Reviewerly/exHarmony huggingface dataset:
Threats, Opportunities, and Misconceptions

The release of ChatGPT was a significant milestone in AI, not just because of its groundbreaking capabilities and its pushing of the boundaries of technology but also because of the unprecedented interest it generated in the general public. While AI technology components have been part of day-to-day technology for decades, this level of enthusiasm from the general public was previously unheard of. This widespread enthusiasm, as well as the media hype around them, has caused some misunderstandings and misinterpretations of LLMs and their capabilities. This has led to some concern, and in some cases fear, toward LLMs and AI technology in general. There are some aspects Large language models (LLMs) aspects regarding LLMs that pose legitimate concerns and need to be addressed as the technology progresses and gets applied. However, from the conversations happening about LLMs, it is clear that some of the concerns gaining traction are misplaced.
NLP Through the Ages

Natural language processing (NLP) is a subfield of artificial intelligence and computational linguistics. It focuses on enabling computers to understand, interpret, and generate human language in a way that is both meaningful and useful. The primary goal of NLP is to bridge the gap between human language and computer understanding, allowing machines to process, analyze, and respond to natural language data. LLMs would not exist without the concepts and algorithms developed through NLP research over the years. Therefore, to understand LLMs, we need to understand the concepts of NLP.
Transformers

In 2017, Ashish Vaswani et al. from Google Brain and Google Research proposed a revolutionary new architecture of neural networks for natural language processing (NLP) and other sequence-to-sequence tasks in their “Attention Is All You Need” paper. In this paper, Vaswani et al. presented a new approach that relies heavily on attention mechanisms to process sequences, allowing for parallelization, efficient training, and the ability to capture long-range dependencies in data.
PENGEMBANGAN LAYANAN PEMANTAU ANAK BERBASIS LOKASI PADA PERANGKAT ANDROID

These day technology develops rapidly. This matter affect the techonology utilization in many aspect today, especially when adults, teenagers and children were using Andoid smartphone. For some parents, it’s hard to spare a little time for their family and their children. This matter cause parent’s to less-care about their children and not be able to monitor their children’s activity, moreover some of this children could be in a place which they should not visit. 85% respondents from public who had married, need a monitoring service to know their children location. In nowadays, this monitoring services have already been design, however there is always some difficulty in using this kind of services because of the only language that available was english, and moreover the server was build and located overseas. This research was intended to create an monitoring sevice application based on location on Android devices that available in Indonesian to be used by parents. This application use GPS feature, Geofencing, and Google Maps that available on Android Devices.
Detecting Adversarial Examples via Classification Difference of a Robust Surrogate Model

Existing supervised learning detectors may deteriorate their performance when detecting unseen adversarial examples (AEs), because they may be sensitive with training samples. We found that (1) the CNN classifier is modest robust against AEs generated from other CNNs, and (2) such adversarial robustness is rarely affected by unseen instances. So, we construct an attack-agnostic detector based on an adversarial robust surrogate CNN to detect unknown AEs. Specifically, for a protected CNN classifier, we design a surrogate CNN classifier and predict the image with different classification labels on them as an AE. In order to detect transferable AEs and maintain low false positive rate, the surrogate model is distilled from the protected model, aiming at enhancing the adversarial robustness (i.e., suppress the transferability of AE) and meanwhile mimicking the output of clean image. To defend the potential ensemble attack targeted at our detector, we propose a new adversarial training scheme to enhance the security of the proposed detector. Experimental results of generalization ability tests on Cifar-10 and ImageNet-20 show that our method can detect unseen AEs effectively and performs much better than the state-of-the-arts.
Quantized SGD in Federated Learning: Communication, Optimization and Generalization

Federated Learning (FL) has gained significant attention due to its impressive scalability properties and its ability to preserve data privacy. To improve the communication efficiency of FL, quantization techniques have emerged as commonly used approaches. However, the introduction of randomized quantization in FL can introduce additional variance, impacting the accuracy of the models. Furthermore, few studies in the existing literature have explored the impact of quantization on the generalization ability of FL algorithms. In this paper, we focus on examining the interplay among key factors in the widely used distributed Stochastic Gradient Descent (SGD) algorithm with quantization. Specifically, we investigate the relationship between quantization level, optimization error, and generalization performance. For convex objectives, our main results reveal several trade-offs between communication efficiency, optimization error, and generalization performance. In the case of non-convex objectives, our theoretical findings indicate that the quantization level has a more significant impact on the generalization ability compared to convex cases. Moreover, our derived generalization bounds for non-convex objectives suggest that early stopping may be necessary to ensure a certain level of generalization accuracy, even when the step size in SGD is very small. Finally, we conduct several numerical experiments utilizing logistic models and deep neural networks to validate and support our theoretical findings.
ANALISIS KUALITAS LAYANAN WEBSITE SMK NEGERI 4 KOTA JAMBI MENGGUNAKAN METODE WEBQUAL 4.0

In finding information, we often experience difficulties, but having a website makes it easier for us to obtain information, one of which is on the educational website, namely SMK Negeri 4 Jambi City, which is a site that provides information about schools, accepting new students and other information. However, the information service available on the SMK Negeri 4 Jambi City website still has a number of problems, including the student menu, which is in the rules and regulations section, the alumni menu is in the alumni directory section, the work unit menu is in the job fair (BKK) section, curriculum, and student affairs. The purpose of this study was to determine the quality of the website at SMK Negeri 4 Jambi City using the webqual (Website Quality) method. The webqual method itself consists of several dimensions, namely quality of use, quality of information, quality of service interaction with quantitative data analysis techniques obtained from questionnaires. With a total sample of 100 respondents and data processing using SmartPLS 4 software. The results showed that of the 3 (three) variables in the model (hypotheses) tested, 2 hypotheses had a significant effect and 1 other hypothesis had no significant effect. The 2 hypotheses accepted are H2 and H3. While 1 hypothesis that is rejected is H1. Overall the website of SMK Negeri 4 Jambi City has a positive impact
Interpreting Decision Process in Offline Reinforcement Learning for Interactive Recommendation Systems

Recommendation systems, which predict relevant and appealing items for users on web platforms, often rely on static user interests, resulting in limited interactivity and adaptability. Reinforcement Learning (RL), while providing a dynamic and adaptive approach, brings its unique challenges in this context. Interpreting the behavior of an RL agent within recommendation systems is complex due to factors such as the vast and continuously evolving state and action spaces, non-stationary user preferences, and implicit, delayed rewards often associated with long-term user satisfaction. Addressing the inherent complexities of applying RL in recommendation systems, we propose a framework that includes innovative metrics and a synthetic environment. The metrics aim to assess the real-time adaptability of an RL agent to dynamic user preferences. We apply this framework to LastFM datasets to interpret metric outcomes and test hypotheses regarding MDP setups and algorithm choices by adjusting dataset parameters within the synthetic environment. This approach illustrates potential applications of our framework, while highlighting the necessity for further research in this area.
Heterogeneous Graph Fusion with Adversarial Learning for Recommendation Service

Social recommendation, in particular, relies on modeling social information to provide high-order information beyond user-item interaction in recommendation service. However, current approaches rely on graph neural network-based embedding, which can result in an over-smoothing problem. Additionally, graph diffusion, which encodes high-order features, can add noise to the model. Previous research has not adequately addressed the latent influence of social relations. In this work, we introduce a new recommendation framework named Heterogeneous Information Network Fusion with Adversarial Learning (HIN-FusionGAN), which inherently fuses adversarial learning-enhanced social networks with the fused graph between the user-item interaction graph and user-user social graph. We propose a heterogeneous information network that fuses social and interaction graphs into a unified heterogeneous graph, explicitly encoding high-order collaborative signals. We employ user embeddings using both interaction information and adversarial learning-enhanced social networks, which are efficiently fused by the feature fusion model. To address the issue of over-smoothing and uncover latent feature representation, we use the structure of an adversarial network in social relation graph. Comprehensive experiments on three real-world datasets demonstrate the superiority of our proposed model.
A Relation-Oriented Approach for Complex Entity Relation Extraction

Entity relation extraction targets the extraction of structured triples from unstructured text, and is the start of the entire knowledge graph lifecycle. In recent advances, Machine Reading Comprehension (MRC) based approaches provide new paradigms for entity relationship extraction and achieve the state-of-the-art performance. Aiming at the features of nested entities, overlapping relationships and distant entities in recipe texts, this study proposes a relation-oriented approach for complex entity relation extraction. This approach addresses the entity redundancy problem caused by the traditional pipeline models which are entity-first methods. By predicting the starting and ending of entities, it solves the problem of nested entities that cannot be identified by traditional sequence labeling methods. Finally, the error propagation issue is mitigated by the triple determination module. We conduct extensive experiments on multi-datasets in both English and Chinese, and the experimental results show that our method significantly outperforms the baseline model in terms of both precision, recall and micro-F1 score.
Regulatory Recommendations for Fraud Problem in The Turkish Telecommunication Sector

Fraud has been a persistent issue throughout human history. As technology continues to advance in various fields, fraudulent activities adapt and evolve accordingly. The telecommunication industry, in particular, has undergone significant transformations since the early 2000s with the advent of mobile technologies. It is evident that telecommunication fraud has seen a substantial increase during this time, leading to serious financial and reputational damage. Therefore, combating and preventing fraud has become a crucial task in the telecommunication sector, as it is in all industries. This study delves into the topic of fraud, with a particular emphasis on telecommunication fraud. It investigates the experiences and efforts made to minimize and prevent fraud globally. Additionally, the study includes a focus group analysis involving two mobile operators in Turkey, aiming to understand the current situation and industry expectations concerning telecommunication fraud within the country. After evaluating the information gathered and examining the existing efforts, the study offers a series of regulatory recommendations for reducing and preventing fraud in the Turkish telecommunication sector.
Curve Enhancement: A No-Reference Method for Low-Light Image Enhancement

In this paper, we introduce an end-to-end method for enhancing low-light images without relying on paired datasets. Our solution is reference-free and unsupervised, addressing the lack of real-world low-light paired datasets effectively. Specifically, we design a Brightness Boost Curve (BB-Curve) that enhances the brightness of image pixels in a finely mapped form. Additionally, we propose a lightweight deep neural network that can estimate the curve parameters and evaluate the quality of the enhanced images using a series of no-reference loss functions. We validate our method through experiments conducted on several datasets and provide both subjective and quantitative evaluations to demonstrate its significant brightness enhancement capabilities, free from smearing and artifacts. Notably, our approach displays a strong ability to generalize while retaining details that are crucial for image interpretation. With the reduced network structure and simple curve mapping, our model yields superior training speed and the best prediction performance among comparative methods.
Explainable Offensive Language Classifier

Offensive content in social media has become a serious issue, due to which its automatic detection is a crucial task. Deep learning approaches for Natural Language Processing (or NLP) have proven to be on or even above human-level accuracy for offensive language detection tasks. Due to this, the deployment of deep learning models for these tasks is justified. However, there is one key aspect that these models lack, which is explainability, in contrast to humans. In this paper, we provide an explainable model for offensive language detection in the case of multi-task learning. Our model achieved an F1 score of 0.78 on the OLID dataset and 0.85 on the SOLID dataset. We also provide a detailed analysis of the model interpretability.
Multi-model Smart Contract Vulnerability Detection Based on BiGRU

Smart contracts have been under constant attack from outside, with frequent security problems causing great economic losses to the virtual currency market, and their security research has attracted much attention in the academic community. Traditional smart contract detection methods rely heavily on expert rules, resulting in low detection precision and efficiency. This paper explores the effectiveness of deep learning methods on smart contract detection and propose a multi-model smart contract detection method, which is based on a multi-model vulnerability detection method combining Bi-directional Gated Recurrent Unit (BiGRU) and Synthetic Minority Over-sampling Technique (SMOTE) for smart contract vulnerability detection. Through a comparative study on the vulnerability detection of 10312 smart contract codes, the method can achieve an identification accuracy of 90.17% and a recall rate of 97.7%. Compared with other deep network models, the method used in this paper has superior performance in terms of recall and accuracy.
Efficient Chinese Relation Extraction with Multi-entity Dependency Tree Pruning and Path-Fusion

Relation Extraction (RE) is a crucial task in natural language processing that aims to predict the relationship between two given entities. In recent years, a large majority of approaches utilized syntactic information, particularly dependency trees, to enhance relation extraction by providing superior semantic guidance. Compared with other fields, Chinese texts are more semantic complex, and contain multiple pairs of entities. However, many studies only focus on removing extraneous information from the dependency tree that pertains to a single entity pair. We hypothesis that preserving the semantic and structural interaction between multiple entity pairs in the tree is more conducive to the identification of the current entity pair relationship. Therefore, we propose a new pruning strategy called Multi-entity dependency Tree Pruning and path-Fusion (MTPF), which preserves the ancestor nodes of each entity pair to their lowest common ancestor, as well as the shortest path from that node to each entity. Then we introduce A-GCN as the encoder for the syntax tree obtained above, and the idea of multi-classification sequence as the decoder. Experimental results on two Chinese benchmark datasets, the financial dataset constructed by ourselves and DUIE1.0, demonstrate the effectiveness of our pruning strategy for CRE, where our approach outperforms strong dependency-tree baselines and achieve state-of-the-art results on both datasets.
Dual-Branch Contrastive Learning for Network Representation Learning

Graph Contrastive Learning (GCL) is a self-supervised learning algorithm designed for graph data and has received widespread attention in the field of network representation learning. However, existing GCL-based network representation methods mostly use a single-branch contrastive approach, which makes it difficult to learn deeper semantic relationships and is easily affected by noisy connections during the process of obtaining global structural information embedding. Therefore, this paper proposes a network representation learning method based on a dual-branch contrastive approach. Firstly, the clustering idea is introduced into the process of embedding global structural information, and irrelevant nodes are selected and removed based on the clustering results, effectively reducing the noise in the embedding process. Then, a dual-branch contrastive method, similar to ensemble learning, is proposed, in which the two generated views are compared with the original graph separately, and the joint optimization method is used to continuously update the two views, allowing the model to learn more discriminative feature representations. The proposed method was evaluated on three datasets, Cora, Citeseer, and Pubmed, for node classification and dimensionality reduction visualization experiments. The results show that the proposed method achieved better performance compared to existing baseline models.
Traceable Health Data for Consciously Trained ML Models

Advances in Machine Learning have highlighted the importance of gathering and labeling data, particularly in settings such as healthcare. Issues of ownership, privacy, data provenance, bias in trained models and informed consent, have raised an interest in the ethical use of personal data. We introduce THREAD, a Reference Architecture raised on a decentralized network to gather healthcare data, sign consent, annotate data and obtain credit from it, allowing users to trace the use of their data, and data scientist to trace their provenance, while protecting user privacy. We demonstrate the use of THREAD through an implementation of it, able to gather breast cancer risk factors data to train a machine learning model. The e-wallet implemented on THREAD is used to sign user’s consent to participate in the study. Credit is given according to valid data uploaded and the validation of data from other users. A customized computational notebook is used to import the data consciously gathered by TRHEAD and train ML models. Blockchain is used as a decentralized registry for each operation performed over users data and as a repository of consent letters stored as Non Fungible Tokens (NFT) representing rights of their ownership. Thus, the data consumed can be traced, confirming that they were ethically/consciously trained.
Self-supervised-Enhanced Dual Hierarchical Graph Convolution Network for Social Recommendation

Graph convolution networks (GCNs) have made significant progress in the field of recommendation systems in recent years, and many GCN-based frameworks have applied in social recommendation methods. The essence of social recommendation tasks is modeling user preferences through user social relationships to alleviate the sparsity issue. However, existing GCN-based social recommendation frameworks still have some inherent problems. Firstly, since there are no node attributes available as semantic information in the recommendation task, lightweight graph convolutions that remove feature transformation and non-linear activation function have become widely applied in recommendation task, with the core lies in its message passing mechanism. Social recommendation methods always directly apply existing message passing paradigms, which always have obvious limitations in their message passing mechanisms. Secondly, most existing social recommendation frameworks are limited to pairwise relations and unable to effectively extract implicit inter-graph information. To address these issues, we propose a Self-Supervised-Enhanced Dual Hierarchical Graph Convolution Network (SSHGCN). In this framework, we first propose a LEWB (Link Encoded-Weight Balanced) message passing paradigm applied to graph convolution network to train user and item representations. Then we explicitly model marginal information between user social graph and user-item interaction graph, item knowledge graph and user-item interactions graph by hypergraph. Finally, we construct hierarchical self-supervised signals and unify self-supervised task and recommendation task for joint training. Extensive experiments on real-world datasets demonstrate that our method outperforms competitive methods. Thorough ablation study verifies the rationality of LEWB message passing paradigm and the effectiveness of the hierarchical self-supervised tasks in our framework.
Implication of Technology to Curb Digital Violence Among Women on Social Media Platforms

Gender Violence is one of the most dreaded forms of violence that is inflicted on the basis of gender. Earlier it was considered to be against women but in the current day, gender violence is just not limited to only women. There have been many cases who have been abused, humiliated, and exploited irrespective of their gender. What meant to be so much user-friendly created a pathway of destruction for them in their lives. Some were blackmailed and threatened after being abused and humiliated. Therefore, technology was the only means to come out of this deadly trap and lead a normal life again. Therefore, this paper is an attempt to study the implication of technology and explore ways and means to stop the digital violence which takes place among the users who are students and are much prone to using social media.