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Computer Sciences & Mathematics Forum

Computer Sciences & Mathematics Forum is an open access journal dedicated to publishing findings resulting from academic conferences, workshops, and similar events in the area of computer science and mathematics.
Each conference proceeding can be individually indexed, is citable via a digital object identifier (DOI), and is freely available under an open access license. The conference organizers and proceedings editors are responsible for managing the peer-review process and selecting papers for conference proceedings.

All Articles (272)

  • Proceeding Paper
  • Open Access

Current encryption technologies mostly rely on complex algorithms or difficult mathematical problems to improve security. Therefore, it is difficult for these encryption technologies to possess both high security and high efficiency, which are two properties that people desire. Trying to solve this dilemma, we built a new encryption technology, called configurable encryption technology (CET), based on the typical structure of reconfigurable quaternary logic operator (RQLO) that was invented in 2018. We designed the CET as a block cipher for symmetric encryption, where we use four 32-quit RQLO typical structures as the encryptor, decryptor, and two key derivation operators. Taking advantage of the reconfigurability of the RQLO typical structure, the CET can automatically reconfigure the keys and symbol substitution rules of the encryptor and decryptor after each encryption operation. We found that a chip containing about 70,000 transistors and 500 MB of nonvolatile memory could provide all the CET devices and generalized keys needed for any user’s lifetime, to implement a practical one-time pad encryption technology. We also developed a strategy to solve the current key distribution problem with prestored generalized key source data and on-site appointment codes. The CET is expected to provide a theoretical basis and core technology for using the RQLO to build a new cryptographic system with high security, fast encryption/decryption speed, and low manufacturing cost.

10 April 2024

Schematic diagram of 1-quit RQLO typical structure.
  • Proceeding Paper
  • Open Access

iBALR3D: imBalanced-Aware Long-Range 3D Semantic Segmentation

  • Keying Zhang,
  • Ruirui Cai and
  • Xinqiao Wu
  • + 2 authors

Three-dimensional semantic segmentation is crucial for comprehending transmission line structure and environment. This understanding forms the basis for a variety of applications, such as automatic risk assessment of line tripping caused by wildfires, wind, and thunder. However, the performance of current 3D point cloud segmentation methods tends to degrade on imbalanced data, which negatively impacts the overall segmentation results. In this paper, we proposed an imBalanced-Aware Long-Range 3D Semantic Segmentation framework (iBALR3D) which is specifically designed for large-scale transmission line segmentation. To address the unsatisfactory performance on categories with few points, an Enhanced Imbalanced Contrastive Learning module is first proposed to improve feature discrimination between points across sampling regions by contrasting the representations with the assistance of data augmentation. A structural Adaptive Spatial Encoder is designed to capture the distinguish measures across different components. Additionally, we employ a sampling strategy to enable the model to concentrate more on regions of categories with few points. This strategy further enhances the model’s robustness in handling challenges associated with long-range and significant data imbalances. Finally, we introduce a large-scale 3D point cloud dataset (500KV3D) captured from high-voltage long-range transmission lines and evaluate iBALR3D on it. Extensive experiments demonstrate the effectiveness and superiority of our approach.

14 March 2024

  • Proceeding Paper
  • Open Access

This paper explains that setting up artificial intelligence courses can clearly enhance students’ interest in high technology, boost learning confidence and promote students’ overall development in the following three aspects: the significance of artificial intelligence education to students, the confusion regarding artificial intelligence teaching in this stage, especially in rural middle schools, and some related suggestions.

26 February 2024

  • Proceeding Paper
  • Open Access

Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as an effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-optimal policies and lead to a degraded online reinforcement learning performance. In this paper, we propose a model-based data augmentation strategy to maximize the benefits of offline reinforcement learning pre-training and reduce the scale of data needed to be effective. Our approach leverages a world model of the environment trained on the offline dataset to augment states during offline pre-training. We evaluate our approach on a variety of MuJoCo robotic tasks, and our results show that it can jumpstart online fine-tuning and substantially reduce—in some cases by an order of magnitude—the required number of environment interactions.

18 February 2024

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Editors: Kuan-Chuan Peng, Ziyan Wu

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Computer Sciences & Mathematics Forum - ISSN 2813-0324Creative Common CC BY license