Accidental complexity in software design isn't always so severe, but even in mild scenarios, it does annoy users and waste time, Buhle said. Website sign-up flows, for example, ask for too much information, turning away frustrated prospects before they finish the form. Usually this bad UX manifests because the company thinks of more questions to which it would love to know the answers. However, the answers don't affect how the user will experience the site. Another common example occurs in software menu systems. Rather than create menus that match how users think, designers derive ideas from internal business units and create menus accordingly, Buhle said. You don't want your menu to match your org chart. Confused users will fail to find critical functions when they need them. To avoid these kinds of UI complexity, test products with real users. User acceptance testing is not always as easy as it sounds. Professional UX researchers should analyze how users interact with prototypes and existing products. Designers, engineers and especially startup founders do a terrible job in this role. They are invested in the products and lack training on how to conduct unbiased tests, Buhle said.
For the last decade, business intelligence has been used to gain insight from historical data, but until recently, these analytical techniques have been mainly manual. This is changing and Wayne Butterfield, director at global technology research and advisory firm, ISG, explains that business leaders are welcoming “the promise of artificial intelligence (AI) to both remove the manual process and improve the quality of insight.” He says: “Data-driven insights — using historical data to predict future outcomes — combine data, advanced analytics and AI to transform decision making, based on predictive insights in areas like revenue, demand and supply. “It’s still early days, but auto machine learning (AutoML) technologies are lowering the barrier to entry for organisations that may not have large teams of data scientists, but that still see the value in looking forward and not backwards with their data.” Pointing to AutoML tools, like Kortical.io and Data Robot, Butterfield explains that these are “becoming more popular in automation centres of excellence, as advanced AI models are plunged into the relatively simple robotic process automation-type processes, to take action based on these predictions.”
"The shift to subscriptions will shield cybersecurity from immediate IT spending cuts, but additional expenditure will be affected for the rest of the year as organizations begin the next stage in their response to the pandemic," Matthew Ball, chief analyst at Canalys, said in a press release. "The switch from free trials to paid-for subscriptions will be a factor in maintaining cybersecurity growth. But the mix of cost-containment measures, workforce reduction, and cashflow issues will result in greater scrutiny of existing projects and smaller deals." Increases in spending will vary among different security products and services, according to Canalys. Investments in endpoint security will grow as remote working conditions continue. But this growth may taper off following the strong spending during the first quarter, especially among small and midsized businesses. Network security will remain the largest segment, accounting for 36% of all cybersecurity spending. However, this area may see a decrease in spending as organizations de-emphasize traditional appliance-based perimeter defenses. Organizations will have to beef up spending in other segments to address new vulnerabilities created by a remote and decentralized workforce.
Machine Learning in Action in Finance: Using Graphical Lasso to Identify Trading Pairs in International Stock ETFs
Remember the regression method called lasso, used to induce a sparse solution to your regression problem by adding an L1 regularization term? Graphical lasso is its extension to the world of graphs. Instead of estimating coefficients for independent variables in regression problems, graphical lasso estimates the precision (inverse covariance) matrix of your data. Thus, instead of driving many of the coefficients to 0 as in lasso regression, it pushes many values in the matrix to 0. Why ‘graphical’? Because the precision matrix can be shown to correspond uniquely to an undirected graph(more on this in later sections). In other words, the goal of graphical lasso is to induce from your data an undirected graph with sparse connections. This fact will come handy later when we try to illustrate the ETF graph and identify possible clusters. For a more mathematically concrete treatment of the algorithm, please refer to this Wikipedia article: https://en.wikipedia.org/wiki/Graphical_lasso for now, or stay tuned in for my second article in this series, which analyzes the algorithm step by step. In this experiment, let us use the daily closing prices of the tickers. We will use historical data provided by Yahoo Finance.
Privacy and security are a key battle in the browser wars as users become increasingly aware of what’s happening to their data. This is even more important as people work from home during the pandemic—and Edge has been aware of this when launching recent feature updates. But there are some obstacles to Edge being seen as a privacy-based browser. A few months ago, Edge came under fire for privacy violations, and its move to bring the browser to Windows has irked some users. Meanwhile, Chrome recently introduced new featured to help address user concerns about security and privacy. But a new report by NSS Labs actually saw Microsoft’s Edge beat Chrome in the security stakes. Because it uses Microsoft Defender SmartScreen, Edge was found to offer the best phishing protection compared with the other browsers tested, blocking 95.5% of phishing URLs. Google, which uses the Safe Browsing API, came second at 86.9%. As Microsoft focused site OnMsft reports, another separate NSS Labs report shows how Edge also has better malware protection than rivals Chrome, Firefox and Opera. Microsoft Edge blocks 98.5% of malware, while second place Firefox blocks an average of 86.1%, followed by Google Chrome at 86.0%.
Scales said he doesn't think there's a single person at Cochrane who doesn't recognize this is a potential issue. It's more a question of what to do about it, and a consensus does not exist. Scales referred to data science experience to establish that trying to weed out bias out of data is extremely difficult. "You essentially have to pick which biases you want. Or at least try to be as transparent as possible about what biases might be there, or make the data and metadata as transparent as possible, so other people can look through it to decide what the biases are," Scales said. There is several suggested solutions along these lines, Scales added. Some people suggest that more public money be put into RCTs because they're essentially a public good. A way to reduce bias is to make sure that non-biased studies are set up. Using public money to do those studies could help ensure there's not one particular interest being represented. Others point out the fact RCTs can be enormous multi-year undertakings that get summarized in what's often an eight-page journal article. Many important details and potential biases are being left out. Registries hosting all of the information from these trials would enable digging into the weeds and deciding whether there are any additional biases from the original raw data.
Worldwide spending on digital transformation technologies and services will reach $2.3 trillion in 2023, forecasts IDC in its Worldwide Semiannual Digital Transformation Spending Guide. That will mark an important milestone, notes Craig Simpson, research manager at IDC’s Customer Insights and Analysis Group: It will be the first time digital transformation will account for the majority of IT spending (53 percent) in the IDC forecast. Measuring the return on this increasingly significant category of the technology budget is tricky since these initiatives transcend functional and business boundaries and take time to yield results. But IT leaders can take a number of actions to boost the long-term value that their organizations derive from digital transformation dollars. Taking these steps is especially important now for IT leaders, who face greater funding hurdles as the pandemic puts pressure on technology budgets. “Return on investment (ROI) analysis is an important component of the business justification behind any digital transformation effort,” says Elizabeth Ebert, IT advisory lead for North America at IT consultancy and service provider Avanade.
There are a small number of really important cybersecurity hygiene actions, so think about it in the current climate as washing your hands from a cybersecurity perspective, that businesses can do to really eliminate the risk associated with a lot of common cybersecurity threats. So some examples of this are enabling strong multi-factor authentication or ensuring that you're rapidly patching all of your devices to it to inoculate them against known vulnerabilities, to prevent things like ransomware attacks. And then finally, treating cybersecurity like a team sport, building a culture of awareness in your company so that all the employees in your company can act like security trailblazers. ... One of the concepts that I think sometimes gets lost in these security conversations is the concept of ethics and how data is used, and I know these overlap quite a bit. What's the role in working with people who are looking at the ethical use of data? So you maybe have something like least privileged required, a concept of saying, "Hey, look, for security purposes, only a certain number of industries or with certain roles need to have access to this data."
Some translations specify kitchen scales, some say that it was a pot of some kind, but it’s not a weighty detail, so to speak. What’s important is that the curious Kasim’s wife smears the bottom of the instrument with honey (suet in some translations) to find out why her relative needs it all of a sudden. And when it’s returned, lo and behold, a gold coin is stuck to the bottom — which means that her sister-in-law was using it to count gold! Even a cyberdunce can see that the author is describing a spyware module integrated into a legitimate product. Kasim’s wife provides a device (under the Measure-as-a-Service model) and spies on the activity of the client. The clear moral of the story is: Use tools from trusted sources — and check them for vulnerabilities and malicious implants. ... One of the gang members marks the gate of Kasim’s house, where Ali Baba now lives, and returns with his associates that night to slaughter its occupants. However, the cunning Marjaneh spots the sign and marks the gates of all of the other houses on the street in exactly the same way, thereby foiling the attack.
Every software system is a solution to a problem. But if we start with assumptions about the solution rather than clear statements of problems, we may never figure out the best use of time and resources to provide value to customers. Even if we had a crystal ball and knew exactly how to solve our users’ problems, it would still not be enough. We also have to know how to get there—the increments of value to be delivered along the way. We need to find stepping stones to a final product, and each stone must align to something a customer wants. How do we do this? Again, we try to work from indivisible elements. I like to call these “semantic primitives.” We want these, our raw materials, to be discreet and independently evaluable. Again, “don’t mix the paint!” These are implemented in various ways. The word “requirements” gets a lot of hate these days. “User stories” are popular, but “use cases” have fallen out of fashion. After a blog post on Medium, “jobs-to-be-done” became “the framework of customer needs” seemingly overnight. Regardless of how you conceive them, the purpose is the same: to serve as building blocks for understanding the problem we want to solve and to help us be creative as we move along our product journey.
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