Reviewerly/exHarmony huggingface dataset:
HiveMind: Learning to Play the Cooperative Chess Variant Bughouse with DNNs and MCTS

In 2017, the AlphaZero algorithm achieved superhuman performance in chess, outperforming the then-reigning chess engine Stockfish 8. AlphaZero has since been applied to other variants of chess, such as Crazyhouse, with similarly impressive results. However, limited work has been done on the chess variant Bughouse, which has both cooperative and real-time aspects, as well as a far higher game tree complexity than chess. In this paper, we present HiveMind, a neural network Bughouse engine that focuses on cooperation and decision-making. We trained HiveMind via supervised learning on human Bughouse games. We then used the AlphaZero algorithm by incorporating domain knowledge, using clock times to determine the optimal turn sequence, to perform a tree search over both boards. This two-board search incorporated all aspects of Bughouse, including being time aware and capable of cross-board coordination without heuristics. Finally, we evaluated the strength of HiveMind by playing matches with different time settings against Fairy-Stockfish, the current state-of-the-art alpha-beta Bughouse engine. HiveMind convincingly defeated Fairy-Stockfish achieving a win rate of over 95% with a search time of 2 seconds, showing significantly better scaling.
Representing Player Behaviour via Graph Embedding Techniques: A Case Study in Dota 2

We explore the use of graph embedding techniques to represent the player behaviour that is expressed in the logs of video games. While such logs hold data that could be useful for personalization, the data is often poorly structured for use with Artificial Intelligence systems and its dimensionality is often high. By using a graph to structure the logs and applying embedding techniques to reduce their dimensionality, a compact vector representation can be obtained that preserves some of their semantics. To explore the potential value of this approach, we obtained gameplay logs from over 3000 matches of Defense of the Ancients 2 (Dota 2) and compared 13 parameter variations of three different embedding techniques: NODE2VEC, LINE, and TGN. Our analysis considers the effects of embedded vector size, dataset size, a step size used for updating vectors as a game proceeds, and different types of player interaction. The results show that NODE2VEC outperforms the other techniques on 7 of the 13 variations that we tested, and that removing one type of player interaction can make it easier to predict the others.
Joint Level Generation and Translation Using Gameplay Videos

Procedural Content Generation via Machine Learning (PCGML) faces a significant hurdle that sets it apart from other fields, such as image or text generation, which is limited annotated data. Many existing methods for procedural level generation via machine learning require a secondary representation besides level images. However, the current methods for obtaining such representations are laborious and time-consuming, which contributes to this problem. In this work, we aim to address this problem by utilizing gameplay videos of two human-annotated games to develop a novel multi-tail framework that learns to perform simultaneous level translation and generation. The translation tail of our framework can convert gameplay video frames to an equivalent secondary representation, while its generation tail can produce novel level segments. Evaluation results and comparisons between our framework and baselines suggest that combining the level generation and translation tasks can lead to an overall improved performance regarding both tasks. This represents a possible solution to limited annotated level data, and we demonstrate the potential for future versions to generalize to unseen games.
Automatic Bug Detection in Games using LSTM Networks

We introduced a new framework to detect perceptual bugs using a Long Short-Term Memory (LSTM) network, which detects bugs in video games as anomalies. The detected buggy frames are then clustered to determine the category of the occurred bug. The framework was evaluated on two First Person Shooter (FPS) games. Results show the effectiveness of the framework.
Baba is You is Undecidable

We establish the undecidability of 2019 puzzle game Baba is You through a reduction from the Post correspondence problem. In particular, we consider a restricted form of the Post correspondence problem introduced by Neary [9] that is limited to five pairs of words. Baba is You is an award winning tile-based game in which the player can reprogram the game’s mechanisms by pushing blocks that spell out the rules. We achieve undecidability through a generalization of the size of the playfield in the horizontal direction, adding a "hallway" to one side of the level. The computational possibilities of Baba is You has been claimed several times online through demonstrations of simulated Turing machines and of Conway’s Game of Life, though this contribution appears to be the first formal proof of the undecidability of the game.
Extend Wave Function Collapse Algorithm to Large-Scale Content Generation

Wave Function Collapse (WFC) is a widely used tile-based algorithm in procedural content generation, including textures, objects, and scenes. However, the current WFC algorithm and related research lack the ability to generate commercialized large-scale or infinite content due to constraint conflict and time complexity costs. This paper proposes a Nested WFC (N-WFC) algorithm framework to reduce time complexity. To avoid conflict and backtracking problems, we offer a complete and sub-complete tileset preparation strategy, which requires only a small number of tiles to generate aperiodic and deterministic infinite content. We also introduce the weight-brush system that combines N-WFC and sub-complete tileset, proving its suitability for game design. Our contribution addresses WFC’s challenge in massive content generation and provides a theoretical basis for implementing concrete games.
End-to-End Tibetan-Chinese Speech Translation Based on Multi-task and Multi-level Pre-training

End-to-end speech translation is a technology capable of translating speech in the source language directly into text in the target language, and it can mitigate the propagation errors that cascade system can introduce. Nevertheless, compared to relatively mature speech recognition and machine translation, the present challenge of end-to-end speech translation is the lack of parallel corpus data for low-resource minority languages. This creates obstacles in building efficient speech translation models. In this paper, we propose a model for end-to-end Tibetan-Chinese speech translation based on the multi-task and multi-level pre-training strategy to address the issue of scarce Tibetan-Chinese parallel corpus data. Our model extends the Transformer speech translation model with a speech recognition decoder and constructs a multi-task learning model that shares the encoder. Moreover, we utilize high-resource ST training data and ASR training data and adopt a multi-level pre-training strategy during the training process to improve the generalization of the Tibetan-Chinese speech translation model. Experimental results demonstrate that our proposed method effectively enhances the ability of Tibetan-Chinese speech translation in low-resource environments, outperforming both the end-to-end baseline models and simple multi-task learning models.
Human Pose Estimation using Multiple Attention Mechanisms in a High-Resolution Network

Aiming to address the issue of information loss during the feature extraction process of High-Resolution Network (HRNet) in human pose estimation tasks, a high-resolution human pose estimation network called CDLNet (CDL-Attention Network) is proposed. Building upon the foundation of HRNet, CDLNet incorporates multiple attention mechanisms. By introducing the coordinate attention module and constructing Coorneck and Coorblock modules to replace the commonly used Bottleneck and Basicblock modules in HRNet, CDLNet effectively captures both channel and position information from the feature maps, enabling the model to better localize and identify target regions. Furthermore, the multi-scale attention mechanism is introduced to capture channel and spatial information on feature maps with 1/16 resolution, and the non-local attention module is added at 1/32 resolution to expand the receptive field and extract useful information. Experimental validation is conducted on the publicly available MS COCOVAL 2017 dataset. The results demonstrate that the method in this paper achieves a 3.4% improvement in mean Average Precision (mAP). It effectively enhances the feature extraction capability, reduces information loss, and improves the accuracy of human pose estimation compared with the HRNet.
Technical Challenges of Deploying Reinforcement Learning Agents for Game Testing in AAA Games

Going from research to production, especially for large and complex software systems, is fundamentally a hard problem. In large-scale game production, one of the main reasons is that the development environment can be very different from the final product. In this technical paper we describe an effort to add an experimental reinforcement learning system to an existing automated game testing solution based on scripted bots in order to increase its capacity. We report on how this reinforcement learning system was integrated with the aim to increase test coverage similar to [1] in a set of AAA games including Battlefield 2042 and Dead Space (2023). The aim of this technical paper is to show a use-case of leveraging reinforcement learning in game production and cover some of the largest time sinks anyone who wants to make the same journey for their game may encounter. Furthermore, to help the game industry to adopt this technology faster, we propose a few research directions that we believe will be valuable and necessary for making machine learning, and especially reinforcement learning, an effective tool in game production.
Contrastive Node Representation Learning in Graph via Mutual Channel Distillation

Contrastive learning is a recent hot topic in computer vision (CV), natural language processing (NLP), and even a broad perspective of artificial intelligence, which also has obtained increasing attention for self-supervised graph representation learning. However, most graph contrastive learning methods might sub-optimally integrate node features and topological structures in a complex graph with rich information. To address this issue and improve the richness of node representation learned from graphs, we propose a novel contrastive node representation learning in graph via Mutual Channel Distillation(MCD), which utilizes knowledge distillation and multichannel contrastive learning techniques to improve the quality of node embeddings. Our approach applies mutual channel distillation and multi-channel contrastive learning to help the student model and the teacher model learn from each other, allowing for more diversified and informative node representation to be generated. Experimental results demonstrated that MCD yielded competitive performance over baseline models on multiple node classification and clustering benchmark datasets.
Game Reviews Reviewed: A Game Designer’s Perspective on AI-generated Game Review Analyses

Due to the growing number of available games, game reviews provide a vital basis for decision-making when buying a game. Apart from that, these reviews could serve as a rich design resource and inspiration for game designers. In this paper, we analyze game reviews formulated by players via a large language model and ask GPT-3, an artificial intelligence, game design-related questions based on the Player Experience Inventory (PXI) questionnaire. The obtained results are evaluated in expert interviews, where we asked four indie game designers about the quality of the game review analyses. The experts were generally impressed by the AI-generated texts but also provided some recommendations regarding the game review analyses’ quality, structure, and setup.
Rotating Frame Labeling and Improved YOLOv7-based Algorithm for Vehicle Object Detection

This paper proposes a novel rotating frame labeling method based on the bottom of the vehicle to overcome the limitations of horizontal labeling frames, which often contain unnecessary background information and overlook vehicle directionality. The proposed method utilizes the CARLA simulation simulator for dataset construction. To enhance compatibility with the dataset, we introduce an improved rotating vehicle target detection algorithm based on YOLOv7. The algorithm incorporates an adaptive feature fusion module to effectively utilize features at different scales, giving priority to features with significant information through adaptive weight learning. Additionally, the integration of CBAM (convolutional block attention model) allows the model to fully exploit channel and spatial features. Furthermore, the Transformer module is integrated to capture global and contextual information, thereby improving the network's detection capability. To address the variability in detection frame angles within the dataset, we replace the original loss function with KLD(Kullback-Leibler Divergence) loss for target rotation frame regression. Experimental results demonstrate excellent performance on our self-built dataset, achieving a mAP value of 94.62% and an FPS value of 37. Moreover, the combined results outperform other algorithms on the HRSC2016 rotating target detection dataset.
An Asynchronous Federated Learning Method for Industrial Internet of Things Based on Local Differential Privacy

Federated learning (FL) has been considered a promising artificial intelligence technology that aims to provide distributed machine learning services while protecting the privacy of training data on terminal device nodes. The traditional federated learning method obtains a local model through training on different device nodes and completes the update of the global model through a central server. However, this approach has issues such as slow communication and privacy leakage. We propose an asynchronous method for updating aggregation in federated learning. The server effectively manages the weight of each model parameter based on the varying training speeds of device nodes to minimize the impact of slower device nodes. Adopting this method can not only improve the efficiency of model updating but also reduce the demand for network bandwidth and computing resources, making it more suitable for resource-constrained environments such as Industrial Internet of Things (IIoT) systems. In addition, to mitigate the impact of poisoned data, we have developed a dynamic mechanism for allocating privacy budget based on the local Rényi Differential Privacy (RDP) mechanism. This mechanism adjusts the size of the privacy budget to achieve a better balance between model efficiency and privacy protection in the context of the Industrial Internet of Things.
Improved YOLOv7 for Small and Overlapping Objects Detection

Object detection is a crucial task in Deep Learning applications. However, detecting small, overlapping objects in complex scenes has always been challenging. To overcome this challenge, we propose a new algorithm based on YOLOv7, which incorporates a multi-path attention enhancement module in the neck part and a receptive field enhancement module on the feature layer responsible for forecasting small objects before the head. This allows all detection layers to focus more effectively on features that require attention and enhance receptive fields. Our experiments on the PASCAL VOC dataset demonstrate that our proposed approach significantly improves the detection of small and overlapping objects compared to YOLOv7.
MLMatch: Multi-layer Image Matching

Image matching is one of the hottest research topics in the field of computer vision, and it aims to compute the correct feature correspondences between images. However, under the influence of illumination changes and scale changes, relying only on the similarity between feature descriptors as the basis for judging feature matching tends to generate more false feature correspondences. To solve the above problems, in this paper, we systematically analyze various factors that lead to false feature correspondences, design corresponding solutions for these various factors, and then propose a multi-layer image matching (MLMatch) method. Firstly, eliminating the potential mismatches caused by brute force matching; secondly, eliminating the mismatches caused by the ambiguity of descriptors; and then, eliminating the feature matches that do not satisfy geometric constraints. Finally, we test the proposed MLMatch method on several open-source datasets and make a comprehensive comparison with the classical methods. Experimental results show that the MLMatch method can not only compute more true feature correspondences but also has a low computational cost.
Comparing Ridge and Logistic Regression with Neural Networks

This research paper presents a comparative analysis of neural networks, logistic regression, and Ridge methods, aiming to assess their respective advantages and limitations. The study involves conducting four analyses on two distinct datasets: two for classification tasks utilizing the MNIST dataset and two for regression tasks based on data derived from the Franke function. The classification analyses employ the widely used MNIST dataset, where the algorithms are trained to classify handwritten digits accurately. In contrast, the regression analyses utilize the Franke function dataset, which generates a two-dimensional surface with smoothly varying peaks and valleys, serving as a benchmark for evaluating the performance of regression models. The findings reveal that neural networks, despite their potential to produce results comparable to more established techniques, present certain challenges in terms of setup and optimization. The complexity of the neural network models used in this study further contributes to the difficulty in interpreting the generated results. These intricacies highlight the need for careful consideration and expertise when employing neural networks for classification and regression tasks. By providing additional information on the concepts of classification and regression, as well as clarifying the datasets used (MNIST and Franke function), we offer a more comprehensive overview of neural networks, logistic regression, and Ridge.
Continuous Sign Language Recognition Method Based on ECA and Time Attention Mechanism

Continuous sign language recognition (CSLR) is a many-to-many sequence learning task, and the commonly used method is to extract features and learn sequences from sign language videos through an encoding-decoding network. However, the effective feature information contained in continuous sign language recognition video frames is relatively small, and there is a problem of insufficient feature extraction due to the excessive redundancy information in the frames. Meanwhile, each frame's importance for sign language recognition varies, which directly affects the accuracy of CSLR results. Therefore, this paper proposes a continuous sign language recognition method based on attention mechanism. Firstly, efficient channel attention (ECA) is incorporated into the residual network in the encoder to allow the network to extract more useful information from each frame feature. The activation function used is HardSwish, which further improves the accuracy of the network. Next, the decoder uses a Long Short-Term Memory (LSTM) combined with time attention mechanism, which assigns different weights to different frames, based on their importance. Finally, we tested our model on the CSL100 dataset and achieved competitive results. The experimental results demonstrate that the attention mechanism we introduced is effective in improving the accuracy of continuous sign language recognition.
Enhanced Vision Transformer with Dual-Dimensional Self-Attention for Image Recognition

This paper presents an improved model based on the Vision Transformer that integrates additional self-attention mechanisms and one-dimensional convolutions to enhance the performance of the Vision Transformer block. The process begins by dividing the image into multiple patches and applying positional encoding. The attention mechanism is first computed for the hidden variables, followed by recalculating the attention mechanism for the patch dimension, and finally, mapping the output result using one-dimensional convolution. By incorporating this mechanism, we capture a greater degree of feature correlations, thereby enhancing the model’s expressive capabilities. Our approach yields significant improvements in image recognition performance, surpassing both traditional Vision Transformer models and conventional convolutional neural networks when parameters and computational complexity are comparable. Of particular note is its effectiveness on relatively small datasets, validating the feasibility and efficiency of our proposed method in enhancing image recognition tasks, making it a promising solution for practical applications across diverse domains.
Image Recognition Algorithm Verification Technology Based on Intelligent Unmanned Systems

With the rapid development of artificial intelligence technology, the application scope of intelligent unmanned systems is expanding. To evaluate the perception ability of intelligent unmanned systems, it is important to solve the problem of insufficient test methods and tools related to image recognition, so it is necessary to study the verification technology of image recognition algorithms. We have developed a testing tool, which uses containerization technology to package the image recognition model and the running environment into image files and run them on the same hardware platform. We choose two image recognition algorithms, build a benchmark data set, and use testing tools to verify the performance of the algorithms.
Enhancing Adversarial Transferability with Multi-level Smoothing Filter Network on Radar Target Recognition

Deep neural network classifiers are susceptible to be deceived by adversarial examples made by attackers, resulting in misclassification. Although the white-box attack performance of the adversarial attacks is very high, the transferability of the adversarial attacks against the unknown model is always very low, and the transferability enhancement of the adversarial examples often brings about an increase in the magnitude of the perturbations, and the concealment of the adversarial examples is weakened. In this paper, we propose a new multi-level smoothing filter network, which is embedded in the iteration of the adversarial attack algorithms to enhance the continuity of adjacent pixels of adversarial perturbations. The experimental results show that after adding our multi-level smoothing filter network, the adversarial examples generated by the attack algorithms are more likely to deceive the target neural networks, and the transferable attack performance and perturbation magnitude compression are improved.
A Campus Virtual Fence System Based on Loitering Detection

Campus virtual fence, based on video analysis technology, can provide an additional layer of safety to the educational institutions. Loitering detection, an important feature of the virtual fence system, is possible to identify suspicious individuals around the campus in advance. This paper presents a campus virtual fence system based on multi-person loitering detection 。 It combines SSD and the state-of-the-art object tracking algorithm DeepSORT with a duration-based analysis mechanism in ROI. In our work, we also proposes several innovations. Firstly, we improve the original SSD algorithm to enhance the detection of small objects. Secondly, we applies quantization techniques, which accelerates the processing speed. Additionally, we optimizes the system’s logic to better handle lost targets in ROI. Our approach efficiently handles complex scenes with overlapping and small targets.