Conflict of interest is the permanent companion of any population of agents (computational or biological). For that reason, the ability to compromise is of paramount importance, making voting a key element of societal mechanisms. One of the voting procedures most often discussed in the literature and, due to its intuitiveness, also conceptually quite appealing is Charles Dodgson's scoring rule, basically using the respective closeness to being a Condorcet winner for evaluating competing alternatives. In this paper, we offer insights on the practical limits of algorithms computing the exact Dodgson scores from a number of votes. While the problem itself is theoretically intractable, this work proposes and analyses five different solutions which try distinct approaches to practically solve the issue in an effective manner. Additionally, three of the discussed procedures can be run in parallel which has the potential of drastically reducing the problem size.

We survey existing rules of thumb, propose novel methods, and comprehensively evaluate a number of solutions to the problem of calculating the cost to serve each location in a single-vehicle transport setting. Cost to serve analysis has applications both strategically and operationally in transportation settings. The problem is formally modeled as the traveling salesperson game (TSG), a cooperative transferable utility game in which agents correspond to locations in a traveling salesperson problem (TSP). The total cost to serve all locations in the TSP is the length of an optimal tour. An allocation divides the total cost among individual locations, thus providing the cost to serve each of them. As one of the most important normative division schemes in cooperative games, the Shapley value gives a principled and fair allocation for a broad variety of games including the TSG. We consider a number of direct and sampling-based procedures for calculating the Shapley value, and prove that approximating the Shapley value of the TSG within a constant factor is NP-hard. Treating the Shapley value as an ideal baseline allocation, we survey six proxies for it that are each relatively easy to compute. Some of these proxies are rules of thumb and some are procedures international delivery companies use(d) as cost allocation methods. We perform an experimental evaluation using synthetic Euclidean games as well as games derived from real-world tours calculated for scenarios involving fast-moving goods; where deliveries are made on a road network every day. We explore several computationally tractable allocation techniques that are good proxies for the Shapley value in problem instances of a size and complexity that is commercially relevant.

Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity only methods which suffer from detection of artificially large number of clusters for Twitter networks.

Studies in recent years have demonstrated that neural organization and structure impact an individual's ability to perform a given task. Specifically, individuals with greater neural efficiency have been shown to outperform those with less organized functional structure. In this work, we compare the predictive ability of properties of neural connectivity on a working memory task. We provide two novel approaches for characterizing functional network connectivity from electroencephalography (EEG), and compare these features to the average power across frequency bands in EEG channels. Our first novel approach represents functional connectivity structure through the distribution of eigenvalues making up channel coherence matrices in multiple frequency bands. Our second approach creates a connectivity network at each frequency band, and assesses variability in average path lengths of connected components and degree across the network. Failures in digit and sentence recall on single trials are detected using a Gaussian classifier for each feature set, at each frequency band. The classifier results are then fused across frequency bands, with the resulting detection performance summarized using the area under the receiver operating characteristic curve (AUC) statistic. Fused AUC results of 0.63/0.58/0.61 for digit recall failure and 0.58/0.59/0.54 for sentence recall failure are obtained from the connectivity structure, graph variability, and channel power features respectively.

The Republican National Convention already seems to have solidified its position as the most dramatic political event in recent memory -- and yes, that's saying a lot. From accusations of plagiarism to a speaker lineup that reads more like a who's who of reality television than the American political system, there's a lot to cover at the 2016 RNC. And at both the Washington Post and Buzzfeed, some of that coverage is coming from bots. While the two publications may seem like rather dissimilar outlets with disparate readerships, both seem to have recognized the value in utilizing bot technology to cover the highly buzzed-about convention. The Washington Post's bot will work with Twitter and Double Robotics, to provide a live stream of the goings-on in Cleveland by way of Periscope. Moreover, readers (or rather, viewers) will be able to ask questions about the convention through a Periscope chat.

Many people spend their childhood peering up into the vast expanse of the sky, dreaming of growing up to become an astronaut. But these dreams could be dashed as the idea of people venturing into space will one day become a distant memory, according to a report published today. Robots will eventually have enough capabilities to replace humans and other animals on space missions, experts have said. Robots will eventually have enough capabilities to replace humans and other animals on space missions, experts have said. Russia is planning to send robots to the ISS to do jobs that are too dangerous for astronauts.

A clash between European Union bureaucracy and artificial intelligence is a plot worthy of a cyberpunk thriller. It will take place in real life in 2018, once some European data protection laws, passed earlier this year, go into effect. And, though we might instinctively be tempted to endorse progress over regulation, the EU is on the side of the angels in this battle. The EU's General Data Protection Regulation and a separate directive contain provisions to protect people against decisions made automatically by algorithms. "The data subject shall have the right not to be subject to a decision based solely on automated processing, including profiling, which produces legal effects concerning him or her or similarly significantly affects him or her," is how the regulation puts it.

LONDON: As you're choking down your latest serving of Trump Clinton Brexit Racism Terrorism Wealth Gap Climate Change Casserole, you could use some good news. Let's start with The Inevitable, the new best-seller by Kevin Kelly, the founder of Wired magazine some 20 years ago and one of our wisest technological prognosticators. "This is the moment that folks in the future will look back at and say, 'Oh to have been alive and well back then!'" Kelly writes. "There has never been a better time with more opportunities, more openings, lower barriers, higher benefit/risk ratios, better returns, greater upside than now. In the mid-2010s, we're getting the first sneak peeks at a bouquet of technologies that can vastly improve the lives of most people on the planet and solve some of our hardest problems – even climate change.

As artificial intelligence and social robotics begin to replace tasks and jobs originally held by highly-trained humans we are entering the uncharted waters of the fourth industrial revolution, or "second machine age". The new machines are very much unlike their predecessors because of they are able to learn. They can thus evolve over time to become better and increase their performance. Optimists suggest that by taking over cognitive but labour-intensive chores the intelligent machines will free human workers to do more "creative" tasks; and by working side by side with us they will boost our imagination to achieve more, using their power for detail, ability to rapidly analyse massive data, and lack of psychological bias. But what is often missing from the current debate are the social and economic roles of companies as employers.

As headlines go, "Brexit leads to robot takeover" sounds like satire. It's up there with Brexit being "the opportunity to create a second Elizabethan Golden Age". Both have been written recently – but I would argue that the former may actually be true. Recessions force companies to make difficult choices to survive. One of the first places to cut costs is the wage bill.