Malaysia declared it'll build a first-of-its-kind AI system powered by Huawei Technologies chips, only to distance itself from that statement a day later, underscoring the Asian nation's delicate position in the U.S.-Chinese AI race. Deputy Minister of Communications Teo Nie Ching said in a speech Monday her country would be the first to activate an unspecified class of Huawei "Ascend GPU-powered AI servers at national scale." Malaysia would deploy 3,000 units of Huawei's primary AI offering by 2026, she said in prepared remarks reviewed by Bloomberg News. Chinese startup DeepSeek would also make one of its AI models available to the Southeast Asian country, the official added.

Kuala Lumpur, Malaysia – China's President Xi Jinping has arrived in Malaysia as part of a Southeast Asian tour which is seen as delivering a personal message that Beijing is a more reliable trading partner than the United States amid a bruising trade war with Washington. Xi arrived in the capital, Kuala Lumpur, on Tuesday evening in what is his first visit to Malaysia since 2013. He flew in from Vietnam where he had signed dozens of trade cooperation agreements in Hanoi on everything from artificial intelligence to rail development. On touching down, Xi said that deepening "high-level strategic cooperation" was good for the common interests of both China and Malaysia, and good for peace, stability and prosperity in the region and the world", according to the official Malaysian news agency Bernama. Xi's three-country tour and his "message" that Beijing is Southeast Asia's better friend than the truculent administration of US President Donald Trump comes as many countries in the 10-member Association of Southeast Asian Nations (ASEAN) bloc are unhappy with their treatment after the US imposed huge tariffs on countries around the world. "This is a very significant visit.

Kulim, Malaysia – When tech giant AT&S decided a few years ago that it needed to ramp up production to keep pace with the artificial intelligence (AI) boom, it did not look to its largest manufacturing facilities in China. The Austrian firm's plants in Chongqing and Shanghai – opened in 2022 and 2016, respectively – employ some 9,000 workers between them, churning out high-end components used in everything from consumer electronics to cars. But AT&S was at the same time coming to grips with the risks of concentrating production in one country. Like many tech firms grappling with the disruption of the COVID-19 pandemic and the trade war salvoes between the United States and China, AT&S decided it needed to diversify its supply chains. Malaysia quickly emerged at the top of the company's list of potential locations for its next plant.

The evaluation of smart contract reputability is essential to foster trust in decentralized ecosystems. However, existing methods that rely solely on static code analysis or transactional data, offer limited insight into evolving trustworthiness. We propose a multimodal data fusion framework that integrates static code features with transactional data to enhance reputability prediction. Our framework initially focuses on static code analysis, utilizing GAN-augmented opcode embeddings to address class imbalance, achieving 97.67% accuracy and a recall of 0.942 in detecting illicit contracts, surpassing traditional oversampling methods. This forms the crux of a reputability-centric fusion strategy, where combining static and transactional data improves recall by 7.25% over single-source models, demonstrating robust performance across validation sets. By providing a holistic view of smart contract behaviour, our approach enhances the model's ability to assess reputability, identify fraudulent activities, and predict anomalous patterns. These capabilities contribute to more accurate reputability assessments, proactive risk mitigation, and enhanced blockchain security.

Autonomous Micro Air Vehicles (MAVs) are becoming essential in precision agriculture to enhance efficiency and reduce labor costs through targeted, real-time operations. However, existing unmanned systems often rely on GPS-based navigation, which is prone to inaccuracies in rural areas and limits flight paths to predefined routes, resulting in operational inefficiencies. To address these challenges, this paper presents ForaNav, an insect-inspired navigation strategy for autonomous navigation in plantations. The proposed method employs an enhanced Histogram of Oriented Gradient (HOG)-based tree detection approach, integrating hue-saturation histograms and global HOG feature variance with hierarchical HOG extraction to distinguish oil palm trees from visually similar objects. Inspired by insect foraging behavior, the MAV dynamically adjusts its path based on detected trees and employs a recovery mechanism to stay on course if a target is temporarily lost. We demonstrate that our detection method generalizes well to different tree types while maintaining lower CPU usage, lower temperature, and higher FPS than lightweight deep learning models, making it well-suited for real-time applications. Flight test results across diverse real-world scenarios show that the MAV successfully detects and approaches all trees without prior tree location, validating its effectiveness for agricultural automation.

--Hybrid light fidelity (LiFi) and wireless fidelity (WiFi) indoor networks has been envisioned as a promising technology to alleviate radio frequency spectrum crunch to accommodate the ever-increasing data rate demand in indoor scenarios. The hybrid LiFi/WiFi indoor networks can leverage the advantages of fast data transmission from LiFi and wider coverage of WiFi, thus complementing well with each other and further improving the network performance compared with the standalone networks. However, to leverage the co-existence, several challenges should be addressed, including but not limited to user association, mobility support, and efficient resource allocation. Therefore, the objective of the paper is to design a new user-access point association algorithm to maximize the sum throughput of the hybrid networks. We first mathematically formulate the sum data rate maximization problem by determining the AP selection for each user in indoor networks with consideration of user mobility and practical capacity limitations, which is a nonconvex binary integer programming problem. T o solve this problem, we then propose a sequential-proximal policy optimization (S-PPO) based deep reinforcement learning method. Extensive simulations are conducted to evaluate the proposed method by comparing it with exhaustive search (ES), signal strength strategy (SSS), and trust region policy optimization (TRPO) methods. Comprehensive simulation results demonstrate that our solution algorithm can outperform SSS by about 32.25% of the sum throughput and 19.09% of the fairness on average, and outperform TRPO by about 10.34% and 10.23%, respectively. Over the past few years, the usage of the internet has been continuously increasing. According to the latest data, people spend an average of 6 hours and 58 minutes daily on screens connected to the internet [1]. Moreover, an increasing number of applications require high-speed support, such as video calls, VR gaming, streaming media, and so on. However, we are facing a global digital divide, i.e., internet speeds in urban areas are often much faster than in rural areas, due to the generally less developed internet infrastructure in rural locations. Visible light communication (VLC), where light-emitting diodes (LEDs) can be used to transmit data by optical spectrum, has been envisioned as a promising solution for last-mile access because of its high bandwidth, enhanced security, electromagnetic interference-free nature, and easy integration with existing infrastructure [2]-[7].

Neural network-based decoding methods have shown promise in enhancing error correction performance, but traditional approaches struggle with the challenges posed by punctured codes. In particular, these methods fail to address the complexities of variable code rates and the need for protocol compatibility. This paper presents a unified Long Short-Term Memory (LSTM)-based decoding architecture specifically designed to overcome these challenges. The proposed method unifies punctured convolutional and Turbo codes. A puncture embedding mechanism integrates puncturing patterns directly into the network, enabling seamless adaptation to varying code rates, while balanced bit error rate training ensures robustness across different code lengths, rates, and channels, maintaining protocol flexibility. Extensive simulations in Additive White Gaussian Noise and Rayleigh fading channels demonstrate that the proposed approach outperforms conventional decoding techniques, providing significant improvements in decoding accuracy and robustness. These results underscore the potential of LSTM-based decoding as a promising solution for next-generation artificial intelligence powered communication systems.

Throughout this project, we have attempted to develop an automatic evaluator that determines whether Basque language compositions meet the C1 level. To achieve our goal, we obtained 10,000 transcribed compositions through an agreement between HABE and HiTZ to train our system. We have developed different techniques to avoid data scarcity and system overfitting: EDA, SCL and regulation; We have also conducted tests with different Language Models to analyze their behavior. Finally, we have also performed analyses of different system behaviors to measure model calibration and the impact of artifacts. -- Proiektu honetan zehar euskarazko idazlanek C1 maila duten edo ez zehazten duen ebaluatzaile automatiko bat garatzen saiatu gara. Gure helburua betetzeko HABE eta HiTZ arteko hitzarmenaren bitartez 10.000 transkribatutako idazlan eskuratu ditugu gure sistema entrenatzeko. Datu eskasia eta sistemaren gaindoitzea ekiditeko teknika ezberdinak landu ditugu: EDA, SCL eta erregulazioa; Hizkuntza Eredu ezberdinekin ere probak egin ditugu duten portaera aztertzeko. Azkenik, sistema ezberdinen portaeren analisiak ere egin ditugu, ereduen kalibrazioa eta artefaktuen eragina neurtzeko.

In view of the gap in the current large language model in sharing memory across dialogues, this research proposes a wormhole memory module (WMM) to realize memory as a Rubik's cube that can be arbitrarily retrieved between different dialogues. Through simulation experiments, the researcher built an experimental framework based on the Python environment and used setting memory barriers to simulate the current situation where memories between LLMs dialogues are difficult to share. The CoQA development data set was imported into the experiment, and the feasibility of its cross-dialogue memory retrieval function was verified for WMM's nonlinear indexing and dynamic retrieval, and a comparative analysis was conducted with the capabilities of Titans and MemGPT memory modules. Experimental results show that WMM demonstrated the ability to retrieve memory across dialogues and the stability of quantitative indicators in eight experiments. It contributes new technical approaches to the optimization of memory management of LLMs and provides experience for the practical application in the future.