Anonymous submissions inspire people to speak up since hiding their identities can protect them from negative influence by their own words. However, the abuse of anonymity may bring harassment to those public submission callers. Existing works only handle DoS attacks or block harassment senders in an active manner, which behave poorly in the early prevention of uncharacterized harassment. In this paper, we propose MsgFliter, a sender-anonymous messaging system with proactive anti-harassment mechanism. Our core idea is to prevent unanswered senders from sending messages continually while keeping their identities, messages, and sender types secret. To meet the functionality and security requirements of MsgFliter, we propose the Anti-Harassment Anonymous Authentication (AHAA) protocol. We associate messages from the same sender through linkable tags and invalidate linkability when a message is replied to. To achieve session indistinguishability, we further combine the proposed anonymous authentication with zero-knowledge proofs of disjunctive relations. We implement MsgFliter and compare its performance with related solutions. Experimental results show that our solution is practicable.

Building large-scale generalist pre-trained models for many tasks is becoming an emerging and potential direction in reinforcement learning (RL).Research such as Gato and Multi-Game Decision Transformer have displayed outstanding performance and generalization capabilities on many games and domains.However, there exists a research blank about developing highly capable and generalist models in multi-agent RL (MARL), which can substantially accelerate progress toward general AI.To fill this gap, we propose Multi-Agent multi-Game ENtity TrAnsformer (MA-GENTA) from the entity perspective as orthogonal research to previous time-sequential modeling.Specifically, to deal with different state/observation spaces in different games, we analogize games as languages by aligning one single game to one single language, thus training different "tokenizers" and a shared transformer for various games.The feature inputs are split according to different entities and tokenized in the same continuous space.Then, two types of transformer-based models are proposed as permutationinvariant architectures to deal with various numbers of entities and capture the attention of different entities.MAGENTA is trained on

Existing game AI research mainly focuses on enhancing agents' abilities to win games, but this does not inherently make humans have a better experience when collaborating with these agents. For example, agents may dominate the collaboration and exhibit unintended or detrimental behaviors, leading to poor experiences for their human partners. In other words, most game AI agents are modeled in a "self-centered" manner. In this paper, we propose a "human-centered" modeling scheme for collaborative agents that aims to enhance the experience of humans. Specifically, we model the experience of humans as the goals they expect to achieve during the task. We expect that agents should learn to enhance the extent to which humans achieve these goals while maintaining agents' original abilities (e.g., winning games). To achieve this, we propose the Reinforcement Learning from Human Gain (RLHG) approach. The RLHG approach introduces a "baseline", which corresponds to the extent to which humans primitively achieve their goals, and encourages agents to learn behaviors that can effectively enhance humans in achieving their goals better. We evaluate the RLHG agent in the popular Multi-player Online Battle Arena (MOBA) game, Honor of Kings, by conducting real-world human-agent tests. Both objective performance and subjective preference results show that the RLHG agent provides participants better gaming experience.

Electronic auctions, popular in the digital era, raise great privacy concerns that may impact participant interests. However, traditional privacy-preserving auction systems fall short in facilitating seller autonomy, particularly in identifying and excluding previously mischievous anonymous bidders. In this paper, we propose MaskAuct, a seller-autonomous auction system with the privacy of bidder identity and bidding price. To enable seller autonomy without compromising bidder privacy, we present a new cryptographic primitive, called Zero-Knowledge Blacklistable Group Signature (ZKBGS), which can invalidate signatures from users in the blacklist without opening user identity. We construct MaskAuct from fully homomorphic encryption and ZKBGS, and introduce the distributed privacy server provider to address the collusion problem. The experimental results show ZKBGS has a smaller signature size (8320 bytes) and running time (635 ms for signing and 24 ms for verification) than the linkable ring signature, even when the length of the blacklist is$2^{9}$. In contrast to the sealed-bid auction scheme SEAL, MaskAuct provides better communication complexity, and is$27\times $faster on bidder computation.

Facial expression recognition (FER) based on the convolutional neural network (CNN) in the wild have numerous challenges. For instance, the complexity of the network model makes FER tasks difficult to deploy on portable devices. Some approaches design lightweight networks to reduce the model size, while the intrinsic imbalance of the existing facial emotion datasets is still ignored. In order to overcome the above problems, the lightweight CNN based on sample equalization method for FER is designed to reduce the network parameters sharply while maintaining the identification accuracy. Specifically, to reduce the number of network parameters, a lightweight network framework (SDNet) is designed with separable convolution layers and dense blocks, which can significantly reduce network parameters. Second, the adaptive class weights are proposed to solve the imbalance of sample numbers. Moreover, a resist overfitting (RO) loss function is proposed to improve the classification accuracy. Extensive experiments are conducted on lab-controlled datasets (CK+, Oulu-CASIA) and in-the-wild datasets (FER2013, SFEW). Experimental results show that our method is superior to several state-of-the-art FER methods.

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on the Honor of Kings, one of the world’s most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning. It is a multi-agent problem with one agent competing against its opponent; and it requires the generalization ability as it has diverse targets to control and diverse opponents to compete with. We describe the observation, action, and reward specifications for the Honor of Kings domain and provide an open-source Python-based interface for communicating with the game engine. We provide twenty target heroes with a variety of tasks in Honor of Kings Arena and present initial baseline results for RL-based methods with feasible computing resources. Finally, we showcase the generalization challenges imposed by Honor of Kings Arena and possible remedies to the challenges. All of the software, including the environment-class, are publicly available.

We present JueWu-SL, the first supervised-learning-based artificial intelligence (AI) program that achieves human-level performance in playing multiplayer online battle arena (MOBA) games. Unlike prior attempts, we integrate the macro-strategy and the micromanagement of MOBA-game-playing into neural networks in a supervised and end-to-end manner. Tested on Honor of Kings, the most popular MOBA at present, our AI performs competitively at the level of High King players in standard 5v5 games.