Reviewerly/exHarmony huggingface dataset:
IndQNER: Named Entity Recognition Benchmark Dataset from the Indonesian Translation of the Quran

Indonesian is classified as underrepresented in the Natural Language Processing (NLP) field, despite being the tenth most spoken language in the world with 198 million speakers. The paucity of datasets is recognized as the main reason for the slow advancements in NLP research for underrepresented languages. Significant attempts were made in 2020 to address this drawback for Indonesian. The Indonesian Natural Language Understanding (IndoNLU) benchmark was introduced alongside IndoBERT pre-trained language model. The second benchmark, Indonesian Language Evaluation Montage (IndoLEM), was presented in the same year. These benchmarks support several tasks, including Named Entity Recognition (NER). However, all NER datasets are in the public domain and do not contain domain-specific datasets. To alleviate this drawback, we introduce IndQNER, a manually annotated NER benchmark dataset in the religious domain that adheres to a meticulously designed annotation guideline. Since Indonesia has the world’s largest Muslim population, we build the dataset from the Indonesian translation of the Quran. The dataset includes 2475 named entities representing 18 different classes. To assess the annotation quality of IndQNER, we perform experiments with BiLSTM and CRF-based NER, as well as IndoBERT fine-tuning. The results reveal that the first model outperforms the second model achieving 0.98 F1 points. This outcome indicates that IndQNER may be an acceptable evaluation metric for Indonesian NER tasks in the aforementioned domain, widening the research’s domain range.
Building Knowledge Graphs in Heliophysics and Astrophysics

We propose a method to build a fully connected knowledge graph in the scientific domains of heliophysics and astrophysics, using word embeddings from BERT which are adaptively fine-tuned to these domains. We extract the scientific concepts automatically by a keyword extractor. The graph nodes representing these concepts are connected and weighed based on the cosine similarities computed from their fine-tuned embeddings. Our method is able to capture various meaningful scientific connections, and it incorporates the possibility to enable knowledge discovery.
Explainable Integration of Knowledge Graphs Using Large Language Models

Linked knowledge graphs build the backbone of many data-driven applications such as search engines, conversational agents and e-commerce solutions. Declarative link discovery frameworks use complex link specifications to express the conditions under which a link between two resources can be deemed to exist. However, understanding such complex link specifications is a challenging task for non-expert users of link discovery frameworks. In this paper, we address this drawback by devising NMV-LS, a language model-based verbalization approach for translating complex link specifications into natural language. NMV-LS relies on the results of rule-based link specification verbalization to apply continuous training on T5, a large language model based on the Transformer architecture. We evaluated NMV-LS on English and German datasets using well-known machine translation metrics such as BLUE, METEOR, ChrF++ and TER. Our results suggest that our approach achieves a verbalization performance close to that of humans and outperforms state of the art approaches. Our source code and datasets are publicly available at https://github.com/dice-group/NMV-LS .
Systemic Functional Grammar

Chapter 1 presents the purpose of the book – i.e. describing how a text-based description of three world languages can be developed. The Systemic Functional Linguistic theory informing these descpriptons is introduced, including modellng of context and discourse semantics, and the basic theoretical parameters of metafunciton, rank and stratification.The nature argumentation in relation to grammar description is outlined.
Poetic Representation of the Gwangju Uprising: the embodiment of liberated motherhood and Mother God: focusing on the Goh Jeong-Hee’s eighth collection of poems Tears-Rain in Gwangju

This paper analyzed the poetic representation of the Gwangju Uprising focusing on Goh Jeong-Hee’s Poetry of May. Goh Jeong-Hee was influenced by Korean women's theology in the 1980s. Korean female theologians criticized the oppression of women under the patriarchal system and raised the Mother God as the source of life. In the late 1980s, the reality faced by women was defined as the reality of killing and insisted on a great transition to life. She stressed that women should be the subject of the unification movement in order to overcome the national contradictions that suppress women. Of course, prior to Christian epistemology, there was a reality of the 1980s in Goh Jeong-Hee’s poetry and Gwangju was at the center of it. However, when the truth of the Gwangju Uprising was not revealed in the late 80s, Goh Jeong-Hee expressed deep despair and skepticism. And using Christian death and resurrection as motifs, the experience of despair was used as an opportunity to move toward a fundamental revolution. Goh Jeong-Hee hinted at the need for a new language by evoking the women’s pain and anger what are not reduced from a male-centered system to a universal language through a ‘Michineon’. And while attempting a revolutionary transformation of thoughts and customs, she embodied death in the uprising as an event that c the birth of life. The Gwangju Uprising was an opportunity for mother to be called the subject of liberation and Mother God to be called the source of life, and was a turning point in transforming Gwangju into a land of livelihood and resurrection. This is because Goh Jeong-Hee found Mother God in women in Gwangju as the source of life. Women in Gwangju, who were considered victims of the uprising and innocent victims, were another main subject of the uprising that supported the community of life by saving the dead, caring for the injured, and feeding the living. Goh Jeong-Hee proved that the practice of living shown by women in Gwangju turned the time stained with violence and death into a 10-day liberation zone. And Goh Jeong-Hee‘s May poem shows a vision of a community of care that we talk about as an alternative society today. Although it has not been fully discussed by Goh Jeong-Hee herself, it is inferred that liberated motherhood uses solidarity and relationship that support each other's lives as its principle. The new world created by female subjects makes us imagine a community of autonomous and mutually beneficial care based on liberated motherhood.
Building and Annotating a Corpus

This chapter describes the process of creating and annotating a corpus. This process involves, for instance, collecting data (speech and writing), transcribing recorded speech, and adding annotation, markup indicating in a conversation, for instance, when one person’s speech overlaps another speaker. While written texts are relatively easy to collect – most writing is readily available in digital formats – speech, especially spontaneous conversations, has to be transcribed, though voice recognition software has made progress in automating the transcription of certain kinds of speech, such as monologues. Other stages of building a corpus are also discussed, ranging from the administrative (keeping records of texts collected) to transcribing recordings of speech. The chapter concludes with a description of various kinds of textual markup and linguistic annotation that can be added to texts. Topics discussed include how to create a “header” for a particular text. Headers contain various kinds of information. For written texts, the header would include, for instance, the title of the text; the author(s); if published, where it was published. Other textual markup is internal to the text, and in a spoken text would include such information as speaker IDs, and the beginnings and ends of overlapping speech.
Design of Visual Seismic Monitoring and Warning System Based on Zabbix

Abstract With build a modern comprehensive seismic monitoring system in Shanghai as the research background, this paper used Grafana design to monitor alarm development. It based on Zabbix open-source system. The visual seismic monitoring and warning system is designed. System can send group call, WeCom and SMS alarm to the operation and maintenance personnel when the fault occurs. It provided detailed fault data. The system improved the capability of business system fault response and handling. It provided an important guarantee for the emergency command, seismometric operation and service period operation of Shanghai Seismic Agency.
Radio Frequency Identification (RFID) Identification System for Small Wooden Traditional and Fishing Boats Less Than 7 Gross Tonnages

Small fishing and traditional wooden boats have been existed since long time ago in Indonesia that has a large number of islands. They provide public transportation and most people of near the coast’s common daily activities of working at sea as a fisherman. They are mostly made traditionally by a kind of wood that available in the country. Even though they are considered as small boat of less than GT.7 that by the regulation not required to be registered as Indonesian ship, they are still required to be recorded to maintain national data. It is the obligation of the government to provide supervision and to regulate them in order to ensure the safety and orderly administration of the boats including to identify each of the boat to establish proper national data. However there are still several difficulties in achieving those goals. Wooden boats, which are vulnerable to damage due to wear and tear, make the shape of the boat change due to the repair frequently. The changes mostly being done without formal recognition by the authority prior of repair. The change of its shape often makes the process of identifying of the boat is more difficult especially during limited time of safety inspection. The failure of boat identification can lead to serious problem with certification and further make the national data of the boat unclear. One of the concepts of achieving information is using electronic identification system such as Radio Frequency Identification (RFID). This paper provides information of RFID, their types and what can be best fitted to small wooden traditional and fishing boat in Indonesia. To compare between those type of system and which can be modified efficiently and effectively to be use on board of small wooden traditional boat. It is expected to be used widely to eliminate difficulties for Marine Inspector for boat’s identification during inspection for regular electronic certification and to assist the government to choose the one that best fitted due to unique specific condition of small wooden traditional and fishing boat.
Social Media Account Hacking Using Kali Linux-Based Tool BeEF

Nowadays, most people, especially teenagers, have at least one social media account. According to research done by Pew Research Center, more than 65% of adults used social media in 2015. While social media has undoubtedly brought some advantages of connecting people, having your digital profile online can also open the destructive potential for hackers to access your accounts. Having your social media hacked can also mean potential risks of stealing your money or promoting false information/political agendas, for example. The leading causes of this can be social media phishing and spoofing attacks. Various techniques will are discussed for hacking an individual’s social media to get desired credentials. The technique implemented is to use a fake social media website and a bridge (man in the middle) between that website and a victim’s system to collect the user information desired. In doing this, security approaches will also be discussed to help users better secure their social media accounts.
Recommendation System Based on Clustering Techniques Using Collaborative Filtering Method

Recommendation system plays a vital role in all fields in the current situation. It enables the prediction of the link between the independent compounds. In the health care domain, the recommendation system helps the caregiver to provide the medications without adverse effects by considering the previous health records. In order to keep up with the demands of modern caregiving, there has been a shift toward more holistic approaches to treatment. Care recipient data access is crucial for this purpose. By incorporating the recommendation system into linked health, we suggest a solution that will increase the potential for greater data accessibility and utilization. The result shows that clustering techniques using collaborative filtering provide a more efficient recommender system for the beneficiary. The proposed method shows that the result produced using the collaborative technique provides more accuracy when compared to the previous methods.
Wireless Hand Gesture Controlled Robot Using STM-32 Microcontroller

In today’s world, industry and daily routine tasks are increasingly attracted to and implemented through robotic automation. Due to their great precision, efficiency, and repeatability, robots are frequently utilized in a wide range of areas including industry, domestic, and even the military. In this project, the user’s hand gesture controls how the mobile robot moves. The paper suggests a practical hand gesture control robot design using STM32 Controller. This system is a human-machine interaction between the human and the robot through physical change such as tilting of the hand. Instead of using a wired controller with buttons, hand gestures are used. The system is constructed in such a way that it reduces human error and intervention, resulting in more exact work. This device can be beneficial for surveillance, physically challenged individuals.
An Extensive Study on Unattended Object Detection in Video Surveillance

Attackers usually attempt to launch operations in a crowded environment by placing explosives. They silently place explosives in objects such as bags and leave them in crowded areas. It will explode in the meantime. As a result, any Unattended Object in Public can be considered suspicious. Prior recognition of such objects is required and essential. Object detection will provide security in shopping malls, train stations, theatres, and airports. The existing system cannot detect objects in more complicated scenarios, such as videos with luminous lighting effects, and some systems have a higher false alarm rate. As a result, the proposed approach attempts to address these issues. The proposed system begins with preprocessing, which is the most crucial method. To create a high-quality image for processing, we use techniques such as Gaussian filtering, Histogram equalization, etc. The idea is then subjected to Background subtraction to detect the objects. Then it will undergo morphological operations such as dilation and erosion. Then we do Canny Edge detection and Object detection. We undertake contour tracking and size matching in object detection. The object will then go through object recognition to determine whether it is suspicious based on the criteria and threshold value we specified. If it detects something suspicious, it will send an alert message to the appropriate person; otherwise, it will update the background and repeat the process. The proposed system can also generate a report containing a list of unattended objects and the time they have been left unattended. In this case, we use the Abandoned Objects Dataset (ABODA). In situations such as different lighting effects, the proposed approach produces better results and reduces the number of false alarms.
Compartmented Proactive Secret Sharing Scheme

A secret sharing scheme commences with a secret and then generates specific shares (or shadows) for participants. A threshold number of participants are adequate to reassemble the secret. In a compartmented secret sharing scheme (CSSS), a bunch of participants is separated into distinct compartments, and only if the overall number of participants exceeds a global threshold and the participants from each compartment surpass a defined compartment threshold can the secret be discovered. In existing CSSS scheme, the Dealer makes the compartment number $$\ell _i$$ as public, and participants are unable to do both share verification and share renewal. Less privacy is available to participants because $$\ell _i$$ is public, and the Dealer may act maliciously since there is no share verification, and since the attacker has scope to know a few shares, he may reconstruct the secret. This paper proposed a Compartmented Proactive Secret Sharing Scheme to address participant privacy by revealing his compartment number only through his share along with share renewal and verification.
Hierarchical Classification of Disaster News Using Local Classifier per Parent Node

Though much work has been done to classify news headlines into generic categories like politics, sports and disasters, more needs to be done to further classify a particular class of news into a deeper level of categories (especially in the case of disaster news). So, this paper presents a hierarchical classification of disaster news into appropriate disaster categories based on their headlines. To demonstrate the effectiveness of such classification, a hierarchical classifier, trained using a local classifier per parent node approach and in mandatory leaf-node prediction setting, is compared with its flat counterpart on disaster news classification, and no significant difference in performance in accuracy is observed. However, the use of a hierarchical classifier as a substitute is justified by demonstrating its advantage in a non-mandatory leaf-node prediction setting, where a significant difference in performance in hierarchical Precision (hP) can be achieved by tuning the stopping criteria of the classifier.
The Effects of Implementing a Library Information System on the Increase in Library Use

This study aims to determine whether the implementation of information technology on library management systems brings a positive effect on the student during the learning process. Information technology is a general term for any technology that assists humans in creating, modifying, storing, communicating, and/or disseminating information. In the digital era, the use of information technology is very important. The existence of information technology in libraries can certainly increase the interest of people to keep reading books in the library because of the ease due to the implementation of information technology. The main objective of this paper is to see the impact of the use of information technology in the library management system of Bina Nusantara Alam Sutera University which has quite many students such as Binusian card as an access to the library gate and for borrowing books. Library management systems help libraries keep track of the books and their checkouts. ​A well-chosen system will increase the library's efficiency, save valuable administration time, lead to a better educational experience for students, and help develop independent learning. The results of the investigation state that several major impacts occur in the library management system due to the use or implementation of information technology. The main impact that occurs is that it is easy for library employees to record the turnover of people in the library.
Video Tampering Detection in Real Time

Videos are used everywhere nowadays for various purposes. There is great improvement in visual innovations, which has wide applications. Videos are essential in almost all industries such as surveillance cameras, social network, e-learning, advertisements, medical etc. Though the rise of digital technology made our lives easier, tampering the videos is done with ease nowadays. These changes reflect in our lives and may affect some decisions as digital videos play a vital role in every aspect. Thus we need to consider the authenticity of the video. Video tampering detection is a method to detect whether the video contents are original or modified. In this paper, to detect the forged content in the videos, we propose a deep learning approach. The aim of the model is to detect whether the video is tampered or not. The proposed method classifies the video as authentic or tampered by performing patch analysis and error level analysis using video frames and using a deep learning classifier. Patch model includes preprocessing the video frames and then separating the patches whereas in error level analysis, the video frames undergo compression level at 90% and then difference is applied. The deep learning classifier Resnet50 is used for both the methods. By applying these two techniques, video forgery can be detected.
Study on the Encryption and Decryption Capabilities of Hybrid Techniques for Images

Data plays an important role in modern life, and because more and more gadgets are connecting to the internet, the amount of data stored online is growing at an alarming rate, which raises the possibility that the information could be stolen and used unethically. The most vulnerable type of data is visual, as a large number of users today keep their personal photographs in online storage facilities such as Google Photos and Google Drive, among others. Data encryption, which can make use of either symmetric or asymmetric keys, is one of the many innovative methods that are being developed in order to improve security. This study explains various methods of encryption and decryption that are used today. In addition to that, it explains the tiniest possible implementation of an image encryption technique, which makes use of a method that is somewhat analogous to symmetric-key cryptography.
A Case Study: Disease Code (ICD-10) Classification in Turkish Medical Summary Dataset

Information Retrieval (IR) holds significant value due to the opportunities it provides such as atomicity, manageability, accuracy, and relevancy. Topic detection, as a sub-branch of Information Retrieval, receives significant attention in many areas such as marketing, finance, and education. Although it may not be as popular as the aforementioned areas, Medicine is nevertheless an area in which topic detection is significant. In this study, it is aimed to detect the disease classes (International Classification of Diseases (ICD)-10 C-Type) on the Turkish medical summaries’ dataset. The text processing, model creation (FastText and BERT), and the performance measurement steps are realized. As the results, 86% overall F1-Score by using optimized hyperparameters with FastText and 92% by using DistilBERTurk are obtained.