Reviewerly/exHarmony huggingface dataset:
A Context Aware Lung Cancer Survival Prediction Network by Using Whole Slide Images

Lung cancer has caused enormous harm to human life and traditional whole slide image (WSI) based lung cancer survival prediction methods suffer from information loss and can not maintain the spatial context of the images, which may play the important roles into survival analysis. Meanwhile, the impact of the heterogeneity between the medical images and the natural images has been noticed for some pre-trained models on medical image representation learning. In this paper, we proposed a Context Aware Lung Cancer Survival Prediction Network (CA-SurvNet) by using the whole slide images, in which the survival prediction is decided by every patch of a WSI and its associated spatial context as well. Specifically, the representation of every WSI patch is first learned via a self-supervised learning based feature extractor, and then are sequentially concatenated followed by a channel-wisely dimensional reduction to preserve the significant information and maintain the spatial structure of the WSI simultaneously. Extensive experiments on two large benchmark datasets validate the superiority of the proposed method to its state-of-the-art competitors, and also its effectiveness of the WSI context preserving into the lung cancer survival prediction.
If You Can’t Beat Them, Join Them: How Text-to-Image Tools Can Be Leveraged in the 3D Modelling Process

The rapidly growing field of text-to-image generation leverages machine learning and natural language processing to allow for the creation of very diverse and high-quality images. Whilst these images can be used in various forms and for many purposes, this paper addresses a specific question: The possibility of utilizing text-to-image generators to create concept art for the 3D-modeling process of a character. There is a potential opportunity to explore how these tools can be incorporated in traditional content creators’ pipelines. This opportunity presents itself now because of two factors: The increased capability of these text-to-image tools compared to previous generative models, as well as the lack of specific focus on creators in current research. We propose a possible pipeline to incorporate these new tools in the traditional 3D modeling process, dubbed the ‘vortex process’. This workflow follows a more circular and iterative pattern compared to examples of a more traditional process. The impact of utilizing text-to-image tools is threefold: It can boost the speed of iteration, as the speed of generating new artwork is increased compared to a human; this then allows for the exploration of more variations in a design process. Additionally, it can free up extra time to allow for extra polish of the final product. In this paper we therefore examine how the use of this technology affects the creative process. Finally, we highlight some of the potential ethical and societal implications of this technology: Loss of human jobs, issues regarding copyright of artwork used to train models etc., which will all impact future use.
Effects of Brightness and Class-Unbalanced Dataset on CNN Model Selection and Image Classification Considering Autonomous Driving

In addition to an approach of combining machine learning (ML) enhanced models and convolutional neural networks (CNNs) for adaptive CNN model selection, a thorough investigation study of the effects of 1) image brightness and 2) class-balanced/-unbalanced datasets is needed, considering image classification (and object detection) for autonomous driving in significantly different daytime and nighttime settings. In this empirical study, we comprehensively investigate the effects of these two main issues on CNN performance by using the ImageNet dataset, predictive models (premodel), and CNN models. Based on the experimental results and analysis, we reveal non-trivial pitfalls (up to 58% difference in top-1 accuracy in different class-balance datasets) and opportunities in classification accuracy by changing brightness levels and class-balance ratios in datasets.
Assessing the Vulnerability of Military Personnel Through Open Source Intelligence: A Case Study of Lithuanian Armed Forces

This study explores the vulnerability of Lithuanian Armed Forces soldiers to hostile intelligence, using Open Source Intelligence (OSINT) to identify the extent of personally identifiable information (PII) disclosure. The research found that soldiers’ social media and sports applications revealed a significant amount of sensitive data, exposing approximately 71% ± 23% of personnel as highly vulnerable to threats such as physical attacks, blackmail, and phishing. Certain privacy measures made data collection more challenging, indicating that clear media usage guidelines could improve security while maintaining soldiers’ social lives. Notably, a substantial amount of information was obtained from individuals connected to, but not in the military, emphasizing the importance of broader social network considerations in security assessments. The study was conducted manually within specific time and network limits, suggesting automation could enhance investigative scope and reduce error margins. The findings highlight the need for military procedures that account for the heightened vulnerability of soldiers and their families and the implementation of measures to mitigate these risks.
vGOAL: A GOAL-Based Specification Language for Safe Autonomous Decision-Making

Formal verification is a reliable approach to addressing safety concerns in autonomous applications. We have designed vGOAL based on the internal logic of the GOAL agent programming language, which serves as the formal specification language of our innovative formal approach to safe autonomous decision-making. A detailed description of vGOAL is necessary to present and justify our approach to safe autonomous decision-making, yet it is currently missing. Therefore, this paper aims to provide a comprehensive description of vGOAL, including its formal syntax, its operational semantics, a real-world robotic application, and a comparison with several comparable agent programming languages, namely, GOAL, Gwendolen, and AgentSpeak (Jason).
Preparation Of Simple Financial Statements For MSME Using Microsoft Excel Accounting Application

The Mawar Ikat Weaving Group in Tawiri village or negeri has not been able to record accounting or prepare financial reports in accordance with the Financial Accounting Standards for Micro, Small and Medium Entities (FAS MSMEs). This business has not implemented an automated accounting recording system yet and only uses paper. Besides, there is no human resources who understand accounting. Therefore, there is no financial reports according to FAS MSMEs. Financial reports are also recommended to be presented computerised, for example by using Microsoft Excel. Financial reports from Excel were chosen because they are neat, easy to read and understand, automatically calculated accurately, practical when there are changes, and cost-effective.The application of this standard is expected to help and facilitate the Mawar Ikat weaving business in preparing financial reports.This research is a type of action research aiming to improve the quality of a situation or problem by carrying out actions or interventions systematically planned and tested. Recording company transactions into journals and preparing financial reports involve several stages of the accounting process, namely Designing the List of Accounts, Recording Transactions in the Journal Posting to the Ledger, Preparation of the Balance Sheet, and Preparation of Financial Statements. The preparation of simple financial statements using the excel accounting application is a positive step for MSME players. Despite limited resources, a basic understanding of accounting and the ability to prepare financial statements will help MSMEs maintain the sustainability of their business and fulfil internal and external financial information needs.
Identifying Self-admitted Technical Debt with Context-Based Ladder Network

Technical debt occurs when development teams take actions to expedite the delivery of a project at the cost of poor code quality and additional work of later refactoring. The accumulation of technical debt will make the software fixes prohibitively expensive. As a typical type of technical debt, Self-Admitted Technical Debt (SATD) is acknowledged by developers in code comments. Identifying SATD in code comments can improve code quality. However, manually discerning whether code comments contain SATD would be expensive and time-consuming. To solve this problem, we propose a method to apply the Ladder Network with the pre-training model to identify SATD based on the labeled data from 10 open source projects and the unlabeled data from another ten projects. By comparing with the original model of Ladder Network, and other semi-supervised learning models, the results show that the proposed method performs better in technical debt identification. In addition, the proposed method also achieves better results compared with supervised learning methods. This shows that our approach can make better use of unlabeled data to improve classification performance.
Lightweight Certificateless Authenticated Key Agreement Protocol

Data security and privacy are important to prevent the reveal, modification and unauthorized usage of sensitive information. The introduction of using critical power devices for internet of things (IoTs), e-commerce, e-payment, and wireless sensor networks (WSNs) has brought a new challenge of security due to the low computation capability of sensors. Therefore, the lightweight authenticated key agreement protocols are important to protect their security and privacy. Several researches have been published about authenticated key agreement. However, there is a need of lightweight schemes that can fit with critical capability devices. Addition to that, a malicious key generation center (KGC) can become a threat to watch other users, i.e impersonate user by causing the key escrow problem. Therefore, we propose a lightweight certificateless Authenticated Key Agreement (AKA) based on the computation Diffie-Hellman problem (CDHP). The proposed protocol maintains the characteristics of certificateless public key cryptography. The protocol is split into two combined phases. In the first phase, our protocol establishes a session key between users (sender and receiver). In the second phase, we use a lightweight proxy blind signature based on elliptic curve discrete logarithm problem (ECDLP). The used proxy signature has small computation costs, and can fit for small devices such sensors and protects against un-authentication and un-authorization on decentralized system. Compared to the existing AKA schemes, our scheme has small computation costs. The protocol achieves the well known security features compared to the related protocols.
Democratizing Personal Website Creation: An AI- Driven Approach for Effortless, Cost-efficient, and High-Quality Web Page Generation

As the world becomes increasingly digitized, the demand for personalized websites is also gradually growing [1]. However, creating a personal website is not an easy task. Generally speaking, people can invest time and effort to learn programming languages for website development, or they can spend money to purchase services fromprofessional web development companies. Both of these methods require significant investment in terms of time or money. To democratize the creation of personal websites, we have developed a program that automatically generates personalized web pages. Users simply need to input instructions in natural language, and the program will generate a personalized website based on the information provided. Websites produced in this manner usually have high quality while simultaneously saving substantial amounts of time and money [2].
An Intelligent Mobile Application to Decrease Food Waste Across School Campuses

In this research project, we aim to solve the issue of food waste particularly in school campuses after seeing the large amount of waste thrown away each day at the school lunch. Oftentimes, students dislike the food choices they are given, and some other times, food gets thrown away when they order more than needed. To combat food waste, we created FoodShare Connect which includes three different roles, school manager, family, and students. The school manager can add the food items for students to vote, as well as the food available for giveaways. This will effectively decrease food waste as the students get to vote for the food that they like, and the unused food products can be distributed to families who want it. Throughout the process, we resolved the issue of preventing users from signing up with the wrong role, dealt with data storage, and prevented students from voting more than once a day. This is done mostly with the use of Google Firebase, where we store the data, and Flutter language. We made sure to save all the necessary information on Firebase, check whether the user is using the same role to log in as their role saved in Firebase, and override all the voting information as the new day begins. Overall, we believe this APP can tremendously decrease food waste in school campuses, save a lot more for others in need, and most importantly, save our environment.
A Data-Driven Strategy for Online Hate Speech Spreader Identification using Modified Pagerank

Social media platforms have become breeding grounds for the dissemination of misinformation and harmful content, including hate speech. This research paper aims to tackle the urgent problem of hate speech circulation on Online Social Networks by primarily focusing on the early identification of users who are prone to spreading it. To achieve this objective, a novel data-driven metric is introduced, referred to as ’Hate Speech Potential’. Additionally, an innovative approach is proposed that leverages a modified version of the PageRank algorithm, termed the ’Hate Speech Potential Rank’ algorithm, to effectively detect and identify malicious users within a network. In a vast network with billions of nodes, the rapid spread of content makes timely detection and mitigation crucial. By assessing a user’s past behaviour of sharing or publishing hate speech, their ’Hate Speech Potential’ can be determined, enabling the identification of sources and spreaders of such content. The modified PageRank algorithm considers both the user’s individual characteristics and the influence of their neighbourhood, thereby capturing a more comprehensive picture of their sharing patterns. A pre-trained machine learning model was employed to accurately classify hate speech posts. By combining the predicted labels and user characteristics and implementing the modified PageRank algorithm, this paper aims to gain deeper insights into the dynamics of information dissemination within a social network, thereby contributing to a better understanding of user sharing behaviour and facilitating the development of effective strategies for addressing hate speech. K-Means clustering was used in experimental evaluations, demonstrating the effectiveness of the proposed approach.
Chinese Named Entity Recognition based on Knowledge-Based Question

The knowledge-Based Question Answering (KBQA) system is an essential part of the customer service system aiming to answer natural language questions by recovering the structured data stored under the knowledge base. KBQA answers the natural language questions by recovering the structured data stored under the knowledge base.KBQA receives the user’s query and first needs to recognize the topic for the query entities like the location, name,organization, etc., The process is Named Entity Recognition (NER) using the Bidirectional Long Short-Term Memory Conditional Random Field model, and the SoftLexicon method is introduced as the Chinese NER tasks. A fuzzy matching module is proposed to analyze the application scenario characteristics using multiple methods. The module efficiently modifies the error recognition results, improving entity recognition performance. The fuzzy matching and the NER model are combined into the NER system. The power grid-related original data is collected to improvise the system following the power grid data characteristics.
Forest Mixing: Investigating the Impact of Multiple Search Trees and a Shared Refinements Pool on Ontology Learning

We aim at development white-box machine learning algorithms. We focus here on algorithms for learning axioms in description logic. We extend the Class Expression Learning for Ontology Engineering (CELOE) algorithm contained in the DL-Learner tool. The approach uses multiple search trees and a shared pool of refinements in order to split the search space in smaller subspaces. We introduce the conjunction operation of best class expressions from each tree, keeping the results which give the most information. The aim is to foster exploration from a diverse set of starting classes and to streamline the process of finding class expressions in ontologies. The current implementation and settings indicated that the Forest Mixing approach did not outperform the traditional CELOE. Despite these results, the conceptual proposal brought forward by this approach may stimulate future improvements in class expression finding in ontologies.
Saving Endangered Languages with a Novel Three-Way Cycle Cross-Lingual Zero-Shot Sentence Alignment

Sentence classification, including sentiment analysis, hate speech detection, tagging, and urgency detection is one of the most prospective and important subjects in the Natural Language processing field. With the advent of artificial neural networks, researchers usually take advantage of models favorable for processing natural languages including RNN, LSTM and BERT. However, these models require huge amount of language corpus data to attain satisfactory accuracy. Typically this is not a big deal for researchers who are using major languages including English and Chinese because there are a myriad of other researchers and data in the Internet. However, other languages like Korean have a problem of scarcity of corpus data, and there are even more unnoticed languages in the world. One could try transfer learning for those languages but using a model trained on English corpus without any modification can be sub-optimal for other languages. This paper presents the way to align cross-lingual sentence embedding in general embedding space using additional projection layer and bilignual parallel data, which means this layer can be reused for other sentence classification tasks without further fine-tuning. To validate power of the method, further experiment was done on one of endangered languages, Jeju language. To the best of my knowledge, it is the first attempt to apply zero-shot inference on not just minor, but endangered language so far.
Enhancing Transfer Learning Across Annotation Schemes with Minerr: A Novel Metric

This paper introduces MINERR (MINimal ERRor evaluation metric between consecutive tasks), a novel metric designed to enhance the efficiency of transfer learning in the context of argument structure identification. One of the principal challenges in the Argument Mining field pertains to the need for high- quality training data, which requires achieving a high level of interannotator agreement for argument constituents. Therefore, datasets within this domain tend to be smaller compared to those in other domains. To address this issue, we propose the consolidation of different datasets and employ the classical two-step method for argument identification, encompassing the identification of argumentative spans and the categorization of labels. An issue related to the separation of these two tasks is the errors interconnectedness between them. To tackle this problem, we introduce a new metric that distinguishes errors stemming from incorrect labelling and errors arising from span misidentification. Our approach incorporates a novel method for dissecting the prediction error of an argument component labelling task into two distinct categories: errors caused by misidentifying the component and errors resulting from assigning incorrect labels. Subsequently, we evaluate our method using a corpus including four distinct argumentation datasets. Overall, this work facilitates the development of a new transfer learning methodology for the application of diverse argument annotation schemes.
PEMBUATAN MEDIA PEMBELAJARAN MATEMATIKA UNTUK ANAK USIA 6-8 TAHUN BERBASIS ANDROID MENGGUNAKAN UNITY 3D

This pandemic requires technology to enter into all aspects of life, including the education aspect. Advances in information technology certainly support various needs for delivering information in the field of education, one of which is the existence of game-based learning media. School learning which is often boring, in this pandemic situation, it is imperative to change it in a more fun way, especially in mathematics. The existence of learning media with anadroid-based games is expected to make it easier to understand learning to count addition. The game "Menjumlah Yuk" is made using Unity. This application provides a home page, instructions page, question page, info page, and destination page. This application has been tested based on the functionality of all the menus created.
Artificial Intelligence in combating cyber threats in Banking and Financial services

Artificial intelligence (AI) is a powerful technology that can enhance the banking and financial sector by providing various opportunities, such as robo-advice, personal data protection, fraud detection, and customer behavior analysis. However, AI also poses some challenges, such as high cost, unemployment, cybercrime, and lack of transparency and explainability. This paper discusses the benefits and drawbacks of AI in the context of cyber security and cybercrime prevention. It also suggests some ways to overcome the limitations and risks of AI, such as ethical design, regulation, education, and human oversight. The paper aims to provide a comprehensive overview of the current and future applications of AI in the banking and financial industry, as well as the potential threats and solutions.
LUT-LIC: Look-Up Table-Assisted Learned Image Compression

Image compression is indispensable in many visual applications. Recently, learned image compression (LIC) using deep learning has surpassed traditional image codecs such as JPEG in terms of compression efficiency but at the cost of increased complexity. Thus, employing LIC in resource-limited environments is challenging. In this paper, we propose an LIC model using a look-up table (LUT) to effectively reduce the complexity. Specifically, we design an LUT replacing the entropy decoder by analyzing its input characteristics and accordingly developing a dynamic sampling method for determining the indices of the LUT. Experimental results show that the proposed method achieves better compression efficiency than traditional codecs with faster runtime than existing LIC models.