Reviewerly/exHarmony huggingface dataset:
PU-Detector: A PU Learning-based Framework for Real Money Trading Detection in MMORPG

Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples.
Explanation Guided Knowledge Distillation for Pre-trained Language Model Compression

Knowledge distillation is widely used in pre-trained language model compression, which can transfer knowledge from a cumbersome model to a lightweight one. Though knowledge distillation based model compression has achieved promising performance, we observe that explanations between the teacher model and the student model are not consistent. We argue that the student model should study not only the predictions of the teacher model but also the internal reasoning process. To this end, we propose Explanation Guided Knowledge Distillation (EGKD) in this article, which utilizes explanations to represent the thinking process and improve knowledge distillation. To obtain explanations in our distillation framework, we select three typical explanation methods rooted in different mechanisms, namely gradient-based , perturbation-based , and feature selection methods. Then, to improve computational efficiency, we propose different optimization strategies to utilize the explanations obtained by these three different explanation methods, which could provide the student model with better learning guidance. Experimental results on GLUE demonstrate that leveraging explanations can improve the performance of the student model. Moreover, our EGKD could also be applied to model compression with different architectures.
DSISA: A New Neural Machine Translation Combining Dependency Weight and Neighbors

Most of the previous neural machine translations (NMT) rely on parallel corpus. Integrating explicitly prior syntactic structure information can improve the neural machine translation. In this article, we propose a Syntax Induced Self-Attention (SISA) which explores the influence of dependence relation between words through the attention mechanism and fine-tunes the attention allocation of the sentence through the obtained dependency weight. We present a new model, Double Syntax Induced Self-Attention (DSISA), which fuses the features extracted by SISA and a compact convolution neural network (CNN). SISA can alleviate long dependency in sentence, while CNN captures the limited context based on neighbors. DSISA utilizes two different neural networks to extract different features for richer semantic representation and replaces the first layer of Transformer encoder. DSISA not only makes use of the global feature of tokens in sentences but also the local feature formed with adjacent tokens. Finally, we perform simulation experiments that verify the performance of the new model on standard corpora.
Active contrastive coding reducing label effort for sensor-based human activity recognition

Human activity recognition (HAR) plays a crucial role in remotely monitoring the health of the elderly. Human annotation is time-consuming and expensive, especially for abstract sensor data. Contrastive learning can extract robust features from weakly annotated data to promote the development of sensor-based HAR. However, current research mainly focuses on the exploration of data augmentation methods and pre-trained models, disregarding the impact of data quality on label effort for fine-tuning. This paper proposes a novel active contrastive coding model that focuses on using an active query strategy to evenly select small, high-quality samples in downstream tasks to complete the update of the pre-trained model. The proposed uncertainty-based balanced query strategy mines the most indistinguishable hard samples according to the data posterior probability in the unlabeled sample pool, and imposes class balance constraints to ensure equilibrium in the labeled sample pool. Extensive experiments have shown that the proposed method consistently outperforms several state-of-the-art baselines on four mainstream HAR benchmark datasets (UCI, WISDM, MotionSense, and USCHAD). With approximately only 10% labeled samples, our method achieves impressive F1-scores of 98.54%, 99.34%, 98.46%, and 87.74%, respectively.
Evolving masked low-rank transformer for long text understanding

Long sequence text processing is time-consuming owing to the ultra-large-scale self-attention computing. Recent advances demonstrate the attention in transformer can be accelerated by redundancy removal, and there are various sparse variants for attention in large sequences are proposed, which leads to state-of-the-art performance on language and vision task. Low-rank method achieve outstanding success in the field of efficient transformer. The dynamic token sparsification is efficiently time-saving and cost-saving, which can be easily extended to prune redundant spans and to yield semantic features. Evolutionary algorithm is attractive for selecting hyperparameter which is of significant importance in effectiveness. Motivated by these works, we propose an efficient transformers model, termed EMLT, to alleviate time and cost without sacrificing the accuracy. EMLT effectively combines strengths of Low-rank transformers, dynamic token sparsification and evolutionary algorithm to ulteriorly cut redundant token and meanwhile maintains the original precision, which can achieve a linear memory and time complexity. We compress transformer in three stages. Firstly, sliding window is validated as local attention to capture fine-grained dependency semantics. After that, low-rank approximation of attention matrix is applied as global attention to store long-range dependency semantics, and aggregated with local attention. On this basis, we consistently prune redundant token in accordance with importance score to further sparse the attention operation. Finally, Evolutionary algorithm is utilized to optimize the hyper-parameters of every layer. The results of comprehensive experiments and analysis show that our method can rival others on accuracy, and outperforms others on efficiency by a large margin in terms of the computational complexity.
Intensive vision-guided network for radiology report generation

Abstract Objective. Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view structure of medical images, such as space-view or channel-view. However, clinicians rely on multi-view imaging information for comprehensive judgment in daily clinical diagnosis. Second, when generating reports, they overlook context reasoning with multi-modal information and focus on pure textual optimization utilizing retrieval-based methods. We aim to address these two issues by proposing a model that better simulates clinicians perspectives and generates more accurate reports. Approach. Given the above limitation in feature extraction, we propose a globally-intensive attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception. GIA aims to learn three types of vision perception: depth view, space view, and pixel view. On the other hand, to address the above problem in report generation, we explore how to involve multi-modal signals to generate precisely matched reports, i.e. how to integrate previously predicted words with region-aware visual content in next word prediction. Specifically, we design a visual knowledge-guided decoder (VKGD), which can adaptively consider how much the model needs to rely on visual information and previously predicted text to assist next word prediction. Hence, our final intensive vision-guided network framework includes a GIA-guided visual encoder and the VKGD. Main results. Experiments on two commonly-used datasets IU X-RAY and MIMIC-CXR demonstrate the superior ability of our method compared with other state-of-the-art approaches. Significance. Our model explores the potential of simulating clinicians perspectives and automatically generates more accurate reports, which promotes the exploration of medical automation and intelligence.
Strengthening Cooperative Consensus in Multi-Robot Confrontation

Multi-agent reinforcement learning (MARL) has proven effective in training multi-robot confrontation, such as StarCraft and robot soccer games. However, the current joint action policies utilized in MARL have been unsuccessful in recognizing and preventing actions that often lead to failures on our side. This exacerbates the cooperation dilemma, ultimately resulting in our agents acting independently and being defeated individually by their opponents. To tackle this challenge, we propose a novel joint action policy, referred to as the consensus action policy (CAP). Specifically, CAP records the number of times each joint action has caused our side to fail in the past and computes a cooperation tendency, which is integrated with each agent’s Q -value and Nash bargaining solution to determine a joint action. The cooperation tendency promotes team cooperation by selecting joint actions that have a high tendency of cooperation and avoiding actions that may lead to team failure. Moreover, the proposed CAP policy can be extended to partially observable scenarios by combining it with Deep Q network or actor-critic–based methods. We conducted extensive experiments to compare the proposed method with seven existing joint action policies, including four commonly used methods and three state-of-the-art methods, in terms of episode rewards, winning rates, and other metrics. Our results demonstrate that this approach holds great promise for multi-robot confrontation scenarios.
TSAGNN: Temporal link predict method based on two stream adaptive graph neural network

Temporal link prediction based on graph neural networks has become a hot spot in the field of complex networks. To solve the problems of the existing temporal link prediction methods based on graph neural networks do not consider the future time-domain features and spatial-domain features are limited used, this paper proposes a novel temporal link prediction method based on two streams adaptive graph neural networks. Firstly, the network topology features are extracted from the micro, meso, and middle perspectives. Combined with the adaptive mechanism of convolution and self-attention, the preprocessing of the feature extraction is more effective; Secondly, an extended bi-directional long short-term memory network is proposed, which uses graph convolution to process topological features, and recursively learns the state vectors of the target snapshot by using the future time-domain information and the past historical information; Thirdly, the location coding is replaced by the time-coding for the transformer mechanism, so that past information and future information can be learned from each other, and the time-domain information of the network can be further mined; Finally, a novel two-stream network framework is proposed, which combines the processing results of point features and edge features. The experimental results on 9 data sets show that the proposed method has a better prediction effect and better robustness than the classical graph neural network methods.
Digital mammogram based robust feature extraction and selection for effective breast cancer classification in earlier stage

Breast cancer poses a significant health risk for women, demanding early detection to mitigate its mortality impact. Leveraging the power of Deep Learning (DL) in medical imaging, this paper introduces a hybrid model that integrates YOLOv7 and Half UNet for feature extraction. YOLOv7 identifies and localizes potential cancerous regions, while Half UNet focuses on extracting pertinent features with its encoder-decoder structure. The fusion of these discriminative features, coupled with feature selection via Coati Optimization, ensures a comprehensive and optimized dataset. The selected features then feed into the CatBoost classification algorithm, refining parameters iteratively for precise predictions and minimizing the loss function. Evaluation metrics, including precision, recall, specificity, and accuracy, demonstrate the model’s superior performance. Notably, the proposed model surpasses existing methods in early-stage breast cancer detection. Beyond numerical metrics, its significance lies in the potential to positively impact patient outcomes and increase survival rates. By amalgamating cutting-edge DL techniques, the model excels in identifying intricate patterns crucial for early cancer detection. The efficient fusion of YOLOv7 and Half UNet, coupled with feature optimization through Coati Optimization, sets this model apart. This research contributes to the evolving landscape of medical imaging and DL applications, emphasizing the potential for enhanced breast cancer diagnosis and improved patient prognoses.
Exploiting classifier inter-level features for efficient out-of-distribution detection

Deep learning approaches have achieved state-of-the-art performance in a wide range of applications. Most often, however, it is falsely assumed that samples at inference follow a similar distribution as the training data. This assumption impairs models' ability to handle Out-of-Distribution (OOD) data during deployment. While several OOD detection approaches mostly focus on outputs of the last layer, we propose a novel mechanism that exploits features extracted from intermediate layers of a deep classifier. Specifically, we train an off-the-shelf auxiliary network using features of early layers to learn distinctive representations that improve OOD detection. The proposed network can be appended to any classification model without imposing any modification to its original architecture. Additionally, the mechanism does not require access to OOD data during training. We evaluate the performance of the mechanism on a variety of backbone architectures and datasets for near-OOD and far-OOD scenarios. The results demonstrate improvements in OOD detection compared to other state-of-the-art approaches. In particular, our proposed mechanism improves AUROC by 14.2% and 8.3% in comparison to the strong OOD baseline method, and by 3.2% and 3.9% in comparison to the second-best performing approach, on CIFAR-10 and CIFAR-100 datasets respectively.
Protocol choice and iteration for the free cornering

We extend the free cornering of a symmetric monoidal category, a double categorical model of concurrent interaction, to support branching communication protocols and iterated communication protocols. We validate our constructions by showing that they inherit significant categorical structure from the free cornering, including that they form monoidal double categories. We also establish some elementary properties of the novel structure they contain. Further, we give a model of the free cornering in terms of strong functors and strong natural transformations, inspired by the literature on computational effects.
A no-reference image quality assessment model based on neighborhood component analysis and Gaussian process

In this paper, a human visual system (HVS) based no-reference image quality assessment model (NR-IQA) is proposed. This is an objective NR-IQA model that uses perceptual features such as structure and orientation to compute the loss of naturalness in the image. An adaptive feature extraction process is modeled to capture the underlying significant features of the images along with the noise. Moreover, the optimal subset of features is derived using the neighborhood component analysis and further used as inputs to the Gaussian process for regression. The various experimentations are carried out on LIVE, TID2008, and TID2013 databases to test the effectiveness of the proposed approach. It is observed that the presented model exhibits a competitive performance in comparison with the existing IQA approaches. Moreover, the computation complexity and run-time show the effectuality of the proposed approach. Further, the experiments show that the predicted score matches with human perceptions. Thus, the proposed model is more accurate, less complex, independent of distortions, and well-suited for real-time applications.
Identification of the Original Images

In social networks, people usually communicate online by posting and passing images, people will find that there are many similar or even visually identical images. When the images determine whether the published information is true or false, original images became crucial, and at the same time, deep learning excelled in image identification. Combining the original image identification and deep learning methods, this paper proposes the original image identification method based on the deep network models, aiming to use the deep network models to identify the original images between the original images and a bunch of variant images of the original images, and obtain satisfactory results. In this way, through the trained network models, the original images can be identified and can help the viewers to determine the correctness of the information. Experimental results verify the feasibility, effectiveness and security of the proposed method.
An efficient two-stage evolutionary algorithm for multi-robot task allocation in nuclear accident rescue scenario

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem’s characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue.
Multi-objective cost-aware bag-of-tasks scheduling optimization model for IoT applications running on heterogeneous fog environment

Recently introduced by CISCO, the fog computing paradigm extends cloud-based computing services near the Internet of Things (IoT) devices to serve their applications, aiming to increase performance. This new computing paradigm tries to meet the requirements of emerging IoT applications, which mostly are geographically distributed and location-aware, low-cost, and delay-sensitive. One of the frequently applied applications is a bag-of-tasks (BoT) of IoT applications, each of which includes several independent tasks. However, inefficient task scheduling approaches for parallel executions may degrade the delivered quality of service (QoS) to users and also increase the providers’ total cost of ownership (TCO). The reliable execution commensurate with users’ service level agreement (SLA) and affordable services tailored to their budget at the same time is favorable for both users and fog service providers because the highly reliable execution is a reputational metric that increases the users’ trust and adherence. To this end, this paper formulates the task scheduling for the execution of BoT in IoT applications to a multi-objective optimization problem with execution time, costs and reliability perspectives. To measure total costs, both the execution cost and monetary cost models are presented. To measure the resource reliability, the new scheduling failure factor (SFF) metric is also presented, which is a kind of cost parameter. To solve this combinatorial NP-Hard problem, a multi-objective cost-aware discrete grey wolf optimization-based algorithm (MoDGWA) is proposed. To verify the performance of MoDGWA, diverse scenarios were conducted and the performance of the proposed algorithm was compared against other state-of-the-arts. The extensive simulation results prove the average dominance of MoDGWA, and it has 0.55 %, 7.28 %, 10.20 %, and 45.83 % reduction against other state-of-the-art in terms of makespan, ToC, TSFF, and the cost score respectively. Note that, the implementation of the MoDGWA is available on https://github.com/mirsaeid123/Mirsaeid.