Reviewerly/exHarmony huggingface dataset:
PU GNN: Chargeback Fraud Detection in P2E MMORPGs via Graph Attention Networks with Imbalanced PU Labels

The recent advent of play-to-earn (P2E) systems in massively multiplayer online role-playing games (MMORPGs) has made in-game goods interchangeable with real-world values more than ever before. The goods in the P2E MMORPGs can be directly exchanged with cryptocurrencies such as Bitcoin, Ethereum, or Klaytn via blockchain networks. Unlike traditional in-game goods, once they had been written to the blockchains, P2E goods cannot be restored by the game operation teams even with chargeback fraud such as payment fraud, cancellation, or refund. To tackle the problem, we propose a novel chargeback fraud prediction method, PU GNN, which leverages graph attention networks with PU loss to capture both the players’ in-game behavior with P2E token transaction patterns. With the adoption of modified GraphSMOTE, the proposed model handles the imbalanced distribution of labels in chargeback fraud datasets. The conducted experiments on three real-world P2E MMORPG datasets demonstrate that PU GNN achieves superior performances over previously suggested methods.
Deep Serial Number: Computational Watermark for DNN Intellectual Property Protection

In this paper, we present DSN (Deep Serial Number), a simple yet effective watermarking algorithm designed specifically for deep neural networks (DNNs). Unlike traditional methods that incorporate identification signals into DNNs, our approach explores a novel Intellectual Property (IP) protection mechanism for DNNs, effectively thwarting adversaries from using stolen networks. Inspired by the success of serial numbers in safeguarding conventional software IP, we propose the first implementation of serial number embedding within DNNs. To achieve this, DSN is integrated into a knowledge distillation framework, in which a private teacher DNN is initially trained. Subsequently, its knowledge is distilled and imparted to a series of customized student DNNs. Each customer DNN functions correctly only upon input of a valid serial number. Experimental results across various applications demonstrate DSN’s efficacy in preventing unauthorized usage without compromising the original DNN performance. The experiments further show that DSN is resistant to different categories of watermark attacks.
OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction

A large-scale industrial recommendation platform typically consists of multiple associated scenarios, requiring a unified click-through rate (CTR) prediction model to serve them simultaneously. Existing approaches for multi-scenario CTR prediction generally consist of two main modules: i) a scenario-aware learning module that learns a set of multi-functional representations with scenario-shared and scenario-specific information from input features, and ii) a scenario-specific prediction module that serves each scenario based on these representations. However, most of these approaches primarily focus on improving the former module and neglect the latter module. This can result in challenges such as increased model parameter size, training difficulty, and performance bottlenecks for each scenario. To address these issues, we propose a novel framework called OptMSM (Optimizing Multi-Scenario Modeling). First, we introduce a simplified yet effective scenario-enhanced learning module to alleviate the aforementioned challenges. Specifically, we partition the input features into scenario-specific and scenario-shared features, which are mapped to specific information embedding encodings and a set of shared information embeddings, respectively. By imposing an orthogonality constraint on the shared information embeddings to facilitate the disentanglement of shared information corresponding to each scenario, we combine them with the specific information embeddings to obtain multi-functional representations. Second, we introduce a scenario-specific hypernetwork in the scenario-specific prediction module to capture interactions within each scenario more effectively, thereby alleviating the performance bottlenecks. Finally, we conduct extensive offline experiments and an online A/B test to demonstrate the effectiveness of OptMSM.
BCAD: An Interpretable Anomaly Transaction Detection System Based on Behavior Consistency

In the era of digital payment, abnormal behaviors such as fraud pose a huge threat to E-commerce platforms. Traditional anti-fraud approaches usually apply supervised learning which requires sophisticated knowledge extraction and does not adapt to evolving anomalous behaviors. In recent years, unsupervised learning methods has been widely applied to anomaly detection. However, they still suffer from a serious shortcoming, that they judge anomalies based on the global distribution while ignoring the user’s own historical information. In this paper, we propose a novel problem of unsupervised anomaly transaction detection focusing on the individual level. To tackle this problem, we first derive behavior consistency hypothesis based on data exploration. Then based on this assumption, we propose a new framework named Behavior Consistency based Anomaly Detection (BCAD). Specifically, BCAD learns representations for the target behavior and the history behavior preferences respectively by contrastive learning, and then measure the similarity between them to identify anomaly transactions. Besides, to disentangle the behavior representation into several attributes, we design an attribute gate module which can extract high-level user preferences from historical behaviors. Overall, BCAD can not only detect whether a target behavior is abnormal, even if the fraudulent pattern never appeared before, but also give an interpretation from the perspective of preference attributes. Extensive experiments on the real-world business dataset demonstrate that BCAD can detect abnormal behaviors effectively and provide insightful results for human beings.
Prototype-Guided Counterfactual Explanations via Variational Auto-encoder for Recommendation

Counterfactual reasoning has recently achieved impressive performance in the explainability of recommendation. However, existing counterfactual explainable methods ignore the realism of explanations and consider only the sparsity and proximity of explanations. Moreover, the huge counterfactuals space causes a time-consuming search process. In this study, we propose Prototype-Guided Counterfactual Explanations (PGCE), a novel counterfactual explainable recommendation framework to overcome the above issues. At its core, PGCE leverages a variational auto-encoder generative model to constrain the modification of features to generate counterfactual instances that are consistent with the distribution of real data. Meanwhile, we constructed a contrastive prototype for each user in a low-dimensional latent space, which can guide the search direction towards the optimal candidate instance space, thus, speed up the search process. For evaluation, we compared our method with several state-of-the-art model-intrinsic methods on three real-world datasets, in addition to the latest counterfactual reasoning-based method. Extensive experiments show that our model is not only able to efficiently generate realistic counterfactual explanations but also achieve state-of-the-art performance on other popular explainability evaluation metrics.
Future Augmentation with Self-distillation in Recommendation

Sequential recommendation (SR) aims to provide appropriate items a user will click according to the user’s historical behavior sequence. Conventional SR models are trained under the next item prediction task, and thus should deal with two challenges, including the data sparsity of user feedback and the variability and irregularity of user behaviors. Different from natural language sequences in NLP, user behavior sequences in recommendation are much more personalized, irregular, and unordered. Therefore, the current user preferences extracted from user historical behaviors may also have correlations with the next-k (i.e., future clicked) items besides the classical next-1 (i.e., current clicked) item to be predicted. Inspired by this phenomenon, we propose a novel Future augmentation with self-distillation in recommendation (FASRec). It considers future clicked items as augmented positive signals of the current clicks in training, which addresses both data sparsity and behavior irregularity and variability issues. To denoise these augmented future clicks, we further adopt a self-distillation module with the exponential moving average strategy, considering soft labels of self-distillation as confidence for more accurate augmentations. In experiments, FASRec achieves significant and consistent improvements on both offline and online evaluations with different base SR models, confirming its effectiveness and universality. FASRec has been deployed on a widely-used recommendation feed in Tencent. The source codes are in https://github.com/FASRec/FASRec .
Advancing Fraud Detection Systems Through Online Learning

The rapid increase in digital transactions has led to a consequential surge in financial fraud, requiring an automatic way of defending effectively from such a threat. The past few years experienced a rise in the design and use by financial institutions of different machine learning-based fraud detection systems. However, these solutions may suffer severe drawbacks if a malevolent adversary adapts their behavior over time, making the selection of the existing fraud detectors difficult. In this paper, we study the application of online learning techniques to respond effectively to adaptive attackers. More specifically, the proposed approach takes as input a set of classifiers employed for fraud detection tasks and selects, based on the performances experienced in the past, the one to apply to analyze the next transaction. The use of an online learning approach guarantees to keep at a pace the loss due to the adaptive behavior of the attacker over a given learning period. To validate our methodology, we perform an extensive experimental evaluation using real-world banking data augmented with distinct fraudulent campaigns based on real-world attackers’ models. Our results demonstrate that the proposed approach allows prompt updates to detection models as new patterns and behaviors are occurring, leading to a more robust and effective fraud detection system.
LtrGCN: Large-Scale Graph Convolutional Networks-Based Learning to Rank for Web Search

While traditional Learning to Rank (LTR) models use query-webpage pairs to perform regression tasks to predict the ranking scores, they usually fail to capture the structure of interactions between queries and webpages over an extremely large bipartite graph. In recent years, Graph Convolutional Neural Networks (GCNs) have demonstrated their unique advantages in link prediction over bipartite graphs and have been successfully used for user-item recommendations. However, it is still difficult to scale-up GCNs for web search, due to the (1) extreme sparsity of links in query-webpage bipartite graphs caused by the expense of ranking scores annotation and (2) imbalance between queries (billions) and webpages (trillions) for web-scale search as well as the imbalance in annotations. In this work, we introduce the Q-subgraph and W-subgraph to represent every query and webpage with the structure of interaction preserved, and then propose LtrGCN—an LTR pipeline that samples Q-subgraphs and W-subgraphs from all query-webpage pairs, learns to extract features from Q-subgraphs and W-subgraphs, and predict ranking scores in an end-to-end manner. We carried out extensive experiments to evaluate LtrGCN using two real-world datasets and online experiments based on the A/B test at a large-scale search engine. The offline results show that LtrGCN could achieve $$\varDelta $$ NDCG $$_{5}$$ = 2.89%–3.97% compared to baselines. We deploy LtrGCN with realistic traffic at a large-scale search engine, where we can still observe significant improvement. LtrGCN performs consistently in both offline and online experiments.
The impact of service robots on customer satisfaction online ratings: The moderating effects of rapport and contextual review factors

Abstract Recent research has established a positive relationship between the use of service robots powered by artificial intelligence in hospitality firms and customer satisfaction online ratings, a particularly important form of electronic word of mouth. However, it is not clear if and how this relationship is augmented or diminished by moderating factors. In this study, we examined four potential moderators by using machine learning and natural language processing techniques to analyze 20,166 online reviews of hotels that had implemented service robots. We had four key findings. First, a positive service robot‐satisfaction rating relationship was further enhanced by improved customer‐service robot rapport during the service encounter. Second, higher customer effort focused on service robots in a review reduced the service robot‐satisfaction rating relationship. Third, posting reviews using a mobile device (vs. other devices) showed higher satisfaction ratings. Finally, customers' prior experience in writing online reviews was unrelated to the service robot‐satisfaction rating relationship. Taken together, these results suggest that service robots should be designed to be interactive and encourage customers to build rapport, for example, by service robots engaging in conversational flows. Moreover, customers should be nudged to use their mobile devices to post timely reviews on their positive human–robot interactions.
TriangleNet: Edge Prior Augmented Network for Semantic Segmentation through Cross-Task Consistency

This paper addresses the task of semantic segmentation in computer vision, aiming to achieve precise pixel-wise classification. We investigate the joint training of models for semantic edge detection and semantic segmentation, which has shown a promise. However, implicit cross-task consistency learning in multitask networks is limited. To address this, we propose a novel “decoupled cross-task consistency loss” that explicitly enhances cross-task consistency. Our semantic segmentation network, TriangleNet, achieves a substantial 2.88% improvement over the Baseline in mean Intersection over Union (mIoU) on the Cityscapes test set. Notably, TriangleNet operates at 77.4% mIoU/46.2 FPS on Cityscapes, showcasing real-time inference capabilities at full resolution. With multiscale inference, performance is further enhanced to 77.8%. Furthermore, TriangleNet consistently outperforms the Baseline on the FloodNet dataset, demonstrating its robust generalization capabilities. The proposed method underscores the significance of multitask learning and explicit cross-task consistency enhancement for advancing semantic segmentation and highlights the potential of multitasking in real-time semantic segmentation.
A one-time-pad-like chaotic image encryption scheme using data steganography

One-time-pad-like image ciphers using chaos and hash functions have achieved outstanding performance in terms of security strength and efficiency, but they fail to consider the problem of hash value distribution, limiting their practical application significantly. Moreover, most existing algorithms are designed based on independent permutation and sequential diffusion operations, which may lead them to end up being broken by chosen-plaintext attacks. In this paper, we propose a one-time-pad-like chaotic image cipher using data steganography. The whole scheme consists of an encryption process for the plain-image and an embedding process for the encrypted image. Different from the permutation–diffusion type image ciphers, we remove the permutation stage and use only a plaintext-dependent non-sequential diffusion operation. The encryption algorithm exploits the security potential of the encryption order and can achieve a satisfactory level of security with only once scanning of the entire image. During the embedding process, a reversible data-hiding technique is adopted to embed the hash value of the plain-image into the cipher-image so that it can be transmitted together to the receiver, overcoming the practical limitation encountered by the existing one-time-pad-like ciphers. Experimental results indicate that the proposed algorithm is secure against all common attacks and outperforms state-of-the-art approaches in terms of both security and efficiency.
Assessing data phishing risks associated with unencrypted apps on smartphones with non-parametric test and random forest model: Insights from Kuwait phishing scam calls

The outbreak of covid-19 has helped M-commerce to strengthen its roots and helped to increase the number of customers in different industries. Likewise, Electronics product companies also developed M-commerce platforms such as mobile applications (Apps) regardless of their website to sell their products based on customers’ interests and likes. In order to create personalized and targeted marketing strategies, companies collect data from users of M-commerce platforms. For this, they have taken the data from the Handheld device users who installed the companies’ applications with or without the consent of the users which raises ethical concerns related to privacy and security. Unlike other studies this research explores the reason of increased data phishing cases in Kuwait from 2020 and the findings are supported by statistics using machine learning and python language. This paper examines the ethical implications-data phishing chances from users if M-commerce platforms of electronics Apps which are data unencrypted installed in the smartphones. The study employs a mixed-methods approach that includes semi-structured interviews with industry experts and customers, Checking the M-commerce Data privacy details, and analysis of relevant literature. Using the study's survey findings, the hypothesis was tested and a machine learning model was developed that predicts the likelihood of data theft when two specific apps are installed on a smartphone. The research pointed out that the null hypothesis is true and there is approximately 82% of chance if the smart phone users installs data unencrypted apps in the device.
Dynamic feature splicing for few-shot rare disease diagnosis

Annotated images for rare disease diagnosis are extremely hard to collect. Therefore, identifying rare diseases under a few-shot learning (FSL) setting is significant. Existing FSL methods transfer useful and global knowledge from base classes with abundant training samples to enrich features of novel classes with few training samples, but still face difficulties when being applied to medical images due to the complex lesion characteristics and large intra-class variance. In this paper, we propose a dynamic feature splicing (DNFS) framework for few-shot rare disease diagnosis. Under DNFS, both low-level features (i.e., the output of three convolutional blocks) and high-level features (i.e., the output of the last fully connected layer) of novel classes are dynamically enriched. We construct the position coherent DNFS (P-DNFS) module to perform low-level feature splicing, where a lesion-oriented Transformer is designed to detect lesion regions. Thus, novel-class channels are replaced by similar base-class channels within the detected lesion regions to achieve disease-related feature enrichment. We also devise a semantic coherent DNFS (S-DNFS) module to perform high-level feature splicing. It explores cross-image channel relations and selects base-class channels with semantic consistency for explicit knowledge transfer. Both low-level and high-level feature splicings are performed dynamically and iteratively. Consequently, abundant spliced features are generated for disease diagnosis, leading to more accurate decision boundary and improved diagnosis performance. Extensive experiments have been conducted on three medical image classification datasets. Our results suggest that the proposed DNFS achieves superior performance against state-of-the-art approaches.
High-performance one-stage detector for SiC crystal defects based on convolutional neural network

SiC (silicon carbide), as the most important third-generation semiconductor material, has huge market prospects in numerous fields, such as 5G base stations and new energy vehicle charging piles. The identification of SiC crystal defects is essential for improving crystal quality. Currently, this study relies mainly on artificial methods to identify defects, which have significant limitations in terms of accuracy and efficiency. Thus, to quickly detect and classify different SiC crystal defects in complex scenarios, a convolutional neural network-based SiC crystal defect detection (SCDD-Net) model is presented for the first time in this study. SCDD-Net uses an improved online convolutional re-parameterization method that can effectively extract the features of SiC crystal defects and decrease the large training overhead. We devised a new spatial pyramid pooling module that, when combined with the global context block, enables the fast fusion of high-level crystal defects and underlying features. We also designed an anchor-based decoupling detection head network to identify smaller crystal defects. By collecting and processing more than 5300 high-quality microscopic images, we built a fine-grained labeled SiC crystal defect image dataset, SiC-Crystal-5K, for the first time. The experimental results show that the SCDD-Net has excellent detection accuracy compared to other state-of-the-art models. The mean average precision for high-resolution SiC crystal defect identification reached 99.53%, corresponding to a single-image detection speed of 102 fps. In addition to crystal-defect detection, the SCDD-Net model can be used as a general-purpose detector in a wide range of scenarios.
Graph transformer-based self-adaptive malicious relation filtering for fraudulent comments detection in social network

In recent years, Graph Neural Networks (GNNs) have proven to be effective in detecting fraud within social networks by gathering information from neighboring nodes to predict fraudulent actions. However, the continuous evolution and camouflaging tactics of fraudsters, such as establishing malicious connections with legitimate users and mimicking their behaviors, can often elude GNN-based detection methods. To tackle this issue, we propose a multi-order moments graph transformer, named MMGT, to effectively learn node representations through an attention mechanism that captures information of different orders of moments. Building upon this foundation, we further introduce a self-adaptive malicious relation filtering model for fraud detection, denoted as SFGT. Initially, we form a multi-relational graph that encapsulates complex relations within a given social network and derive node representations by accumulating neighbor node and edge data based on an enhanced graph transformer. Subsequently, a threshold-based malicious relation filter mechanism is proposed to eliminate malicious links by assessing the distance between nodes. Furthermore, an adaptive threshold learning policy is developed to bolster the model’s performance and its ability to generalize. Finally, extensive experiments are carried out on two public datasets, Amazon and YelpChi, which underscores the effectiveness of our proposed model. The experimental results indicate that our model achieves state-of-the-art performance.