Spiking Neural Networks(SNNs) provide a brain-inspired and event-driven mechanism that is believed to be critical to unlock energy-efficient deep learning. The mixture-of-experts approach mirrors the parallel distributed processing of nervous systems, introducing conditional computation policies and expanding model capacity without scaling up the number of computational operations. Additionally, spiking mixture-of-experts self-attention mechanisms enhance representation capacity, effectively capturing diverse patterns of entities and dependencies between visual or linguistic tokens. However, there is currently a lack of hardware support for highly parallel distributed processing needed by spiking transformers, which embody a brain-inspired computation. This paper introduces the first 3D hardware architecture and design methodology for Mixture-of-Experts and Multi-Head Attention spiking transformers. By leveraging 3D integration with memory-on-logic and logic-on-logic stacking, we explore such brain-inspired accelerators with spatially stackable circuitry, demonstrating significant optimization of energy efficiency and latency compared to conventional 2D CMOS integration.

Thailand has targeted to achieve carbon neutrality by 2050 when the power grid will need to accommodate 50% share of renewable electricity generation capacity; see [Ene21]. The most recent draft of Power Development Plan 2024 (PDP2024) for 2024 - 2037 from [Ene24] proposes to add a new solar generation capacity of approximately 24,400 MWp (more than 4 times the amount issued in the previous Alternative Energy Development Plan 2015-2036 (AEDP2015) at 6,000 MWp, shown in [Dep15, p.9]. This amount does not yet include behind-the-meter, self-generation solar installed capacities of the prosumers, which is expected to increase at an accelerating rate. Solar integration into the power grid with such a sharprising amount will pose technical challenges to the operation and control of the transmission and distribution networks, carried out by the transmission system operator (TSO) and distribution system operator (DSO), as presented in [OB16]. Hence, TSO in Thailand will need an effective means to estimate the solar power generation across the entire transmission network, on an hourly basis, or even finer time resolution, to provide economic hour-to-hour generation dispatch for load following the total net load of the transmission, and to prepare sufficient system flexibility (i.e., ramp-rate capability of the thermal and hydropower plants, or energy storage systems) to cope with the net load fluctuation due to solar generation intermittency for maintaining system frequency stability, concurrently, in its operation. For DSO, a significant amount of reverse power flow when self-generation from solar exceeds self-consumption can lead to technical concerns of voltage regulation and equipment overloading problems. The near real-time estimation of solar generation in each distribution area will enable DSO to activate proper network switching or reconfiguring to mitigate such fundamental concerns to ensure its reliable operation.

Alphabet's Google plans to invest 1 billion to build data centers in Thailand, joining global tech companies in adding cloud and artificial intelligence infrastructure in Southeast Asia. The company will add facilities in Bangkok and Chonburi, a province southeast of the capital. The outlay could help add 4 billion to Thailand's economy by 2029 and support 14,000 jobs annually over the next five years, Google said Monday, citing a Deloitte study. The investment was unveiled by Google and Paetongtarn Shinawatra, Thailand's recently appointed prime minister, underscoring the push by Southeast Asia's governments to attract foreign tech firms. Long seen as a tech hinterland, the region of about 675 million people is fast emerging as a growth opportunity for Apple, Microsoft, Nvidia and Amazon, which are spending billions of dollars to ramp up AI data centers from Thailand and Malaysia to Singapore and Indonesia.

Pathogen identification is pivotal in diagnosing, treating, and preventing diseases, crucial for controlling infections and safeguarding public health. Traditional alignment-based methods, though widely used, are computationally intense and reliant on extensive reference databases, often failing to detect novel pathogens due to their low sensitivity and specificity. Similarly, conventional machine learning techniques, while promising, require large annotated datasets and extensive feature engineering and are prone to overfitting. Addressing these challenges, we introduce PathoLM, a cutting-edge pathogen language model optimized for the identification of pathogenicity in bacterial and viral sequences. Leveraging the strengths of pre-trained DNA models such as the Nucleotide Transformer, PathoLM requires minimal data for fine-tuning, thereby enhancing pathogen detection capabilities. It effectively captures a broader genomic context, significantly improving the identification of novel and divergent pathogens. We developed a comprehensive data set comprising approximately 30 species of viruses and bacteria, including ESKAPEE pathogens, seven notably virulent bacterial strains resistant to antibiotics. Additionally, we curated a species classification dataset centered specifically on the ESKAPEE group. In comparative assessments, PathoLM dramatically outperforms existing models like DciPatho, demonstrating robust zero-shot and few-shot capabilities. Furthermore, we expanded PathoLM-Sp for ESKAPEE species classification, where it showed superior performance compared to other advanced deep learning methods, despite the complexities of the task.

Startling video footage capturing the tragic moment a robotic arm fatally crushed a worker at a factory in Thailand has emerged today. The harrowing incident unfolded at the Vandapac factory located in Thailand's Chonburi province on March 27. The unsuspecting worker appeared to be laying out sheets of material when the arm forcefully swung down and pinned him against a bench. Unsettling CCTV footage shows how the victim was incapacitated beneath the hulking metal device as a fellow employee continued working across the room, seemingly unaware of the catastrophe unfolding just behind him. Emergency responders swiftly intervened after the alarm was eventually raised, releasing the man before administering critical aid and rushing him to Chonburi Hospital.

We present PyThaiNLP, a free and open-source natural language processing (NLP) library for Thai language implemented in Python. It provides a wide range of software, models, and datasets for Thai language. We first provide a brief historical context of tools for Thai language prior to the development of PyThaiNLP. We then outline the functionalities it provided as well as datasets and pre-trained language models. We later summarize its development milestones and discuss our experience during its development. We conclude by demonstrating how industrial and research communities utilize PyThaiNLP in their work. The library is freely available at https://github.com/pythainlp/pythainlp.

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.

Real estate is a critical sector in Thailand's economy, which has led to a growing demand for a more accurate land price prediction approach. Traditional methods of land price prediction, such as the weighted quality score (WQS), are limited due to their reliance on subjective criteria and their lack of consideration for spatial variables. In this study, we utilize aerial or satellite imageries from Google Map API to enhance land price prediction models from the dataset provided by Kasikorn Business Technology Group (KBTG). We propose a similarity-based asset valuation model that uses a Siamese-inspired Neural Network with pretrained EfficientNet architecture to assess the similarity between pairs of lands. By ensembling deep learning and tree-based models, we achieve an area under the ROC curve (AUC) of approximately 0.81, outperforming the baseline model that used only tabular data. The appraisal prices of nearby lands with similarity scores higher than a predefined threshold were used for weighted averaging to predict the reasonable price of the land in question. At 20\% mean absolute percentage error (MAPE), we improve the recall from 59.26\% to 69.55\%, indicating a more accurate and reliable approach to predicting land prices. Our model, which is empowered by a more comprehensive view of land use and environmental factors from aerial or satellite imageries, provides a more precise, data-driven, and adaptive approach for land valuation in Thailand.

Every human being in this world produces waste. South Africa is a developing country with many townships that have limited waste resources. Over-increasing population growth overpowers the volume of most municipal authorities to provide even the most essential services. Waste in townships is produced via littering, dumping of bins, cutting of trees, dumping of waste near rivers, and overrunning of waste bins. Waste increases diseases, air pollution, and environmental pollution, and lastly increases gas emissions that contribute to the release of greenhouse gases. The ungathered waste is dumped widely in the streets and drains contributing to flooding, breeding of insects, rodent vectors, and spreading of diseases. Therefore, the aim of this paper is to design a smart waste management system for the city of Johannesburg. The city of Johannesburg contains waste municipality workers and has provided some areas with waste resources such as waste bins and trucks for collecting waste. But the problem is that the resources only are not enough to solve the problem of waste in the city. The waste municipality uses traditional ways of collecting waste such as going to each street and picking up waste bins. The traditional way has worked for years but as the population is increasing more waste is produced which causes various problems for the waste municipalities and the public at large. The proposed system consists of sensors, user applications, and a real-time monitoring system. This paper adopts the experimental methodology.

We consider a fairness problem in resource allocation where multiple groups demand resources from a common source with the total fixed amount. The general model was introduced by Elzayn et al. [FAT*'19]. We follow Donahue and Kleinberg [FAT*'20] who considered the case when the demand distribution is known. We show that for many common demand distributions that satisfy sharp lower tail inequalities, a natural allocation that provides resources proportional to each group's average demand performs very well. More specifically, this natural allocation is approximately fair and efficient (i.e., it provides near maximum utilization). We also show that, when small amount of unfairness is allowed, the Price of Fairness (PoF), in this case, is close to 1.