Reviewerly/exHarmony huggingface dataset:
Social Media in Support of Indonesia's One Data Interoperability Process for Implementing Data Governance Policies

This study aims to determine whether Twitter is a social media platform that supports the One Data Indonesia Interoperability process to implement data regulations by producing accurate, integrated and accountable data. Use of Social media in Participation, Transparency, Discussion, involvement, and Communication Strategy. This descriptive research uses NVivo 12 Plus with data sources from Twitter @diklat BIG, @datagoid, @bappenasRI, and @Jokowi via NCapture. NCapture data and use the automatically configured crosstab feature. This study shows that social media significantly drives data interoperability strategies in Indonesia. Communication indicators from Participation in previous attempts were 22.00% and 3236 referrals. The 21.00% transparency category includes clear objectives, processes and program support. They completed 20.00% of the various stages of data collection, verification, and data priority release. To socialize the implementation of One Data Indonesia and the action to accelerate the provision of the One Data Portal, tweets were used with an involvement proportion of 18.00%. Communication Strategy realizes the importance of data and the existence of One Data Indonesia by using the Conversation Indicator, which has a Participation proportion of 17.00%. This study is essential for analyzing social media assistance using Twitter-based activity support and providing new, up-to-date data with NVivo 12 Plus analysis.
Bibliometric Analysis: The Use of Blockchain Technology in Smart City Mapping Concept

Research on the adoption of blockchain technology in supporting the application of the smart city idea is extensively carried out to keep up with the development and difficulty of more intricate city management. This study aims to review research trends in the use of blockchain technology in the concept of Smart City mapping through research published on the Scopus database in 2016 - 2022. Three hundred forty-two (342) documents taken from Scopus with the keywords “smart city” and “blockchain technology” for 2016 to 2022 were analyzed using a bibliometric approach. Articles were analyzed based on the number of publications per year, country, author, affiliation, source, and funding. CiteSpace analyzed connectivity trends between keywords, authors, and countries. The results showed that Sustainable Smart Cities, Energy Trading, and On-demand Protocol dominated ten (10) clusters. Broadly speaking, it also discusses sustainable smart cities and energy trading using blockchain technology. The most dominant authors in this study trend are Jabalbarezisarbijan. Meanwhile, analysis of the Scopus database revealed three dominating countries, namely India (72), China (63), and the United States (47). At the same time, the institution, according to the most dominating institutional publication trend, is Brandon University. Then, blockchain technology substantially impacts the advancement of infrastructure within the domains of smart cities, intelligent transportation systems, electric vehicles, and renewable energy. This current study proposed the trajectory of advancement for the use of blockchain technology in smart city governance, thereby illuminating the future of the smart city concept.
The Use of Ome-TV Application as a technological medium to Practice English Speaking Skills

In this modern moment, a plethora of applications are available to students seeking to enhance their English-speaking proficiency. One such platform is Ome-TV, which facilitates students' engagement with individuals from various regions around the English-speaking world. The objectives of this study were to investigate the benefits of utilizing the Ome-TV program as a tool for students to enhance their English-speaking abilities. Additionally, the study attempted to uncover any limitations associated with the use of the Ome-TV application for students' English-speaking practice. The present study employed a qualitative approach utilizing a descriptive qualitative design, with a sample size of six English Language Education Department (ELED) students who served as participants. The researchers employed interviews as a method for data collection. The findings revealed that the benefits of utilizing the Ome-TV application for English Language Education Department (ELED) students' English oral proficiency include becoming effective media for practicing English speaking skills, providing a supportive and entertaining environment for language exposure, meeting mutually respectful fellows to speak English with, providing interactive pronunciation drills in real-time, enhancing lexical proficiency, and receiving a substantial amount of input on grammatical knowledge. Meanwhile, the limitations of utilizing the Ome-TV application for English Language Education Department (ELED) students' English oral proficiency consist of inappropriate use of English as a formal language, substantial use of unfavorable language, and utilizing diverse ethnic accents.
THE INFLUENCE OF THE USE OF SIPOLAN ICONS AS SOCIAL MEDIA CONTENT IN INFORMATION DISSEMINATION PROGRAMS (CASE STUDY AT THE CLASS I IMMIGRATION OFFICE OF TPI POLONIA)

This SIPOLAN was created with the aim of providing an immigration image with a Mido midi design wrapped in the Batak Toba custom with ulos and sortal which depicts the location of the Polonia immigration office in North Sumatra. The pattern used in graphic designs and graphic videos are shows in every post of the Polonia Immigration Office on various platforms. This study used associative quantitative causal relationship to measure how far SIPOLAN's social media content influences the effectiveness of immigration information dissemination by Polonia Immigration Office information section is published on the platform used by the office. This research can be used as input for the TIKIM Subdivision (Immigration Information and Communication Technology). At the Polonia Immigration Office, so that content on social media published by the Polonia Immigration Office has more effective standardization in delivery and delivery strategies to the public, the use of SIPOLAN Social Media Content has an effect of 87.7% on the ease of dissemination information and residue due to the influence of other factors 12.3% which were not applied in this study in the form of interactions from outside social media or face-to-face employees with each other, with superiors or with the public.,
IMPLEMENTATION OF ISO 9126 IN THE QUALITY TEST OF THE M-PASSPORT APPLICATION AT THE CLASS I NON-TPI TANGERANG IMMIGRATION OFFICE

In order to improve services to the community, the Directorate General of Immigration has utilized information technology in the form of a service information system for the community. The purpose of this study is to measure the quality of the information system for immigration services at the Tangerang Immigration Office according to ISO 9126. Collection method the data used is simple observation, interviews, and quantitative in the form of filling out questionnaires. The validity and reliability test uses SPSS 26. The results of this study indicate that the measurement of the quality of patient service information systems carried out with the ISO 9126 standard is obtained as follows: Functionality variable is 95%, Reliability variable is 90.7%, Usability variable is 90%, Variable the efficiency is 93%, the maintainability variable is 91% and the portability variable is 95.7%. And through the percentage of due diligence, it can be concluded that the use of the M-Passport application in immigration services for passport applicants at the Immigration Office Class I Non TPI Tangerang has fulfilled the ISO 9126 standard with the percentage obtained an average actual score of 91.99% or an average criterion "Very good".
ANALYSIS OF CLASS I NON TPI BEKASI IMMIGRATION OFFICE WEBSITE USING WEBQUAL 4.0 AND IPA (IMPORTANCE PERFORMANCE ANALYSIS) METHODS

The Covid-19 pandemic has forced the public to follow the sophistication of technological developments, including activities in government public services.The website is one of the media that utilizes the internet and is used in disseminating information to the public. This study aims to determine the suitability of the Bekasi Immigration Office website with the standardization of the Ministry of Law and Human Rights' website and to obtain a website service quality value using the webqual 4.0 and IPA (Importance Performance Analysis) methods. The method used in this study is to do a qualitative and quantitative approach. The standardization of Kemenkumham pages consists of domain naming, display standards, accessibility, information interoperability, social media accounts and search engine optimization, in conjunction with webqual 4.0 method, there are three research variables, namely usability, information quality, and interaction quality. There are 100 respondents in the study. Results of the research, there are six categories of suitability for standardization that have not been fulfilled on the Kanim Bekasi website. Meanwhile website correlation index is good and the results of the IPA Cartesian diagram still have top priority indicators whose performance has not been maximized and it is necessary to improve each category and indicator which is deemed not sufficient to meet the expectations or interests of users.
Application Of Web-Based Archiving Information System (E-Sip) at BPK RI Yogyakarta

Nowadays, almost every aspect of daily life cannot be separated from digital technology. Modern globalization has accelerated the development of information technology and information systems. Because the user has a specific need, organizations like BPK RI DIY are still resistant to manual document retrieval because they are inefficient and ineffective. This is because the user has a specific need. As such, the goal of developing the web-based application of Aplikasi Sistem Informasi Pengarsipan (E-SIP) is to facilitate archiving issuance. In this application, which was created utilizing the waterfall model, the front-end and back-end frameworks were ReactJS for the front end and NetstJS for the back end, both of which are based on NodeJS for databases using MYSQL. This technique uses the Black box for the GUI of the inactive archive application. Based on test results showing that the archiving application is simple to use and understand, it can be assumed that the application can help organizations like BPK RI DIY in archiving management.
DESIGN AND BUILD AN IMMIGRATION STAY PERMIT E-ALERT APPLICATION AT THE ANDROID-BASED NON-TPI SERANG CLASS I IMMIGRATION OFFICE

The revolution of industry 4.0 encourage people to travel to another countries with different paths. Foreigners who entered to Indonesia must have a valid residence permit. The granting of a residence permit is given by the Directorate General of Immigration through the Immigration Office and sent via email so that many foreigners not careful about the validity period of their residence permit. To provide excellent immigration services and supervision, the Directorate General ofImmigration always makes innovations that can make every job easier. In addition, by designing an Android-based immigration residence permit e-alert application, it can prevent foreigners from committing immigration violations in the form of overstay. In this study the method used is the waterfall system development method where all activities are carried out sequentially to obtain optimal results. The system development application used is Android Studio and Firebase. The method is divided into 5 (five) parts, requirements analysis, design, implementation, testing, and maintenance. The results of this study are in the form of a prototype that can be run using an Android-based emulator and can be used as an innovation reference at First Class of Serang Immigration Office.
CREATION OF A WEBSITE-BASED CHATBOT APPLICATION AT THE CLASS I IMMIGRATION OFFICE OF TPI EAST JAKARTA

The customer service of the immigration office class i tpi east Jakarta is aimed to improve the service to the applicant or applicants.The applicant or applicants have to take the time to consult with customer service .In this case, the 1st class Immigration Office East Jakarta still relies on human resources as an online customer service to response questions from applicants. Given the increasing amount of applicants, it is not very effective nor efficient to answer questions one by one given that not many applicants have much time in their day to receive informations they need in such a long time, want things quickly in a very short time. This study is an attempt to develop a web based chatbot information systems application that can be used by the applicants of the 1st class Immigration Office East Jakarta so that they can obtain informations as fast as the speed of light. The Methods used for this study is the method of extreme programming. Application programming language, it uses an MySQL database and the PHP. The result of the building of the chatbot application is a result of testing of testing was definitely using black box, it can be concluded that this application can run well and answer every question quickly in accordance expected
ONLINE QUEUING SYSTEM BAP PASSPORT INDONESIAN CITIZEN AT CLASS I NON-TPI IMMIGRATION OFFICE BEKASI

The conventional method used for queuing services for Indonesian passport BAP is no longer relevant to these circumstances and the covid-19 pandemic and has not been able to answer future immigration challenges. The current queuing system is that applicants must come to the immigration office for scheduling processing and then be recorded by manual book officer queue. The Online Queue System for Indonesian Passport BAP can be a solution to help current problems and answer future challenges. The online queuing system that will be implemented can be processed through a queuing system link that can be posted on the social media of the Bekasi immigration office. The online queuing system cycle method used by the author in this online queuing system is a waterfall method where each stage must be completed before entering the next phase. Starting from analyzing system requirements, system design, system implementation, system testing, and system implementation and maintenance. The result of the process is that the online queuing system for Indonesian passport BAP has gone through each stage well so that the online queuing system is feasible to implement. The online queuing system is expected to help the intelligence section and prosecution of the Bekasi immigration office provide convenience for applicants and is very effective with the ongoing COVID-19 pandemic conditions and responds to future service challenges.
Rethinking the Optimal Strategy of Deep Learning for Sar Image Semantic Segmentation

Deep learning framework based on transformer have achieved excellent performance in semantic segmentation tasks. However, due to the different properties of SAR image, it is difficult to ensure the rationality and effectiveness if the optical training pipeline is directly transferred to the SAR semantic segmentation task. In this paper, the optimal strategies get rethink when it comes to SAR image semantic segmentation. First, for the imaging process of SAR images, unreasonable data augmentation methods are removed. Second, the implementation and feasibility verification of other optimization strategies, such as Online Hard Example Mining (OHEM), Mosaic and self-training process, are carried out. Finally, comparison experiments demonstrate the effectiveness of the entire system on a large open dataset of built-up areas in SAR Images.
Context-Augmented Key Phrase Extraction from Short Texts for Cyber Threat Intelligence Tasks

In this paper, we address contextual limitations of current deep learning-based and heuristic key phrase extraction tools as applied to the domain of cybersecurity. To address these limitations, we develop a hybrid system that augments state-of-the-art (SOTA) transformers for the task of key phrase sequence labeling, using a novel set of part-of-speech (POS) and role-aware tagging rules to generate fine-grained tag sequences from short text corpora. Next, we fine-tune multiple SOTA deep learning (DL) language model (LM) architectures to these transformed sequences. We then evaluate the architectures by measuring the outcomes from respective LMs to select the best-performing underlying transformers for extracting cybersecurity key phrases. This new ensemble achieves very significant predictive gains over SOTA baselines on general cybersecurity corpora, such as F1 scores at least 25% higher than hybrid SOTA transformers fine-tuned using baseline tagging rules on the generic corpus, with a much less significant tradeoff (of less than 5% in F1) on a vulnerability-specific corpus.
Knowledge Transfer-based Network from Medium and High-Resolution SAR Imagery for Built-Up Extraction with Class-Imbalanced Data

With the rapid urbanization, building detection in synthetic aperture radar (SAR) images has become an increasingly important research topic. However, Built-Up Extraction faces two challenges: class imbalance and inconsistent resolution. In this paper, to address the challenges of class imbalance and varying resolutions, we adopt SegFormer as the main framework and introduce Lovász loss to handle class imbalance while using knowledge distillation to transfer knowledge between different resolutions. Experiments show our method can improve built-up extraction accuracy. Our model achieves 0.8424 IoU on the competition test set.
Error Detection on Knowledge Graphs with Triple Embedding

Knowledge graphs (KGs), as essential ingredients in many real-world applications, always contain a considerable number of errors. KG error detection aims to find the triples whose head entity, tail entity, and corresponding relation are mismatched. Despite being urgently needed, existing KG error detection methods lack generalizability. They mainly utilize supervised information such as entity type or erroneous labels, but such information is not often available in the real world. The challenges in detecting errors in KGs are twofold. Firstly, KGs exhibit unique data characteristics that distinguish them from general graphs. Secondly, real-world KGs tend to be large, and labels are often scarce. To bridge the gap, we propose a novel KG error detection framework based on triple embedding, termed TripleNet. TripleNet constructs a triple network by treating each triple as a node and connecting them via shared entities. It then employs a Bi-LSTM module to capture intra-triple translational information at the local level and uses a graph attention network to gather inter-triple contextual information at the global level. Finally, it computes the suspicious score of each triple by integrating its local and global-level information. Experimental results on two real-world KGs demonstrated that TripleNet outperforms state-of-the-art error detection algorithms with comparable or even better efficiency.
Subspace Outliers Detection by Signal Subspace Matching

We present a novel and computationally efficient solution to the problem of subspace outlier detection that does not assume knowledge of the number of outliers nor exact knowledge of the dimension of the inliers subspace. The solution is based on a powerful representation of the inliers subspace, referred to as soft projection, and on a novel goodness-of-fit metric, referred to as signal subspace matching (SSM). Experimental results, demonstrating the performance of the SSM solution, are included.
Multiclass Minimax Learning for Deep Neural Networks

For classification tasks, deep neural networks seek to minimize the average risk of classification error during the training step. When some classes are more difficult to recognize, the class-conditional classification probabilities, also called the recalls, of the neural network classifier are generally very unequal in many real world applications. This paper proposes a multiclass minimax approach for equalizing the recalls of a deep neural network. Our approach replaces the top layer of the neural network by a specific discrete minimax decision rule. This novel top layer is based on a K-means partitionning of its input deep features to train a minimax Bayes classifier that can fit any input statistical distribution. The learning process, based on a subgradient optimization algorithm, is scalable when the number of classes is large. Numerical experiments compare our approach to several state-of-the-art algorithms on medical images and CIFAR-100 database.
Applications of Artificial Intelligence in Finance: Prospects, Limits and Risks

Advances in artificial intelligence (AI) highlight the potential of this technology to affect the financial sector. This article aims to identify the applications and impact of AI on the main activities of the financial sector, more particularly banking. The prospective literature review highlighted four main application themes for AI: credit risk analysis and assessment, personalization of the customer experience, risk intermediation, instantaneous resolution of combinatorial problems (nowcasting). However, the new methods and sources of data exploited by AI can give rise to numerous legal and ethical biases beyond the control of the bank. These emerging technologies, and the associated risks, therefore call for an adaptation of financial regulation to better adapt banking responsibility.
Inducing Coordination in Multi-Agent Repeated Game through Hierarchical Gifting Policies

Coordination, i.e., multiple autonomous agents in a system to achieve a common goal, is critical for distributed systems since it can increase the overall reward among all agents. However, The dynamic environment and selfish agents pose challenges to learning coordination behavior from historical interaction data in a long-term interaction environment. Previous works mostly focus on one-shot or short-term distributed agent interaction environments, which often leads to selfish or lazy behavior in long-term interaction environments, i.e., prioritizing individual optimal strategies over cooperative strategies. This behavior is mainly due to the lack of historical memory or incomplete use of historical interaction data to guide the current interaction strategy. In this paper, we propose a hierarchical peer-rewarding mechanism, hierarchical gifting, that allows each agent to dynamically assign some of their rewards to other agents based on historical interaction data and guide the agents towards more coordinated behavior while ensuring that agents remain selfish and decentralized. Specifically, we first propose an auxiliary opponent modeling task so that agents can infer opponents’ types through historical interaction trajectories. In addition, we design a hierarchical gifting strategy that dynamically changes during execution based on known opponents’ types. We employ a theoretical framework that captures the benefit of hierarchical gifting in converging to the coordinated behavior by characterizing the equilibria’s basins of attraction in a dynamical system. With hierarchical gifting, we demonstrate increased coordinated behavior of different risk, general-sum coordination games to the prosocial equilibrium both via numerical analysis and experiments.