Grid layouts are used by designers to spatially organise user interfaces when sketching and wireframing. However, their design is largely time consuming manual work. This is challenging due to combinatorial explosion and complex objectives, such as alignment, balance, and expectations regarding positions. This paper proposes a novel optimisation approach for the generation of diverse grid-based layouts. Our mixed integer linear programming (MILP) model offers a rigorous yet efficient method for grid generation that ensures packing, alignment, grouping, and preferential positioning of elements. Further, we present techniques for interactive diversification, enhancement, and completion of grid layouts (Figure 1). These capabilities are demonstrated using GRIDS1, a wireframing tool that provides designers with real-time layout suggestions. We report findings from a ratings study (N = 13) and a design study (N = 16), lending evidence for the benefit of computational grid generation during early stages of design.

Arguably, the most important scenario for search technology is lifelong learning and education, both for students and all citizens. Human learning is a complex multidimensional activity, which includes procedural learning (e.g., activity patterns associated with cooking, sports) and knowledge-based learning (e.g., mathematics, genetics). It also includes different levels of learning, such as the ability to solve an individual math problem correctly. It also includes the development of meta-cognitive self-regulatory abilities, such as recognizing the type of problem being solved and whether one is in an error state. These latter types of awareness enable correctly regulating ones approach to solving a problem, and recognizing when one is off track by repairing momentary errors as needed. Later stages of learning enable the generalization of learned skills or information from one context or domain to others such as applying math problem solving to calculations in the wild (e.g., calculation of garden space, engineering calculations required for a structurally sound building).

Mark Wallace 1 and Aldeida Aleti 1 1 Monash University, Wellington Road, Clayton, Vic and 3800, Australia Abstract For most practical optimisation problems local search outperforms random sampling - despite the "No Free Lunch Theorem". This paper introduces a property of search landscapes termed Neighbours' Similar Fitness (NSF) that underlies the good performance of neighbourhood search in terms of local improvement . Though necessary, NSF is not sufficient to ensure that searching for improvement among the neighbours of a good solution is better than random search. The paper introduces an additional (natural) property which supports a general proof that, for NSF landscapes, neighbourhood search beats random search. 1 Introduction Local Search is a successful class of methods used to solve many large complex optimisation problems. A problem (S,f) is defined as a set S of candidate solutions, termed its search space, and a fitness function f that maps candidate solutions to a fitness measure. Many researchers have explored why different forms of local search [ Burke and Kendal, 2014 ] are so effective, and deep theoretical studies have been published on the performance of algorithms on specific classes of problems [ Michiels et al., 2007 ] . Our focus is on challenging problems for which it is hard to find optimal (or just "good") solutions. In section 5 it will also be shown that all the example hard problems (classed as PLS-Complete) in [ Michiels et al., 2007 ] have this same property that solutions thin out towards the optimum.

AI algorithms are not immune to biases. Traditionally, non-experts have little control in uncovering potential social bias (e.g., gender bias) in the algorithms that may impact their lives. We present a preliminary design for an interactive visualization tool CEB to reveal biases in a commonly used AI method, Neural Networks (NN). CEB combines counterfactual examples and abstraction of an NN decision process to empower non-experts to detect bias. This paper presents the design of CEB and initial findings of an expert panel (n=6) with AI, HCI, and Social science experts.

There is growing concern over the potential misuse of artificial intelligence (AI) research. Publishing scientific research can facilitate misuse of the technology, but the research can also contribute to protections against misuse. This paper addresses the balance between these two effects. Our theoretical framework elucidates the factors governing whether the published research will be more useful for attackers or defenders, such as the possibility for adequate defensive measures, or the independent discovery of the knowledge outside of the scientific community. The balance will vary across scientific fields. However, we show that the existing conversation within AI has imported concepts and conclusions from prior debates within computer security over the disclosure of software vulnerabilities. While disclosure of software vulnerabilities often favours defence, this cannot be assumed for AI research. The AI research community should consider concepts and policies from a broad set of adjacent fields, and ultimately needs to craft policy well-suited to its particular challenges.

Colorectal cancer, largely arising from precursor lesions called polyps, remains one of the leading causes of cancer-related death worldwide. Current clinical standards require the resection and histopathological analysis of polyps due to test accuracy and sensitivity of optical biopsy methods falling substantially below recommended levels. In this study, we design a novel capsule network architecture (D-Caps) to improve the viability of optical biopsy of colorectal polyps. Our proposed method introduces several technical novelties including a novel capsule architecture with a capsule-average pooling (CAP) method to improve efficiency in large-scale image classification. We demonstrate improved results over the previous state-of-the-art convolutional neural network (CNN) approach by as much as 43%. This work provides an important benchmark on the new Mayo Polyp dataset, a significantly more challenging and larger dataset than previous polyp studies, with results stratified across all available categories, imaging devices and modalities, and focus modes to promote future direction into AI-driven colorectal cancer screening systems. Code is publicly available at https://github.com/lalonderodney/D-Caps .

But, says Kambhampati, the rapid improvements in deepfake technology means that we will soon have to rely on AI techniques to detect what the human eye cannot. "There is not a 100% foolproof way of identifying deepfakes, not even for AI researchers," Thomas says. "Detection is always going to be an arms race. As people develop more accurate detection algorithms, fakers will develop even more sophisticated frauds." There are non-technical ways to sniff out a deepfake, just like other forms of disinformation. Ask yourself: Who is the person publishing this information?

LAS VEGAS – A connected world means paying for gas with your voice and sensing when a plant needs water. The annual CES technology conference in Las Vegas runs through Friday and offers a forum for big companies and startups to unveil their products and services for the coming year. If swiping a credit card is too much of a chore, you'll soon be able to pay for gas by voice. Later this year, those who have Amazon's voice assistant Alexa in their cars will be able to drive into Exxon and Mobil stations and say, "Alexa, pay for gas." Alexa will then ask you to confirm what station you're at and which pump you're using.

A Japanese language school whose stock soared almost 12-fold last year is planning to expand into new businesses as its chief executive officer tries to keep the rally alive. RareJob Inc., a Tokyo-based online English conversation school that uses teachers in the Philippines, will focus on areas including leadership training and job placement, Gaku Nakamura, the company's founder and chief executive officer, said in an interview. Nakamura said one of his goals is to boost the company's market value to ¥100 billion ($922 million) from its current level of about ¥25 billion. RareJob surged 1,093% in 2019, the second-best performance in Japan's Mothers market of smaller shares, after it surprised investors by saying earnings would jump. Analysts -- and history -- suggest it will be difficult to keep up those gains after the company's valuation exceeded 100 times estimated profit.

WASHINGTON – U.S. lawmakers across the political spectrum called for de-escalation of tensions with Iran Wednesday following back-and-forth airstrikes, but clear divisions remained over President Donald Trump's military strategy with Tehran. Republicans praised the commander in chief for signaling he had no immediate plans to respond militarily hours after Iran's missile strikes on Iraqi bases housing American troops. Iran's riposte followed the death of a top-ranked Iranian commander from a U.S. drone attack. Many Democrats seethed over Trump's unilateral order to kill the Iranian commander, Qassem Soleimani, without congressional consent. But they took heart in both sides appearing to choose de-escalation rather than a war posture.