This study introduces a Preference-Based Reinforcement Learning (PbRL) approach tailored for autonomous vehicle (AV) applications within a simulated environment. Traditional RL methods often struggle with the complexities of reward function engineering, failing to perform behaviors of human desire. The proposed framework integrates human preferences directly into the training loop, our framework offers a novel methodology for enhancing the decision-making processes of autonomous system. Our results demonstrate that PbRL can refine the strategies of AVs to align more closely with human-like decision-making, highlighting the potential for increased adaptability and safety in autonomous technologies.

The music industry is facing challenges in engaging fans and providing transparency, feedback, and rewards. Blockchain technology presents a potential solution by enabling new forms of fan engagement and participation. This paper proposes a blockchain-based music platform that leverages Ethereum’s decentralized platform and smart contract functionality. Ethereum’s Proof of Stake (PoS) consensus algorithm makes it more energy-efficient than the Proof of Work (PoW) algorithm used by Bitcoin. The platform could facilitate investment in up-and-coming artists, feedback mechanisms, and rewards for fans. The scalability and decentralization of Ethereum make it an attractive choice for building a platform that can accommodate a large number of users and transactions without compromising performance. The proposed platform offers a secure, scalable, and decentralized solution that provides novel ways for fans to engage and participate in the music industry while being energy-efficient, sustainable, and accessible to everyone with a stake in the system.

Abstract In software, an algorithm is a well-organized sequence of actions that provides the optimal way to complete a task. Algorithmic thinking is also essential to break-down a problem and conceptualize solutions in some steps. The proper selection of an algorithm is pivotal to improve computational performance and software productivity as well as to programming learning. That is, determining a suitable algorithm from a given code is widely relevant in software engineering and programming education. However, both humans and machines find it difficult to identify algorithms from code without any meta-information. This study aims to propose a program code classification model that uses a convolutional neural network (CNN) to classify codes based on the algorithm. First, program codes are transformed into a sequence of structural features (SFs). Second, SFs are transformed into a one-hot binary matrix using several procedures. Third, different structures and hyperparameters of the CNN model are fine-tuned to identify the best model for the code classification task. To do so, 61,614 real-world program codes of different types of algorithms collected from an online judge system are used to train, validate, and evaluate the model. Finally, the experimental results show that the proposed model can identify algorithms and classify program codes with a high percentage of accuracy. The average precision, recall, and F-measure scores of the best CNN model are 95.65%, 95.85%, and 95.70%, respectively, indicating that it outperforms other baseline models.

Despite the incredible adoption of cryptocurrencies, blockchain-based cryptocurrencies have likewise raised some concerns. The scalability problem is the major one among them. An off-blockchain payment channel network (PCN) has been introduced to solve this issue. PCN can fundamentally reduce blockchain scalability by constructing a number of payment channels between the nodes and without committing every single transaction to the blockchain. But as a matter of fact, there has an unwanted assumption in PCN that channel participants must remain online and follow blockchain updates, for the synchronization with blockchain to protect the channel against deception. To mitigate this issue “Watchtower” concept has been proposed. Watchtower is a watching service and always stays online that a channel participant can hire it by offering incentives for monitoring the channel and checking blockchain updates consistently to prevent fraud on behalf of the hiring party. However, watchtower may be more beneficial by cooperating with the cheating counterparty and neglecting to perform the watching service properly. The efficiency drawback can occur for that. In this work, we have been motivated by this issue and tried to find out an effective and reliable watchtower for the channel watching service from multiple watchtower nodes or candidates in the PCN. In particular, we have been approached by using the distributed Peterson Leader-Election Algorithm to find the best watchtower among multiple of them where the more successfully performed work node or candidate will be selected for the channel monitoring job. We also have provided a detailed step-by-step process of the algorithm including experiments and illustrations for employing watchtower among multiple of them.

Efficient path planning and minimization of path movement costs for collision-free faster robot movement are very important in the field of robot automation. Several path planning algorithms have been explored to fulfill these requirements. Among them, the A-star (A*) algorithm performs better than others because of its heuristic search guidance. However, the performance, effectiveness, and searching time complexity of this algorithm mostly depends on the robot motion block to search for the goal by avoiding obstacles. With this challenge kept in mind, this paper proposes an efficient robot motion block with different block sizes for the A* path planning algorithm. The proposed approach reduces robots’ path cost and time complexity to find the goal position as well as avoid obstacles. In this proposed approach, grid-based maps are used where the robot’s next move is decided by searching eight directions among the surrounding grid points. However, the proposed robot motion blocks size has a significant effect on path cost and time complexity of the A* path planning algorithm. For the experiment and to validate the efficiency of the proposed approach, an online benchmarked dataset is used. The proposed approach is applied on thousands of different grid maps with various obstacles, starting, and goal positions. The obtained results from the experiment show that the presented robot motion blocks reduce the robot’s pathfinding time complexity and number of search nodes by maintaining a minimum path cost towards the goal position.

Efficient knowledge sharing, computation load minimization, and collision-free movement are very important issues in the field of multi-robot automation. Several cloud robot architectures have been investigated to fulfill these requirements. However, the performance of the cloud-robot architectures created to date are suboptimal due to the lack of efficient data management for multi-robotic systems. With this point in mind, this paper proposes an efficient cloud multi-robot framework with cloud database model for mobile robot applications to facilitate multi-robot management, communication, and resource sharing. In this proposed architecture, the cloud framework is comprised with cloud data analysis, cloud database management, and cloud service management. The data analysis serves different data processing and decision-making tasks for generating the next robot action based on robot sensors’ data with the help of a data access components layer. A multistage cloud database model distributes, stores, and accesses different categories of data related to robot sensors and environments. And cloud service facilitates multi-robot management, communication, and resource sharing in the cloud framework. Additionally, as a use case, a cloud-based convolutional neural network (CNN) model is introduced for learning and recognizing robot application data. The obtained results of our tests indicate that the proposed cloud-robot architecture provides efficient computation power, communications, and knowledge sharing for managing multi-mobile robot systems.