Reviewerly/exHarmony huggingface dataset:
PERBANDINGAN METODE LVQ DAN BACKPROPAGATION UNTUK KLASIFIKASI STATUS GIZI ANAK DI KECAMATAN SANGKUP

The problem of children nutrition isi still a problem in various regions in Indonesia. Poor or poor nutrition of children is influenced by several factors, namely insufficient food intake and infectious diseases. Undernutrition or poor nutrition can be known from the nutritional status assessment obtained from classifying the nutrional status of children. Classification is a part of data mining that is often used to classify data based on certain data or variables. This study aims to compare the classification of the nutritional status of children using data mining with the learning vector quantization (LVQ) and backpropagation methods. Test were carried out using a comparasion ratio of training and testing data, namely 75% and 25%. From the research results, LVQ is superior with an accuracy of 95.12% and backpropagation of 80.49%.
Modelling Text Similarity: A Survey

Online social networking services such as Twitter and Instagram have become pervasive platforms for engaging in discussions on a wide array of topics. These platforms cater to both mainstream subjects, like music and movies, as well as more specialized areas, such as politics. With the growing volume of textual data generated on these platforms, the ability to define and identify similar texts becomes crucial for effective investigation and clustering. In this paper, we explore the challenges and significance of text similarity regression models in the context of online social networking services. We delve into the methods and techniques employed to define and find similarities among texts, enabling the extraction of meaningful patterns and insights. Specifically, we categorize text similarity regression models into four distinct types: set-theoretic, sequence-theoretic, real-vector, and end-to-end methods. This categorization is based on the mathematical formalisation of similarity used by each model. Ultimately, our survey aims to provide a comprehensive overview of the interlinkages between independently proposed methods for text similarity. By understanding the strengths and weaknesses of these methods, researchers can make informed decisions when designing novel approaches and algorithms. We hope this survey serves as a valuable resource for advancing the state-of-the-art in addressing the complex problem of text similarity.
Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm

Recommender systems play a crucial role in suggesting relevant content to users based on their past activities. They employ a process known as "collaborative filtering" to efficiently navigate extensive content repositories. However, concerns have been raised regarding the potential bias and homogeneity in recommendations, resulting in filter-bubbles and echo-chambers. Detecting and mitigating these biases is crucial for ensuring fair and diverse automated decision-making systems. This study investigates the impact of YouTube's recommendation algorithm on three distinct narratives across multiple dimensions. Our objective is to identify potential biases and gain insights into its decision-making behavior. We applied a multi-method approach to evaluate emotional content, moral foundations, lexical similarity, and social network analysis across 5 depths of YouTube recommendations. The results of our analysis showed diversity in emotions, significant drift in topics, and a push toward non-related, but highly influential videos across multiple recommendation depths. The findings from this study contribute to the understanding of bias in recommender systems. These insights inform the development of strategies to mitigate biases and improve the user experience. Policymakers and platform developers can utilize this knowledge to establish effective guidelines and policies for their recommender systems, enhancing decision-making processes.
US-Net: A Breast Ultrasound Image Segmentation using Deep Learning

Segmentation of medical images is a crucial step in many clinical applications, including the precise diagnosis and treatment of diseases like breast cancer. Therefore, automated segmentation of breast tumors from breast ultrasound images remains a challenging task. In this paper, we developed a new model, called Ultrasound Network (US-Net), which uses the U-Net architecture with attention gates embedded in the skip connections to assign weights to feature maps based on their importance for the segmentation task. Our method underwent evaluation on three public datasets: BUSI, UDIAT, and STUHospital, using the Dice coefficient as the primary metric for segmentation performance. Notably, US-Net achieved impressive Dice coefficients of 86.99%, 94.38%, and 94% on BUSI, UDIAT, and STUHospital, respectively. Experimental results showed that our network outperformed the latest image segmentation methods for lesion segmentation in breast ultrasound.
Applying Social Network Embedding and Word Embedding for Socialbots Detection

With the growth of social networking website, social media has become a major platform for marketing, such as social business, political manipulation, influence and brand management, etc. However, social media marketing is very different to traditional marketing. Social media marketing needs to face large number of users and need to repeat same process frequently. It is therefore a very human power consuming task. Under this situation, it is the reason why Robotic Process Automation and Social-bots is now very popular in many social networking websites. However, there are many negative effects when applying social-bots for social marketing. Therefore, more and more researchers are devoting on propose efficient ways to detect social-bots. In this paper, we proposed an approach to detect social-bots by considering the content that users posted as well as the behavior and features when using social networking website. In this approach, we adopt the concept of word embedding and social network embedding. Convolutional neural network is used as the main techniques to train the model for social-bots detection. The experimental results show that the proposed combination approach has better detection accuracy than only social network embedding or word embedding approach as well as it reaches 92% detection accuracy by using our dataset.
Characterizing Suspicious Commenter Behaviors

YouTube has revolutionized content consumption and global user interaction. It has become a central hub for video sharing, entertainment, and information dissemination. However, as the user base continues to expand and actively engage with the platform, concerns have arisen regarding the presence of suspicious behavior among commenters. This study presents an approach based on social network analysis to detect suspicious commenter behaviors and identify similarities across various YouTube channels in relation to such behaviors. The analysis involves 20 YouTube channels that disseminated false views about the U.S. Military. The dataset included 7,782 videos, 294,199 commenters, and 596,982 comments. We employ a combination of methods, including Graph2vec, UMAP, K-means, Hierarchical clustering, qualitative and quantitative analyses. The objective is to categorize channels based on the level of suspicious behavior and reveal common patterns exhibited among them. To assess the effectiveness of the proposed methodology, the outcomes revealed the presence of commenter mobs and significant similarities among these channels, providing valuable insights into the prevalence of suspicious commenter behavior.
Tracking User Sentiment Changes on Social Networks

We present a time-dependent approach for learning temporal co-variates explaining the subsequent user sentiments in social networks. In most of the existing approaches, we note that the underlying text classification setting, generally used to model user sentiments, is designed to ingest a user comment generated at time t to predict his corresponding sentiment at the same time. Under such constraint, user sentiments can only be given whenever she or he has generated a comment. Furthermore, the evolving historical sentiments are omitted and no anticipation of subsequent sentiments could be made. To alleviate this limitation, we propose a time-dependent approach that takes advantage of historical user comments to learn temporal co-variates that explain their evolving sentiments. We demonstrate that our approach could be used to predict user sentiments at subsequent times ahead. Experimental results on Tweets data, during the Covid-19 pandemic, illustrate the suitability of our approach.
PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance of GCNs on ML tasks. Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks. These subgraph-based sampling approaches rely on heuristics - such as graph partitioning via edge cuts - to identify clusters that are then treated as minibatches during GCN training. In this work, we hypothesize that rather than relying on such heuristics, one can learn a reinforcement learning (RL) policy to compute efficient clusters that lead to effective GCN performance. To that end, we propose PolicyClusterGCN, an online RL framework that can identify good clusters for GCN training. We develop a novel Markov Decision Process (MDP) formulation that allows the policy network to predict "importance" weights on the edges which are then utilized by a clustering algorithm (Graclus) to compute the clusters. We train the policy network using a standard policy gradient algorithm where the rewards are computed from the classification accuracies while training GCN using clusters given by the policy. Experiments on six real-world datasets and several synthetic datasets show that PolicyClusterGCN outperforms existing state-of-the-art models on node classification task.
pyStudio: An Open-Source Machine Learning Platform

Data analytics has emerged as a critical capability for businesses and organizations in the modern era. The abundance of data necessitates a deep understanding and the exploitation of its potential to gain insights into current and future scenarios.
A Multidisciplinary Lens of Bias in Hate Speech

Hate speech detection systems may exhibit discriminatory behaviours. Research in this field has focused primarily on issues of discrimination toward the language use of minoritised communities and non-White aligned English. The interrelated issues of bias, model robustness, and disproportionate harms are weakly addressed by recent evaluation approaches, which capture them only implicitly. In this paper, we recruit a multidisciplinary group of experts to bring closer this divide between fairness and trustworthy model evaluation. Specifically, we encourage the experts to discuss not only the technical, but the social, ethical, and legal aspects of this timely issue. The discussion sheds light on critical bias facets that require careful considerations when deploying hate speech detection systems in society. Crucially, they bring clarity to different approaches for assessing, becoming aware of bias from a broader perspective, and offer valuable recommendations for future research in this field.
Auditing Gender Analyzers on Text Data

AI models have become extremely popular and accessible to the general public. However, they are continuously under the scanner due to their demonstrable biases toward various sections of the society like people of color and non-binary people. In this study, we audit three existing gender analyzers -uClassify, Readable and HackerFactor, for biases against non-binary individuals. These tools are designed to predict only the cisgender binary labels, which leads to discrimination against non-binary members of the society. We curate two datasets - Reddit comments (660k) and, Tumblr posts (2.05M) and our experimental evaluation shows that the tools are highly inaccurate with the overall accuracy being ≈ 50% on all platforms. Predictions for non-binary comments on all platforms are mostly female, thus propagating the societal bias that non-binary individuals are effeminate. To address this, we fine-tune a BERT multi-label classifier on the two datasets in multiple combinations, observe an overall performance of ≈ 77% on the most realistically deployable setting and a surprisingly higher performance of 90% for the non-binary class. We also audit ChatGPT using zero-shot prompts on a small dataset (due to high pricing) and observe an average accuracy of 58% for Reddit and Tumblr combined (with overall better results for Reddit).
Combating Identity Attacks in Online Social Networks: A Multi-Layered Framework Using Zero-Knowledge Proof and Permissioned Blockchain

Identity attacks, such as impersonation, identity theft, and fraudulent account creation, pose significant threats to the security and trustworthiness of Online Social Networks (OSNs). In this paper, we propose a robust and secure framework to verify user identities without compromising their privacy by developing a multi-layered framework leveraging zero-knowledge proof (ZKP) and Hyperledger Fabric private blockchain. We introduce a blockchain-based government identity provider system, coupled with a zero-knowledge proof-based signup process for social networks. Our prototype authenticates user identities in multiple layers, effectively mitigating fraudulent, cloned, and multiple account creations. Our experiments with n (n = 50) users showed a 100% success rate for our system, highlighting its effectiveness compared to other OSNs.
RURLMAN: Matching Forum Users Across Platforms Using Their Posted URLs

How can we leverage the URLs posted on online forums to connect forum users with their profiles on other platforms? Most previous studies primarily focus on analyzing textual content and user metadata, paying limited attention to URLs. In this paper, we propose RURLMAN, a modular ensemble of methods for leveraging user-posted URLs to connect online forum users with their cross-platform profiles. Our approach has two key features: (a) we focus on user-posted URLs as the key source of information, and (b) we utilize a modular stacked ensemble integrating multiple methods, including string-matching and two ChatGPT capabilities. We show that RURLMAN effectively combines the strengths of its component methods, outperforming each individual method with an F1 score of 92.6%. We apply RURLMAN in a case study comprising 1.3M URLs posted by 250K forum users across six online security forums and consider URLs to Twitter, Facebook, GitHub, and YouTube. First, we match 30% of the users who shared URLs to these platforms with the corresponding owners of the linked social media profiles. Second, we connect 8% of these users to profiles on multiple platforms. Finally, we identify and analyze "groups" of users based on their posted URLs. To facilitate further research, we will share access to RURLMAN and its datasets with the research community.
FEGR: Feature Enhanced Graph Representation Method for Graph Classification

Graph representation plays a key role in graph analytics to perform a variety of downstream machine-learning tasks. This paper presents a novel method for extracting expressive graph representation based on a combination of statistics captured from a graph and node properties. We use both local and global-level information along with the original node properties to extract a meaningful feature representation of the graph. This allows us to build expressive graph descriptors that can be run with limited training data and computational resources and achieve competitive results. We discuss the merits of the proposed approach in terms of sensitivity, running times, and stability. Our evaluation of various graph classification benchmark datasets shows that the proposed method either outperforms or provides similar results to state-of-the-art methods. We further outline the potential future directions in graph machine learning research.
Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities

Community Question Answering (CQA) in different domains is growing at a large scale because of the availability of several platforms and huge shareable information among users. With the rapid growth of such online platforms, a massive amount of archived data makes it difficult for moderators to retrieve possible duplicates for a new question and identify and confirm existing question pairs as duplicates at the right time. This problem is even more critical in CQAs corresponding to large software systems like askubuntu where moderators need to be experts to comprehend something as a duplicate. Note that the prime challenge in such CQA platforms is that the moderators are themselves experts and are therefore usually extremely busy with their time being extraordinarily expensive. To facilitate the task of the moderators, in this work, we have tackled two significant issues for the askubuntu CQA platform: (1) retrieval of duplicate questions given a new question and (2) duplicate question confirmation time prediction. In the first task, we focus on retrieving duplicate questions from a question pool for a particular newly posted question. In the second task, we solve a regression problem to rank a pair of questions that could potentially take a long time to get confirmed as duplicates. For duplicate question retrieval, we propose a Siamese neural network based approach by exploiting both text and network-based features, which outperforms several state-of-the-art baseline techniques. Our method outperforms DupPredictor [33] and DUPE [1] by 5% and 7% respectively. For duplicate confirmation time prediction, we have used both the standard machine learning models and neural network along with the text and graph-based features. We obtain Spearman's rank correlation of 0.20 and 0.213 (statistically significant) for text and graph based features respectively. We shall place all our codes and data in the public domain upon acceptance.
Improving Chinese Fact Checking via Prompt Based Learning and Evidence Retrieval

Verifying the accuracy of information is a constant task as the prevalence of misinformation on the Web. In this paper, we focus on Chinese fact-checking (CHEF dataset) [1] and improve the performance through prompt-based learning in both evidence retrieval and claim verification. We adopted the Automated Prompt Engineering (APE) technique to generate the template and compared various prompt-based learning training strategies, such as prompt tuning and low-rank adaptation (LoRA) for claim verification. The research results show that prompt-based learning can improve the macro-F1 performance of claim verification by 2%-3% (from 77.62 to 80.29) using golden evidences and 110M BERT based model. For evidence retrieval, we use both the supervised SentenceBERT [2] and unsupervised PromptBERT [3] models to improve evidence retrieval performance. Experimental results show that the micro-F1 performance of evidence retrieval is significantly improved from 11.86% to 30.61% and 88.15% by PromptBERT and SentenceBERT, respectively. Finally, the overall fact-checking performance, i.e. the macro-F1 performance of claim verification, can be significantly improved from 61.94% to 80.16% when the semantic ranking-based evidence retrieval is replaced by SentenceBERT.
Knowledge Graph Embedding for Topical and Entity Classification in Multi-Source Social Network Data

Historically, online data has provided meaningful insights for information mining, leading to the adoption of knowledge graphs for application to online data. Knowledge embedding has become an important aspect of encoding and decoding links, relationships, and predicting the ties of an entity to an existing knowledge graph. This study applied topic modeling to extract topics, entities, and themes from heterogeneous web data from different sources around the Indo-Pacific region and modeled a knowledge graph. The knowledge graph was subjected to knowledge embedding by applying four scoring mechanisms: ComplEx, TransE, DistMult, and HolE, on a domain knowledge graph of Indo-Pacific Belt and Road initiatives to determine whether it was capable of revealing missing insights. This work significantly uses knowledge graphs and embedding to understand socioeconomic-related discussions online. Valuable insights were gained from the data in this research's clustering results of knowledge embedding. Important themes such as NASAKOM and BRI were identified in Cluster 0. Cluster 1 contained themes that discussed Marxist movements synonymous with Indonesia, and Cluster 2 showed themes on China's road policies, such as Asia-Pacific Economic Cooperation and Export-Import Bank China. Cluster 3 focused mainly on China's economic policies and the Philippines. Overall, this study demonstrates the usefulness of topic modeling and knowledge embedding in uncovering insights from online data and has implications for understanding socioeconomic trends in the Indo-Pacific region.
AD-TIN: Edge Anomaly Detection for Temporal Interaction Networks using Multi-representation Attention

Anomaly detection in temporal interaction networks (TINs) has become critical in network security, digital finance, and social networks. While recent studies based on Graph Neural Networks (GNNs) have yielded promising results, the existing methods are still limited by insufficient labels and noisy data, often ignoring the information filtering for unrelated user interactions. Therefore, this paper proposes a dynamic edge anomaly detection framework, AD-TIN, to address these challenges based on a multi-representation attention mechanism. It encodes graph structural information using a network information propagation module with neighbor sampling and graph diffusion. Furthermore, the network update module combines past node states with current structural features to capture the temporal information in potential user relationships, effectively mitigating the impact of noisy data. Extensive experiments on three real-world datasets demonstrate the robustness and efficacy of AD-TIN in addressing noise and unrelated interactions for edge anomaly detection.
Exploring the Landscape of Drug Communities on Reddit: A Network Study

In a world where the use of social media is almost universal, understanding its influence on drug information consumption has become a crucial topic of research. This study delves into the networks of drug-centric subreddits to gain insights into community structures and the nature of user interactions within Reddit. After conducting a thematic analysis of 131 drug-related subreddits, we selected four dominant ones for a comprehensive network analysis, examining the broader interactions of their active users. Our results show that drug-related subreddits are highly connected to each other, resembling small-world networks, and tangential subreddits, like r/AskReddit and r/gaming, are influential in connecting different parts of networks. While each community within the sub-networks has its unique focus, there is a clear convergence around shared themes and interests, notably harm reduction and mutual support. Our research not only underscores the pivotal role of online platforms like Reddit in bridging the active members of drug-related subreddits to a broader spectrum of content but also highlights the underlying commonalities that weave these communities together, emphasizing their mutual interests and shared values.