Corruption continues to be one of the biggest societal challenges of our time. New hope is placed in Artificial Intelligence (AI) to serve as an unbiased anti-corruption agent. Ever more available (open) government data paired with unprecedented performance of such algorithms render AI the next frontier in anti-corruption. Summarizing existing efforts to use AI-based anti-corruption tools (AI-ACT), we introduce a conceptual framework to advance research and policy. It outlines why AI presents a unique tool for top-down and bottom-up anti-corruption approaches. For both approaches, we outline in detail how AI-ACT present different potentials and pitfalls for (a) input data, (b) algorithmic design, and (c) institutional implementation. Finally, we venture a look into the future and flesh out key questions that need to be addressed to develop AI-ACT while considering citizens' views, hence putting "society in the loop".

Since the outbreak of the pandemic, the world has grown increasingly reliant on artificial intelligence (AI) technologies. Thousands of new innovations -- from contact-tracing apps to the drones delivering medical equipment -- sprang up to help us meet the challenges of Covid-19 and life under lockdown. The unprecedented speed with which a vaccine for Covid-19 was discovered can partly be attributed to the use of AI algorithms which rapidly crunched the data from thousands of clinical trials, allowing researchers around the world to compare notes in real time. As Satya Nadella, the chief executive of Microsoft observed, in just two months, the world witnessed a rate of digital transition we'd usually only see in two years. In 2017, PWC published a study showing that adoption of AI technologies could increase global GDP by 14% by 2030. In addition to creating jobs and boosting economies, AI technologies have the potential to drive sustainable development and even out inequalities, democratising access to healthcare and education, mitigating the effects of climate change and making food production and distribution more efficient.

We deploy and demonstrate the CoinTossX low-latency, high-throughput, open-source matching engine with orders sent using the Julia and Python languages. We show how this can be deployed for small-scale local desk-top testing and discuss a larger scale, but local hosting, with multiple traded instruments managed concurrently and managed by multiple clients. We then demonstrate a cloud based deployment using Microsoft Azure, with large-scale industrial and simulation research use cases in mind. The system is exposed and interacted with via sockets using UDP SBE message protocols and can be monitored using a simple web browser interface using HTTP. We give examples showing how orders can be be sent to the system and market data feeds monitored using the Julia and Python languages. The system is developed in Java with orders submitted as binary encodings (SBE) via UDP protocols using the Aeron Media Driver as the low-latency, high throughput message transport. The system separates the order-generation and simulation environments e.g. agent-based model simulation, from the matching of orders, data-feeds and various modularised components of the order-book system. This ensures a more natural and realistic asynchronicity between events generating orders, and the events associated with order-book dynamics and market data-feeds. We promote the use of Julia as the preferred order submission and simulation environment.

Individual behavior and decisions are substantially influenced by their contexts, such as location, environment, and time. Changes along these dimensions can be readily observed in Multiplayer Online Battle Arena games (MOBA), where players face different in-game settings for each match and are subject to frequent game patches. Existing methods utilizing contextual information generalize the effect of a context over the entire population, but contextual information tailored to each individual can be more effective. To achieve this, we present the Neural Individualized Context-aware Embeddings (NICE) model for predicting user performance and game outcomes. Our proposed method identifies individual behavioral differences in different contexts by learning latent representations of users and contexts through non-negative tensor factorization. Using a dataset from the MOBA game League of Legends, we demonstrate that our model substantially improves the prediction of winning outcome, individual user performance, and user engagement.

Transformers are arguably the main workhorse in recent Natural Language Processing research. By definition a Transformer is invariant with respect to reorderings of the input. However, language is inherently sequential and word order is essential to the semantics and syntax of an utterance. In this paper, we provide an overview of common methods to incorporate position information into Transformer models. The objectives of this survey are to i) showcase that position information in Transformer is a vibrant and extensive research area; ii) enable the reader to compare existing methods by providing a unified notation and meaningful clustering; iii) indicate what characteristics of an application should be taken into account when selecting a position encoding; iv) provide stimuli for future research. The Transformer model as introduced by Vaswani et al. (2017) has been found to perform well for many tasks, such as machine translation or language modeling. With the rise of pretrained language models (PLMs) (Peters et al., 2018; Howard & Ruder, 2018; Devlin et al., 2019; Brown et al., 2020) Transformer models have become even more popular. As a result they are at the core of many state of the art natural language processing (NLP) models. A Transformer model consists of several layers, or blocks. Each layer is a self-attention (Vaswani et al., 2017) module followed by a feed-forward layer. Layer normalization and residual connections are additional components of a layer.

Violence, whether it is focused on individuals such as crime or at larger scales such as war, seems to be almost inevitable in our world. However, researchers are asking if it can be prevented and if geospatial big data techniques, including machine learning methods, could potentially be used to prevent violence from going out of control. A recent World Economic Form blog has highlighted varied efforts that attempt to mitigate violence at different scales, with geospatial data often a core feature of different methods discussed in these tools.[1] Two tools have been recently developed that focus on small-scale acts of violence and harassment. Using crowdsourcing and hot spot mapping, Safecity and HarassMap[2] have been created, which depict recent trends of assaults, sexual harassment, and local crime to help individuals determine areas to avoid.

In computer vision, object detection is the problem of locating one or more objects in an image. Besides the traditional object detection techniques, advanced deep learning models like R-CNN and YOLO can achieve impressive detection over different types of objects. These models accept an image as the input and return the coordinates of the bounding box around each detected object. This tutorial discusses the confusion matrix, and how the precision, recall and accuracy are calculated. In another tutorial, the mAP will be discussed. In binary classification each input sample is assigned to one of two classes. Generally these two classes are assigned labels like 1 and 0, or positive and negative.

With the recent advances of the Internet of Things, and the increasing accessibility of ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as relationship explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as Artificial Social Intelligence (ASI) that has the potential to tackle the social relationship explosion problem. This paper discusses the role of IoT in social relationships detection and management, the problem of social relationships explosion in IoT and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

Product title compression for voice and mobile commerce is a well studied problem with several supervised models proposed so far. However these models have 2 major limitations; they are not designed to generate compressions dynamically based on cues at inference time, and they do not transfer well to different categories at test time. To address these shortcomings we model title compression as a meta learning problem where we ask can we learn a title compression model given only 1 example compression? We adopt an unsupervised approach to meta training by proposing an automatic task generation algorithm that models the observed label generation process as the outcome of 4 unobserved processes. We create parameterized approximations to each of these 4 latent processes to get a principled way of generating random compression rules, which are treated as different tasks. For our main meta learner, we use 2 models; M1 and M2. M1 is a task agnostic embedding generator whose output feeds into M2 which is a task specific label generator. We pre-train M1 on a novel unsupervised segment rank prediction task that allows us to treat M1 as a segment generator that also learns to rank segments during the meta-training process. Our experiments on 16000 crowd generated meta-test examples show that our unsupervised meta training regime is able to acquire a learning algorithm for different tasks after seeing only 1 example for each task. Further, we show that our model trained end to end as a black box meta learner, outperforms non parametric approaches. Our best model obtains an F1 score of 0.8412, beating the baseline by a large margin of 25 F1 points.

Your brain has been altered, neurologically rewired as you acquired a particular skill. This renovation has left you with a specialized area in your left ventral occipital temporal region, shifted facial recognition into your right hemisphere, reduced your inclination toward holistic visual processing, increased your verbal memory, and thickened your corpus callosum, which is the information highway that connects the left and right hemispheres of your brain. You are likely highly literate. As you learned to read, probably as a child, your brain reorganized itself to better accommodate your efforts, which had both functional and inadvertent consequences for your mind. So, to account for these changes to your brain--e.g,