The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that require lightweight models for one-class classification. Building on Ilyas et al. (2019), we investigate the vulnerability of lightweight one-class classifiers to adversarial attacks and possible reasons for it. Our results show that lightweight one-class classifiers learn features that are not robust (e.g. texture) under stronger attacks. However, unlike in multi-class classification (Ilyas et al., 2019), these non-robust features are not always useful for the one-class task, suggesting that learning these unpredictive and non-robust features is an unwanted consequence of training.

Data-driven approaches and paradigms have become promising solutions to efficient network performances through optimization. These approaches focus on state-of-the-art machine learning techniques that can address the needs of 5G networks and the networks of tomorrow, such as proactive load balancing. In contrast to model-based approaches, data-driven approaches do not need accurate models to tackle the target problem, and their associated architectures provide a flexibility of available system parameters that improve the feasibility of learning-based algorithms in mobile wireless networks. The work presented in this paper focuses on demonstrating a working system prototype of the 5G Core (5GC) network and the Network Data Analytics Function (NWDAF) used to bring the benefits of data-driven techniques to fruition. Analyses of the network-generated data explore core intra-network interactions through unsupervised learning, clustering, and evaluate these results as insights for future opportunities and works.

Batch normalization (BN) has been widely used in modern deep neural networks (DNNs) due to fast convergence. BN is observed to increase the model accuracy while at the cost of adversarial robustness. We conjecture that the increased adversarial vulnerability is caused by BN shifting the model to rely more on non-robust features (NRFs). Our exploration finds that other normalization techniques also increase adversarial vulnerability and our conjecture is also supported by analyzing the model corruption robustness and feature transferability. With a classifier DNN defined as a feature set $F$ we propose a framework for disentangling $F$ robust usefulness into $F$ usefulness and $F$ robustness. We adopt a local linearity based metric, termed LIGS, to define and quantify $F$ robustness. Measuring the $F$ robustness with the LIGS provides direct insight on the feature robustness shift independent of usefulness. Moreover, the LIGS trend during the whole training stage sheds light on the order of learned features, i.e. from RFs (robust features) to NRFs, or vice versa. Our work analyzes how BN and other factors influence the DNN from the feature perspective. Prior works mainly adopt accuracy to evaluate their influence regarding $F$ usefulness, while we believe evaluating $F$ robustness is equally important, for which our work fills the gap.

We're officially a month into 2020 and the new decade is well underway. So much so, it is worth reflecting back as it jolted our eyes open and set the stage for what's to come. To sum it up in a word, data. Data, data everywhere – how to get it, how to use it, how to see it. Everywhere you looked there were analytics dashboards.

There was plenty to behold at the National Retail Federation's Big Show, hosted at the Javits Center in New York City from Jan. 14-16. After years of "omnichannel" being the buzzword du jour, "artificial intelligence" (AI), "machine learning," "computer vision," and "chatbot" were the mantra for exhibitors and attendees alike. But there was evidence aplenty that other emerging technologies are further penetrating the enterprise and influencing not only the way business will get done in the years ahead but also how consumers will interact with brands. To date, IBM has invested $25 million in blockchain R&D, which has produced a real-world application focused on food safety and developed in partnership with heavyweights ranging from Tyson Foods and Unilever to Kroger, Nestlé and Dole. Walmart built its own food safety platform on the backbone of IBM's blockchain technology.

Several new innovations that change the way retailers manage inventory and consumers purchase products were on display at the National Retail Federation's annual trade show, The New York Times reported. The convention floor included displays of alert systems programmed to identify heavy-spending customers, smart shelves that can track inventory in real time and robots for supply chain applications. During the three-day event, retail industry leaders discussed artificial intelligence, Big Data and automation. Drawing more than 600 exhibitors, the convention featured sessions with leaders from Walmart, Best Buy, Neiman Marcus and other big-name merchants. According to technology on display, certain consumers will soon be able to test-drive or purchase vehicles without any human contact, using their mobile phones at a garage that doubles as a vending machine.

The Singapore Government is stepping up investments in artificial intelligence (AI) to better counter cyber threats from hackers who are also increasingly using AI to vary their strategies. As much as $528 million, or 22 per cent, of Singapore's tech budget this year - the most ever - has been set aside for security software and systems, particularly AI-enabled ones. A huge part of the security budget will go to the first Government Security Operations Centre, which will feature AI and the analytics smarts to detect cyber threats. Separately, the National Research Foundation (NRF) launched a programme in May to boost the use of AI in Singapore, to solve nationwide problems in areas such as finance, city management solutions and healthcare. NRF will invest up to $150 million over five years in this new initiative, dubbed AI.SG.

UP to S$150 million will be committed to AI.SG, a new national programme that seeks to boost Singapore's capabilities in artificial intelligence (AI). The National Research Foundation (NRF) will invest the amount over the next five years. Minister for Communications and Information Yaacob Ibrahim said on Wednesday that AI and data science are among key frontier technologies the Singapore government will harness as part of its "enhanced growth strategy for the digital economy". He was speaking at Innovfest Unbound 2017, a two-day innovation festival celebrating digital disruption and which has attracted more than 8,000 global entrepreneurs and representatives from government agencies and corporates. Dr Yaacob said: "The potential gains from an enabler technology like AI are massive."

The National Research Foundation (NRF) Singapore will launch AI.SG, a national programme in Artificial Intelligence (AI) to catalyze, synergize and boost Singapore's AI capabilities. Up to $150 million will be invested in AI.SG over the next five years. The initiative will be driven by a government-wide partnership comprising NRF, the Smart Nation and Digital Government Office (SNDGO), the Economic Development Board (EDB), the Infocomm Media Development Authority (IMDA), SGInnovate, and the Integrated Health Information Systems (IHiS). AI:SG will bring together research institutions, AI start-ups and companies developing AI products, to grow knowledge, create tools and develop talent to power Singapore's AI efforts. For example, AI can be used to increase traffic throughput during peak hours, or to address healthcare challenges that are to come with an ageing population.

This week at the National Retail Federation's (NRF) annual Big Show in New York City, Microsoft is unveiling the latest digital innovations that are transforming the shopping experience for customers – at every step of their journey, across every channel. While personalization, omnichannel and customer-centricity are not new themes, retailers are more aware than ever that competing in today's world requires emphasis on the entire customer journey. Technology and culture change will be required at all levels of a retail organization to provide the experiences customers expect. We call this digital transformation, and many of our retail customers are already embracing this shift with help from technologies such as Microsoft Azure, Power BI and Dynamics 365, as well as solutions from our global network of partners. The hardware and software on display in our booth at NRF this week from these customers and partners are impressive -- including store-scanning robots, mobile apps, intelligent vending machines and smart shelves. Here are some of the solutions Microsoft customers and partners are implementing that use technology in new ways, from the warehouse to checkout.