This week's publication of the Pandora Papers–which the International Consortium of International Journalists based on a trove of private data leaked from offshore tax havens–showcased the alarming extent of fraud and corruption in the world. While big data tech like graph analytics and machine learning can help to a shine light on bad actors, we'll always be playing catch up, fraud hunters tell Datanami. The sheer numbers behind the Pandora Papers, which the ICIJ published on October 3, 2021, are staggering. The ICIJ was provided with 11.9 million documents, including text files, PDFs, images, emails, and spreadsheets, from 14 offshore tax havens, totaling 2.9 TB of data. The documents contained information about 27,000 shell companies created to protect the assets of 29,000 beneficial owners, including 130 billionaires and 330 politicians from 90 countries.

At the outbreak of World War I, the French army was mobilised in the fashion of Napoleonic times. On horseback and equipped with swords, the cuirassiers wore bright tricolour uniforms topped with feathers--the same get-up as when they swept through Europe a hundred years earlier. Vast fields were filled with trenches, barbed wire, poison gas and machine gun fire--plunging the ill-equipped soldiers into a violent hellscape of industrial-scale slaughter. Only three decades after the first World War I bayonet charge across no man's land, the US was able to incinerate entire cities with a single (nuclear) bomb blast. And since the destruction of Hiroshima and Nagasaki in 1945, our rulers' methods of war have been made yet more deadly and "efficient".

Babylon, a world leading digital-first, value-based care company today announced a collaboration to explore opportunities to improve the accessibility, affordability and quality of healthcare for people across the world by using their combined AI, Machine Learning and Cloud technologies. The new collaboration has the aim of exploring opportunities to accelerate and enhance current AI and Machine Learning, utilizing them to shift the focus from sick care to preventative health care. "Babylon and Microsoft working together shows our combined commitment to build on our leading-edge digital health technologies and deliver better access and greater affordability for health systems and patients alike", said Ali Parsa, Chief Executive Officer, Babylon. "We share the same vision of healthcare and believe that by bringing our assets together we can further the digital health revolution, offer immediate access to all-in-one personalized care and we can enhance the consumer experience, improve patient outcomes and reduce overall costs." As part of this relationship, the two organizations will explore opportunities to innovate and deliver across product, cloud and AI research with the view toward increasing the impact of their complementary healthcare technologies and extending the Babylon healthcare platform.

The phrase "overcoming the odds" is an understatement for 24-year-old Joshua Burgess. Though born with congenital rubella syndrome, which has caused him to suffer from a number of health challenges over the years, he continues to break barriers. On September 28, Burgess participated in the prestigious UNESCO Information for All Programme's (IFAP) Second Artificial Intelligence for Information Accessibility (AI4IA) Conference, where he spoke about'Openness and Inclusivity for the Disabled Community in a New Era'. "My presentation reflected my views as a young, blind Jamaican also living with chronic hearing loss. It was important for me to note that, while I have benefited from artificial intelligence's (AI) ability to help me integrate into society, it is also important for us to recognise that it is not a one-size-fits-all. We must collaborate with key stakeholders to ensure openness, inclusivity, fairness, and accessibility for everyone," said Burgess.

Recent advances in Internet of Things (IoT) technologies and the reduction in the cost of sensors have encouraged the development of smart environments, such as smart homes. Smart homes can offer home assistance services to improve the quality of life, autonomy and health of their residents, especially for the elderly and dependent. To provide such services, a smart home must be able to understand the daily activities of its residents. Techniques for recognizing human activity in smart homes are advancing daily. But new challenges are emerging every day. In this paper, we present recent algorithms, works, challenges and taxonomy of the field of human activity recognition in a smart home through ambient sensors. Moreover, since activity recognition in smart homes is a young field, we raise specific problems, missing and needed contributions. But also propose directions, research opportunities and solutions to accelerate advances in this field.

Machine question answering is an essential yet challenging task in natural language processing. Recently, Pre-trained Contextual Embeddings (PCE) models like Bidirectional Encoder Representations from Transformers (BERT) and A Lite BERT (ALBERT) have attracted lots of attention due to their great performance in a wide range of NLP tasks. In our Paper, we utilized the fine-tuned ALBERT models and implemented combinations of additional layers (e.g. attention layer, RNN layer) on top of them to improve model performance on Stanford Question Answering Dataset (SQuAD 2.0). We implemented four different models with different layers on top of ALBERT-base model, and two other models based on ALBERT-xlarge and ALBERT-xxlarge. We compared their performance to our baseline model ALBERT-base-v2 + ALBERT-SQuAD-out with details. Our best-performing individual model is ALBERT-xxlarge + ALBERT-SQuAD-out, which achieved an F1 score of 88.435 on the dev set. Furthermore, we have implemented three different ensemble algorithms to boost overall performance. By passing in several best-performing models' results into our weighted voting ensemble algorithm, our final result ranks first on the Stanford CS224N Test PCE SQuAD Leaderboard with F1 = 90.123.

Recent work in simultaneous machine translation is often trained with conventional full sentence translation corpora, leading to either excessive latency or necessity to anticipate as-yet-unarrived words, when dealing with a language pair whose word orders significantly differ. This is unlike human simultaneous interpreters who produce largely monotonic translations at the expense of the grammaticality of a sentence being translated. In this paper, we thus propose an algorithm to reorder and refine the target side of a full sentence translation corpus, so that the words/phrases between the source and target sentences are aligned largely monotonically, using word alignment and non-autoregressive neural machine translation. We then train a widely used wait-k simultaneous translation model on this reordered-and-refined corpus. The proposed approach improves BLEU scores and resulting translations exhibit enhanced monotonicity with source sentences.

In recent years, transformer models have revolutionized Natural Language Processing (NLP) and also show promising performance on Computer Vision (CV) tasks. Despite their effectiveness, transformers' attention operations are hard to accelerate due to complicated data movement and quadratic computational complexity, prohibiting the real-time inference on resource-constrained edge-computing platforms. To tackle this challenge, we propose Energon, an algorithm-architecture co-design approach that accelerates various transformers using dynamic sparse attention. With the observation that attention results only depend on a few important query-key pairs, we propose a multi-round filtering algorithm to dynamically identify such pairs at runtime. We adopt low bitwidth in each filtering round and only use high-precision tensors in the attention stage to reduce overall complexity. By this means, we significantly mitigate the computational cost with negligible accuracy loss. To enable such an algorithm with lower latency and better energy-efficiency, we also propose an Energon co-processor architecture. Elaborated pipelines and specialized optimizations jointly boost the performance and reduce power consumption. Extensive experiments on both NLP and CV benchmarks demonstrate that Energon achieves $161\times$ and $8.4\times$ geo-mean speedup and up to $10^4\times$ and $10^3\times$ energy reduction compared with Intel Xeon 5220 CPU and NVIDIA V100 GPU. Compared to state-of-the-art attention accelerators SpAtten and $A^3$, Energon also achieves $1.7\times, 1.25\times$ speedup and $1.6 \times, 1.5\times $ higher energy efficiency.

We present an approach for representing abstract argumentation frameworks based on an encoding into classical higher-order logic. This provides a uniform framework for computer-assisted assessment of abstract argumentation frameworks using interactive and automated reasoning tools. This enables the formal analysis and verification of meta-theoretical properties as well as the flexible generation of extensions and labellings with respect to well-known argumentation semantics.

Improving the resilience of a network protects the system from natural disasters and malicious attacks. This is typically achieved by introducing new edges, which however may reach beyond the maximum number of connections a node could sustain. Many studies then resort to the degree-preserving operation of rewiring, which swaps existing edges $AC, BD$ to new edges $AB, CD$. A significant line of studies focuses on this technique for theoretical and practical results while leaving three limitations: network utility loss, local optimality, and transductivity. In this paper, we propose ResiNet, a reinforcement learning (RL)-based framework to discover resilient network topologies against various disasters and attacks. ResiNet is objective agnostic which allows the utility to be balanced by incorporating it into the objective function. The local optimality, typically seen in greedy algorithms, is addressed by casting the cumulative resilience gain into a sequential decision process of step-wise rewiring. The transductivity, which refers to the necessity to run a computationally intensive optimization for each input graph, is lifted by our variant of RL with auto-regressive permutation-invariant variable action space. ResiNet is armed by our technical innovation, Filtration enhanced GNN (FireGNN), which distinguishes graphs with minor differences. It is thus possible for ResiNet to capture local structure changes and adapt its decision among consecutive graphs, which is known to be infeasible for GNN. Extensive experiments demonstrate that with a small number of rewiring operations, ResiNet achieves a near-optimal resilience gain on multiple graphs while balancing the utility, with a large margin compared to existing approaches.