“Patent examiners face a time-consuming challenge of accurately determining the novelty and nonobviousness of a patent application by sifting through ever-expanding amounts of ‘prior art,’” or inventions that have come before, the researchers explain. It’s challenging work. Compounding the challenge: patent applicants are permitted by law to create hyphenated words and assign new meaning to existing words to describe their inventions. It’s an opportunity, the researchers explain, for applicants to strategically write their applications in a strategic, ML-targeting way. The U.S. Patent and Trademark Office is generally wise to this. It has invited in ML technology that “reads” the text of applications, with the goal of spotting the most relevant prior art quicker and leading to more accurate decisions.. “Although it is theoretically feasible for ML algorithms to continually learn and correct for ways that patent applicants attempt to manipulate the algorithm, the potential for patent applicants to dynamically update their writing strategies makes it practically impossible to adversarially train an ML algorithm to correct for this behavior,” the researchers write. In its study, the team conducted observational and experimental research.
The cloud-testing life cycle includes the following activities. Test Manager/Project manager/Test leader plays the role of Test admin. Test admin creates the test scenarios and designs test cases. Based on the scenarios and test cases, automated test script will be generated either by test admin or professional tester. Once a cloud service provider is available to test admin, he creates the user to give access to testers. Cloud-service provider set-up the infrastructure. Test users/Testers use the credentials to log in to the portal and can use all the assets available. The Cloud testing process starts here. Testers perform the testing. After completion of the process, cloud testing providers deliver the result. Testing firewalls and load balancers involve the expenditure on hardware, software, and maintenance. In the case of applications where the rate of increase in some users is unpredictable or there is variation in deployment environment depending on client requirements, cloud testing is more effective. Software testing has also undergone a long-drawn evolution cycle. From ad-hoc practices within different business units, it gradually evolved into a centralized Managed Test Center approach.
"XORDDoS and Kaiji have been known to leverage telnet and SSH for spreading before, so I see Docker as a new vector which increases the potential of the botnet, a green field full of fresh fruit to pick with no immediate competitors," Pascal Geenens, cybersecurity evangelist at Radware, told ZDNet via email earlier this week. "Docker containers will typically provide more resources compared to IoT devices, but they typically run in a more secured environment, and it might be hard to impossible for the container to perform DDoS attacks," Geenens added. "The unique perspective of IoT devices such as routers and IP cameras is that they have unrestricted access to the internet, but typically with less bandwidth and less horsepower compared to containers in a compromised environment," the Radware researcher told ZDNet. "Containers, on the other hand, typically have access to way more resources in terms of memory, CPU, and network, but the network resources might be limited to only one or a few protocols, resulting in a smaller arsenal of DDoS attack vectors supported by those 'super' bots."
What are CIOs today thinking about now as they plan for the rest of the year ahead at this point? How do they assess the effort to move workers from an office environment to work from home? What are some of the other plans they are laying out for the rest of 2020, a year that so far includes a pandemic, civil unrest due to systemic racism, a recession, massive unemployment, and a presidential election? ... "Every industry is grappling with this pandemic in a different way," Nix said. For instance, higher education has had to create a completely different education model for students, and now there's the question of whether they will come back to physical classrooms or stay with remote education, or do some kind of combination of the two, he said. "COVID-19 led to an unprecedented remote work transformation with challenges in productivity and security at scale that had never been anticipated," Nix said. Most CIOs, 77%, said they are reducing their budgets due to the crisis, and 74% said they are prioritizing initiatives that drive operational efficiency. If you want to know where those priorities are right now, just look at some of the challenges that CIOs say their organizations have faced due to the crisis and IT teams enabling the effort to work from home.
Once the technology to improve business service has been considered, it needs to be implemented in order for the business to begin seeing a positive impact. The organisation may have been running an advanced digital transformation programme in concert prior to the pandemic. However, this will now have to be re-assessed against the backdrop of what changes the core business is undergoing in terms of the products and services it provides and how those are procured and consumed by the customer going forward. The sharp switch of retail from high street to online drives a whole wake of impact behind it in terms of web presence, advertising, inventory management, distribution, staffing, brand awareness, manufacturing, transport – and that is just one industry. This obviously puts a different strain on the IT function as new apps and microservices have to be rushed into production and delivered on new platforms, whilst the legacy apps either get parked in a museum corner for now or resources rapidly found and deployed to modify them. There will need to be a big focus on agility as we enter an unknown period when the lockdown begins to loosen.
There are a lot of different ideas about why the innovation slowdown happened. Perhaps the kinds of inventions that previously transformed the economy—like computers and the internet, or before that the internal-combustion engine—stopped coming along. Or perhaps we just haven’t yet learned how to use the newest technologies, like artificial intelligence, to improve productivity in many sectors. But one likely factor is that governments in many countries have significantly cut investments in technology since the 1980s. Government-funded R&D in the US, says John Van Reenen, an economist at MIT, has dropped from 1.8% of GDP in the mid-1960s, when it was at its peak, to 0.7% now (chart 1). Governments tend to fund high-risk research that companies can’t afford, and it’s out of such research that radical new technologies often arise. The problem with letting private investment alone drive innovation is that the money is skewed toward the most lucrative markets. The biggest practical uses of AI have been to optimize things like web search, ad targeting, speech and face recognition, and retail sales. Pharmaceutical research has largely targeted the search for new blockbuster drugs.
The emergence of IoT and intelligent technologies, including mobile and online banking, is critical to improve customer engagement and make the everyday services clients require to run smoothly. And to stay up-to-speed with a constantly shifting risk landscape and progressing threats, financial institutions must not only plan for today, but also look ahead to ensure the use of the most innovative, yet proven technologies and solutions. As new trends and strategies emerge and take precedence, security leaders should stay prepared and continuously work to gather as much data and intelligence as possible to modernize, simplify, and automate their business. Most financial organizations are looking to leverage technologies to achieve common goals: satisfactory customer engagement, enhanced security, and fraud reduction. Moving forward, banks need to consider how these efforts can be significantly affected by the power of IoT. ... For banks to invest in technology, solutions must allow security teams and investigators to dedicate time and effort to relevant tasks and efficient responses, while leaving certain operations, such as firmware updates and camera verification, up to automation.
Network resilience strategies should be dynamic, and times are changing. It’s not always possible to get onsite all the time. New innovations like IoT are pushing edge deployments with more local processing power at satellite sites. SD-WAN, a now widespread tool, introduces more points of failure via increased software stacks susceptible to buggy firmware updates and other disruptions. Organizations will need to implement smart best practices to ensure network management rises to meet not only the challenges of today, but also tomorrow. But how do we get there? Here are some tips to help get this done. Automation not only eases technician workloads, it also adds security to critical network devices. Some ways it can bolster security include constant event logging with automated analysis and alerts and continuous updates for items like back-up images or firmware update scripts. Automation also makes new site configuration secure, remote and instantaneous with benefits like zero-touch provisioning. Many organizations must constantly update networking functions.
If you don't already have a formal work from home policy, now is the time to develop one. If you already have a work from home policy, you should plan to review it. Once developed or reviewed, work from home policies should be disseminated to employees, so they understand the conditions of working safely and securely from home. An IT work from home policy should minimally mandate strong password selection and no sharing of passwords. The policy should instruct employees about what they should do if their devices are lost or misplaced and inform employees of the methods they should use when they need to transfer or store files. Storing files on local drives at home should be discouraged in favor of storing these assets on the cloud under company management. ... Data encryption and multi-factor authentication should be used if it is necessary to stream or transfer any company-sensitive information or intellectual property. The “catch” with this is that many employees don’t know which information they are working with is intellectual property, so they may inadvertently send information to parties who should not have it.
Effective Data Management can reduce errors by using the MDM as the accurate master copy for the organization’s most important information. This helps ensure any applications built using master data are accurate and effective. However, managing data efficiently requires more than MDM. The organization of data needs to line up with the organization’s business strategy and what data the company needs to move forward. The challenge most Data Managers face is how to best use Analytics and how to integrate Analytics with business processes. Integrating Analytics with Data Management will assure a higher degree of success in Analytics projects. When archiving data, a business should use a storage system capable of supporting data discovery, access, and distribution, and when data archiving, regulations and policies must be considered. Data is also subject to quality control, which might involve double-checking manually-entered data through the use of quality level flags designed to indicate potential problems and check format consistency. Additionally, data should be documented, defining its context, content, and parameters.
Quote for the day:
“I’m convinced that about half of what separates successful entrepreneurs from non-successful ones is pure perseverance.” -- Steve Jobs