What's happening in Myanmar's civil war as military holds elections? Voters in parts of Myanmar are heading to the polls on Sunday for an election that critics view as a bid by the country's generals to legitimise military rule, nearly five years after they overthrew the government of Nobel Laureate Aung San Suu Kyi. The multi-phased election is unfolding amid a raging civil war, with ethnic armed groups and opposition militias fighting the military for control of vast stretches of territory, stretching from the borderlands with Bangladesh and India in the west, across the central plains, to the frontiers with China and Thailand in the north and east. Another third will be covered during a second and third phase in January, while voting has been cancelled altogether in the remainder. Fighting, including air raids and arson, has intensified in several areas.

It was a Monday night in Myanmar's Chang U township in the central Sagaing region, where nearly 100 people had gathered to mark Thadingyut, the festival of the full moon. Some held candles at the event, which doubled as both a celebration and a protest against the military, which seized power in 2021, plunging the country into a bloody civil war. But the celebration soon turned into horror as a motorised paraglider - known locally as a paramotor - flew overhead and dropped bombs onto the crowd. The attack lasted just seven minutes, but at least 26 people died as a result and dozens more were injured. Initially, I thought the lower part of my body had been severed, one 30-year-old who was at the gathering told news agency Reuters.

Plus: ICE deploys secretive phone surveillance tech, officials warn of Chinese surveillance tools in US highway infrastructure, and more. Right-wing internet personality and Turning Point USA cofounder Charlie Kirk was shot and killed on Wednesday during a speaking engagement at Utah Valley University in Orem, Utah. After a chaotic 24-hour manhunt, the FBI named 22-year-old Utah resident Tyler Robinson as a suspect in the murder. As polarization and political violence continues to increase in the US, a new platform from the Public Service Alliance is offering tools like data-removal services and threat monitoring to public servants who increasingly need to defend themselves and their data. Meanwhile, new research this week warned that the number of US investors putting money into invasive commercial spyware rose significantly in 2024.

Geedge Networks, a company with ties to the founder of China's mass censorship infrastructure, is selling its censorship and surveillance systems to at least four other countries in Asia and Africa. A leak of more than 100,000 documents shows that a little-known Chinese company has been quietly selling censorship systems seemingly modeled on the Great Firewall to governments around the world. Geedge Networks, a company founded in 2018 that counts the "father" of China's massive censorship infrastructure as one of its investors, styles itself as a network-monitoring provider, offering business-grade cybersecurity tools to "gain comprehensive visibility and minimize security risks" for its customers, the documents show. In fact, researchers found that it has been operating a sophisticated system that allows users to monitor online information, block certain websites and VPN tools, and spy on specific individuals. Researchers who reviewed the leaked material found that the company is able to package advanced surveillance capabilities into what amounts to a commercialized version of the Great Firewall--a wholesale solution with both hardware that can be installed in any telecom data center and software operated by local government officers.

Cox's Bazar, Bangladesh – The sound of children at play echoes through the verdant lanes of one of the dozens of refugee camps on the outskirts of Cox's Bazar, a densely populated coastal town in southeast Bangladesh. Just for a moment, the sounds manage to soften the harsh living conditions faced by the more than one million people who live here in the world's largest refugee camp. Described as the most persecuted people on the planet, the Rohingya Muslim refugees in Bangladesh may now be one of the most forgotten populations in the world, eight years after being ethnically cleansed from their homes in neighbouring Myanmar by a predominantely Buddhist military regime. "Cox's Bazar is ground zero for the impact of budget cuts on people in desperate need," UN Secretary-General Antonio Guterres said during a visit to the sprawling camps in May. The UN chief's visit followed United States President Donald Trump's gutting of the US Agency for International Development (USAID), which has stalled several key projects in the camps, and the United Kingdom announcing cuts to foreign aid in order to increase defence spending.

This paper explores syllable sequence prediction in Abugida languages using Transformer-based models, focusing on six languages: Bengali, Hindi, Khmer, Lao, Myanmar, and Thai, from the Asian Language Treebank (ALT) dataset. We investigate the reconstruction of complete syllable sequences from various incomplete input types, including consonant sequences, vowel sequences, partial syllables (with random character deletions), and masked syllables (with fixed syllable deletions). Our experiments reveal that consonant sequences play a critical role in accurate syllable prediction, achieving high BLEU scores, while vowel sequences present a significantly greater challenge. The model demonstrates robust performance across tasks, particularly in handling partial and masked syllable reconstruction, with strong results for tasks involving consonant information and syllable masking. This study advances the understanding of sequence prediction for Abugida languages and provides practical insights for applications such as text prediction, spelling correction, and data augmentation in these scripts.

Despite dramatic recent progress in NLP, it is still a major challenge to apply Large Language Models (LLM) to low-resource languages. This is made visible in benchmarks such as Cross-Lingual Natural Language Inference (XNLI), a key task that demonstrates cross-lingual capabilities of NLP systems across a set of 15 languages. In this paper, we extend the XNLI task for one additional low-resource language, Myanmar, as a proxy challenge for broader low-resource languages, and make three core contributions. First, we build a dataset called Myanmar XNLI (myXNLI) using community crowd-sourced methods, as an extension to the existing XNLI corpus. This involves a two-stage process of community-based construction followed by expert verification; through an analysis, we demonstrate and quantify the value of the expert verification stage in the context of community-based construction for low-resource languages. We make the myXNLI dataset available to the community for future research. Second, we carry out evaluations of recent multilingual language models on the myXNLI benchmark, as well as explore data-augmentation methods to improve model performance. Our data-augmentation methods improve model accuracy by up to 2 percentage points for Myanmar, while uplifting other languages at the same time. Third, we investigate how well these data-augmentation methods generalise to other low-resource languages in the XNLI dataset.

In a highly globalized world, it is important for multi-modal large language models (MLLMs) to recognize and respond correctly to mixed-cultural inputs. For example, a model should correctly identify kimchi (Korean food) in an image both when an Asian woman is eating it, as well as an African man is eating it. However, current MLLMs show an over-reliance on the visual features of the person, leading to misclassification of the entities. To examine the robustness of MLLMs to different ethnicity, we introduce MixCuBe, a cross-cultural bias benchmark, and study elements from five countries and four ethnicities. Our findings reveal that MLLMs achieve both higher accuracy and lower sensitivity to such perturbation for high-resource cultures, but not for low-resource cultures. GPT-4o, the best-performing model overall, shows up to 58% difference in accuracy between the original and perturbed cultural settings in low-resource cultures. Our dataset is publicly available at: https://huggingface.co/datasets/kyawyethu/MixCuBe.

The language called Balti belongs to the Sino-Tibetan, specifically the Tibeto-Burman language family. It is understood with variations, across populations in India, China, Pakistan, Nepal, Tibet, Burma, and Bhutan, influenced by local cultures and producing various dialects. Considering the diverse cultural, socio-political, religious, and geographical impacts, it is important to step forward unifying the dialects, the basis of common root, lexica, and phonological perspectives, is vital. In the era of globalization and the increasingly frequent developments in AI technology, understanding the diversity and the efforts of dialect unification is important to understanding commonalities and shortening the gaps impacted by unavoidable circumstances. This article analyzes and examines how artificial intelligence AI in the essence of Large Language Models LLMs, can assist in analyzing, documenting, and standardizing the endangered Balti Language, based on the efforts made in different dialects so far.

Myanmar's resistance fighters notched decisive breakthroughs last year by relying on a scattered fleet of drones in battles against one of Southeast Asia's most feared militaries. But as the civil war grinds on, the rebels increasingly find their familiar weapons -- Chinese-made commercial drones modified to carry arms -- in the unfamiliar hands of the country's ruling junta, according to seven people with knowledge of the matter. "The battle is changing now as drones are being used by both sides," said a 31-year-old rebel fighter in the country's southeast, identifying himself by the nom de guerre of Ta Yoke Gyi. He said the junta began using armed unmanned aerial vehicles (UAVs) to attack the rebels at around the turn of the year, and that a drone his unit recently shot down was identified as Chinese from its components and had been modified for combat. Two rebel fighters in other parts of Myanmar also described similar skirmishes.