waste management process. Many of the government agencies involved in waste management maintain and administer large volumes of data. Lack of transparency and lack of automation are the major challenges in a waste management system. The prime focus on development of sustainable solutions for smart cities has opened up the venues for integrating modern technologies like artificial intelligence, machine learning, blockchain technologies and much more. This chapter explores the various approaches to integration of blockchain technology in developing sustainable solutions for waste management. The objectives of the smart cities mission and its focus domains are detailed in Section 1. Section 2 highlights the challenges in present waste management systems. The working principle of blockchain technology and its characteristics are presented in Section 3. The advantages of using blockchain technology for waste management are presented in Section 4. A few case studies on application of blockchain, mainly in solid waste management and industrial waste management, are reported in Section 5. Potential problem statements and research directions are presented in Section 6. The chapter concludes in Section 7 with major remarks and inferences of the study.
Ship gauges potential for catastrophic earthquake

Fault structures could reveal whether next rupture in Pacific Northwest will be a repeat of megaquake in 1700.
“THE MISSION” STRATEGY APPLICATION TO ELEVATE STUDENTS’ SPEAKING PERFORMANCE

The English Department students of Muhammadiyah University of Parepare faced some difficulties in performing speaking. For instance, the students feel anxious and worried about making mistakes, and they lack language and communicative knowledge. Therefore, to help students overcome their problems and enhance their communicative competence, this research was conducted to determine whether applying “The Mission” strategy could elevate English Education Department students' speaking performance. The researcher in this study conducted a pre-experimental method. The population was the first semester of English Department Students. The researcher used the total sampling method in deciding the research sample. The instruments used in collecting the data were informal interview tests, also known as “Question and Answer”. The result of the data found was analysed by using SPSS Application. The results of the research showed that the students’ speaking performance after giving treatment was significantly improved. It was proved by the data of the students’ mean score post-test was higher than in pre-test (77,00 37,77). It indicates that the elaboration of Task-based Instruction with “The Mission” strategy can empower students’ in performing speaking.
Disrupting the Banking System with Blockchain Technology: The Case of Bitcoin

Bitcoin cryptocurrency appears to be a vital disruptive innovation that will transform the financial system. But the concern is the possibility of its wider acceptance and sustainability in the market. Due to several misconceptions about bitcoin and other cryptocurrencies, it is unclear to most people and businesses if they are secured and legal to use. This raises the question of if bitcoin can stand the test of time to come. Interestingly, a critical review of related articles, papers and journals have given staunch indications that bitcoin and its sister cryptocurrencies are the future. This is evidenced by the increasing number of accounts and transactions carried out by individuals and businesses today. However, there is a need to conduct more extensive academic research about bitcoin for accurate predictions regarding its future global adoption.
Lexical Formation Error In The Descriptive Writing Of Indonesian Tertiary EFL Learners

This study was aimed at identifying the kinds and frequency of formal errors on Indonesian English as a Foreign Language (EFL) learnersâ€™ descripting writing. Therefore, descriptive analysis research design was implemented to achieve the objective. The data were collected from the descriptive writing of university students in Indonesia (N = 40). Then, the data were computerized and tabulated by using descriptive statistic (frequency and percentage) in SPSS version 21. The result of this study presents that from 223 errors, suffix was the most frequent formal errors (32.29%) in Formal Misselection, followed by calque (22.87%) in Formal Misformations and omission (12.56%) in Distorsion. In conclusion, most of the students have considerable difficulty in forming the correct form of the words. Therefore, to produce an excellent descriptive writing, the students are suggested to learn and practice more on words formations (grammar).
A study on Privacy Protection and Data Security in Cloud Computing

Cloud computing is the used to store the huge amount of data in the cloud for storing the various types of data such as –text ,video audio etc. And cloud computing is used the various algorithms to secured a data for the cloud.Cloud computing is the most useful computing model of previous years it is a sure a safety provided services by cloud or user for his security, privacy, protecting the data.Cloud computing is the provide service to consumers and businesses to use without any type of application can’t installing any app to use cloud service access to the internet. Cloud is the various types of advantages included the creation and storage of data at control the cloud server. User can access of our personal files data in any type of computer with the help of connect via internet. In current situation cloud computing can access the business application with the help of internet then there are create a several types of challenges then various benefits to the cloudIn current situation cloud computing is the one of the most popular software in IT industry and any other, then occurs a any type of security issues this paper gives solutions for this security issues.
Sistem Pencatatan Kehadiran Deteksi Wajah Menggunakan Metode Haar Feature Cascade Classifier

Biometric innovation is one source that can be used in security frameworks such as facial recognition as personal information. The human face has a lot of data and has the most normal and widely used attributes for the presentation of human character. As well as communicating feelings and considerations, faces can also be used to identify individuals. One of the innovations in the recognition statement applied to biometrics is the use of the human face as a recording system in the field of education. However, there are several circumstances related to the recording of student absenteeism currently occurring in the field of education, namely " Titip Absent " . Therefore, in this study proposed a system that uses a student attendance recording system using computer technology to reduce the level of cheating when filling in attendance forms and the effectiveness of student data processing, the Haar Cascade classification method is used to record the student attendance process using a biometric system. The algorithm applied in the Haar Cascade classifier method uses a face detector called the "Cascade Classifier". The result of this study is an attendance application that can detect whether all users who have recorded attendance have been registered in the system with the distance between the face and the camera is 50 cm with an accuracy rate of 70%.
DESIGN OF THE ARCHITECTURE OF AN AUTOMATED BIBLIOGRAPHIC SYSTEM

The development of information and computer technologies has led to the need to evolve the concept of universal bibliographic control. The creation of the Internet and web technologies has allowed this concept to reach a new level by creating a number of common international standards. In addition, to ensure control and exchange of bibliographic information public bibliographic and scientometric databases were created. Today, software for managing bibliographic records is in demand in various countries in Europe and America. The most popular software in these countries is EndNote, RefWorks, BibTeX and Zotero. The development of such automated bibliographic system and the adaptation of its functionality to standards and requirements within Ukraine is relevant for a number of reasons. The main reasons are the need for every scientist and lecturer of higher education institutions to confirm their scientific achievements when hiring, submitting scientific work to various competitions, to obtain a scientific degree and so on. Today the rapid development of information and computer technology allows us to abandon the list of scientific papers in manual mode and move to the use of specialized software on smartphones. That is why the architecture of an automated bibliographic system, which is developed as a mobile application based on the Android operating system, was designed. Java is chosen as the programming language in which the software will be written, as the vast majority of the Android operating system is written in this language. A number of technologies were chosen for the selected operating system. They will simplify the process of developing a mobile application. The three-layer architecture of the automated bibliographic system is designed on the basis of the multilevel model of architecture and the MVVM template. This architecture allows to provide the main non-functional characteristics of the quality of the developed software, as well as to effectively implement the rules of business logic within the object-oriented programming paradigm.
Iterative Utterance Segmentation for Neural Semantic Parsing

Neural semantic parsers usually fail to parse long and complex utterances into correct meaning representations, due to the lack of exploiting the principle of compositionality. To address this issue, we present a novel framework for boosting neural semantic parsers via iterative utterance segmentation. Given an input utterance, our framework iterates between two neural modules: a segmenter for segmenting a span from the utterance, and a parser for mapping the span into a partial meaning representation. Then, these intermediate parsing results are composed into the final meaning representation. One key advantage is that this framework does not require any handcraft templates or additional labeled data for utterance segmentation: we achieve this through proposing a novel training method, in which the parser provides pseudo supervision for the segmenter. Experiments on Geo, ComplexWebQuestions, and Formulas show that our framework can consistently improve performances of neural semantic parsers in different domains. On data splits that require compositional generalization, our framework brings significant accuracy gains: Geo 63.1 to 81.2, Formulas 59.7 to 72.7, ComplexWebQuestions 27.1 to 56.3.
Temporal evolution of relative seismic velocity in the Golf of Corinth - Greece.

&amp;lt;p&amp;gt;Our aim is to monitor the temporal evolution of the crust in Greece, with a particular focus on the Gulf of Corinth. &amp;amp;#160;Indeed, Greece is one of the most exposed country to earthquakes in Europe. The Gulf of Corinth, &amp;amp;#160;is known for its fast extension rate of about 15 mm/yr in the western part and 10mm/yr in the eastern part. This fast extension is associated with recurrent seismic swarms and by a few destructive earthquakes. This seismicity is likely the result of a combination of multiple driving processes including fluid migration at depth.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;In the present work, we use seismic noise recorded from 2010 to 2020 by all seismic stations deployed in Greece, and in particular by the dense Corinth Rift Laboratory network, to compute the seismic velocity variation (dv/v) in several subregions. By comparing the result obtained at different periods, we are able to distinguish the temporal evolution of the upper, mid and lower crust. This temporal evolution is compared to the seismicity of the Gulf of Corinth.&amp;lt;/p&amp;gt;
Integrating TEITOK and KonText/PMLTQ at LINDAT

In this paper we describe how the TEITOK corpus platform was integrated with the KonText and PML-TQ corpus platforms at LINDAT to provide document visualization for both existing and future resources at LINDAT.TEITOK is an online platform for searching, viewing, and editing corpora, where corpus files are stored as annotated TEI/XML files.The TEITOK integration also means LINDAT resources will become available in TEI/XML format, and searchable in CWB on top of existing tools at the institute.Although the integration described in this paper is specific for LINDAT, the method should be applicable to the integration of TEITOK or similar tools into an existing corpus architecture.
Johnson-Lindenstrauss Transforms with Best Confidence

The seminal result of Johnson and Lindenstrauss on random embeddings has been intensively studied in applied and theoretical computer science. Despite that vast body of literature, we still lack of complete understanding of statistical properties of random projections; a particularly intriguing question is: why are the theoretical bounds that far behind the empirically observed performance? Motivated by this question, this work develops Johnson-Lindenstrauss distributions with optimal, data-oblivious, statistical confidence bounds. These bounds are numerically best possible, for any given data dimension, embedding dimension, and distortion tolerance. They improve upon prior works in terms of statistical accuracy, as well as exactly determine the no-go regimes for data-oblivious approaches. Furthermore, the projection matrices are efficiently samplable. The construction relies on orthogonal matrices, and the proof uses certain elegant properties of the unit sphere. In particular, the following techniques introduced in this work are of independent interest: a) a compact expression for the projection distortion in terms of singular eigenvalues of the projection matrix, b) a parametrization linking the unit sphere and the Dirichlet distribution and c) anti-concentration bounds for the Dirichlet distribution. Besides the technical contribution, the paper presents applications and numerical evaluation along with working implementation in Python (shared as a GitHub repository).
AdaXpert: Adapting Neural Architecture for Growing Data

In real-world applications, data often come in a growing manner, where the data volume and the number of classes may increase dynamically. This will bring a critical challenge for learning: given the increasing data volume or the number of classes, one has to instantaneously adjust the neural model capacity to obtain promising performance. Existing methods either ignore the growing nature of data or seek to independently search an optimal architecture for a given dataset, and thus are incapable of promptly adjusting the architectures for the changed data. To address this, we present a neural architecture adaptation method, namely Adaptation eXpert (AdaXpert), to efficiently adjust previous architectures on the growing data. Specifically, we introduce an architecture adjuster to generate a suitable architecture for each data snapshot, based on the previous architecture and the different extent between current and previous data distributions. Furthermore, we propose an adaptation condition to determine the necessity of adjustment, thereby avoiding unnecessary and time-consuming adjustments. Extensive experiments on two growth scenarios (increasing data volume and number of classes) demonstrate the effectiveness of the proposed method.