Generally speaking, usability testing comes in two types: moderated and unmoderated. Moderated sessions are guided by a researcher or a designer, while the unmoderated ones rely on users’ own unassisted efforts. Moderated tests are an excellent choice if you want to observe users interact with prototypes in real-time. This approach is more goal-oriented — it lets you confirm or disconfirm existing hypotheses with more confidence. On the other hand, unmoderated usability tests are convenient when working with a substantial pool of subjects. A large number of participants allows you to identify a broader spectrum of issues and points of view. However, it’s important to underline that testing isn’t that black and white. It’s best to look at this practice as a spectrum between moderated and unmoderated testing. Sometimes, during unmoderated sessions, we like to nudge our subjects into the right direction through mild moderation when necessary. Testing our prototypes can provide us with a wide array of insights. Fundamentally, it helps us spot flaws in our designs and identify potential solutions to the issues we’ve uncovered. We learn about the parts of our product that confuse or frustrate our users. By disregarding this step, we’re opening up to the possibility of releasing a product that causes too much friction.
ESET researchers have reverse engineered this small, yet complex malware that is portable to many operating systems including Linux, BSD, Solaris, and possibly AIX and Windows. “We have named this malware Kobalos for its tiny code size and many tricks; in Greek mythology, a kobalos is a small, mischievous creature,” explains Marc-Etienne Léveillé, who investigated the malware. “It has to be said that this level of sophistication is only rarely seen in Linux malware.” Kobalos is a backdoor containing broad commands that don’t reveal the intent of the attackers. It grants remote access to the file system, provides the ability to spawn terminal sessions, and allows proxying connections to other Kobalos-infected servers, Léveillé notes. Any server compromised by Kobalos can be turned into a Command & Control (C&C) server by the operators sending a single command. As the C&C server IP addresses and ports are hardcoded into the executable, the operators can then generate new Kobalos samples that use this new C&C server. In addition, in most systems compromised by Kobalos, the client for secure communication (SSH) is compromised to steal credentials.
Applying the power of AI and blockchain to IP assets enables a paradigm shift in how IP is understood and managed. Companies that understand and adopt this new paradigm will be rewarded. Last year, we announced the inclusion of IPwe — the world’s first AI and blockchain-powered patent platform, among our selection of the next wave of enterprise blockchain business networks. The Paris-based start-up has since deployed a suite of leading-edge IP solutions, removing barriers by addressing fundamental issues within today’s patent ecosystem. IPwe is partnering with IBM to accelerate its mission to address the inefficiencies in the patent marketplace. IBM Cloud and IBM Blockchain teams are working closely with IPwe on a multi-year project to assist IPwe in its mission to deliver world class solutions to its enterprise, SME, university, law firms, research institutions and government customers, with a heavy emphasis on meeting the needs of financial, technology and risk management executives. In addition to giving patent owners tools that provide greater visibility, effective management, and ease of conducting transactions with patents, the IPwe Platform reduces costs for innovators, and creates commercial opportunities for those that wish to partner or engage in financial transactions.
Most community developers will progress through three stages as they become more capable of using the low-code platform. Many community developers won’t progress beyond the first or second stage but some will go onto the third stage and build full-featured applications used throughout your business. Stage 1—UI Generation: Initially they will create applications with nice user interfaces with data that is keyed into the application. For example, they may make a meeting notes application that allows users to jointly add meeting notes as a meeting progresses. This is the UI Generation stage. Stage 2—Integration: As users gain experience, they’ll move to the second stage where they start pulling in data from external systems and data sources. For example, they’ll enhance their meeting notes application to pull calendar information from Outlook and email attendees after each meeting with a copy of the notes. This is the Integration stage. Stage 3—Transformation: And, finally, they’ll start creating applications that perform increasingly sophisticated transformations. For example, they may run the meeting notes through a machine learning model to tag and store the meeting content so that it can be searched by topic. This is the Transformation stage.
Like DevOps, the various types of Ops aim to accelerate processes and improve the quality of what they're delivering: software (DevOps); data (DataOps); AI models (MLOps); and analytics insights (AIOps). Some consider the different Ops types important since the expertise required for each type differs. Others believe it's just hype, specifically relabeling what already exists and/or there's a risk that the fragmentation created by all the different groups may create extra bureaucracy that frustrates faster value delivery. Agile software development practices have been bubbling up to the business for some time. Since the dawn of the millennium, business leaders have been told their companies need to be more agile just to stay competitive. Meanwhile, many agile software development teams have adopted DevOps and increasingly they've gone a step further by embracing continuous integration/continuous delivery (CI/CD) which automates additional tasks to enable an end-to-end pipeline which provides visibility throughout and smoother process flows than the traditional waterfall handoffs. Like DevOps, DataOps, MLOps, and AIOps are cross-functional endeavors focused on continuous improvement, efficiency and process improvement.
In the Security Operations space, we have been using SIEM's for many years with varying degrees of deployments, customization, and effectiveness. For the most part, they have been a helpful tool for Security Operations. But they can be better. Like any tool, they need to be sharpened and used correctly. After a while, even a sharpened tool can become dull from too much use: and with a SIEM that takes the form of too many events creating the dreaded ALERT FATIGUE!!! This is real for security operations and must be addressed; because the more alerts, the more an engineer must work on, and the more they will miss. Insert Sigma Rules for SIEMS (pun intended); a way for Security Operations to implement standardization into the daily tasks of building SIEM queries, managing logs, and threat hunting correlations. What is a Sigma rule, you may ask? A Sigma rule is a generic and open, YAML-based signature format that enables a security operations team to describe relevant log events in a flexible and standardized format. So, what does that mean for security operations? Standardization and Collaboration are now more possible than ever before with the adoption of Sigma Rules throughout the Security Operations community.
Risk modelling includes assessing risks at different time points, which can determine the preventive measures that need to be taken at different stages. This can provide insight into the risk of developing cancer at a time point compared to the other, which is not useful. Hence, scientists trained Mirai to have an ‘additive hazard layer’. This layer can predict a patient’s risk at a time point, let’s say four years, as an extension of the risk at a previous time point, say three years, instead of comparing two different time points. This can help the model learn to make self-consistent risk assessments even with variable amounts of follow-ups as inputs. Secondly, the model includes non-image risk factors like age and hormonal variables but does not necessarily require them at the test time, since a trained network can extract this information from mammograms. Hence, this model can be adopted globally. Lastly, standard training models do not work even with minor variations, such as a change in the mammography machine used. Mirai used an ‘adversarial’ scheme, to de-bias such models to learn from mammogram representations agnostic to the source clinical environment.
The music study is only one of many recent efforts to understand what people are thinking using computers. The research could lead to technology that one day would help people with disabilities manipulate objects using their minds. For example, Elon Musk’s Neuralink project aims to produce a neural implant that allows you to carry a computer wherever you go. Tiny threads are inserted into areas of the brain that control movement. Each thread contains many electrodes and is connected to an implanted computer. "The initial goal of our technology will be to help people with paralysis to regain independence through the control of computers and mobile devices," according to the project’s website. "Our devices are designed to give people the ability to communicate more easily via text or speech synthesis, to follow their curiosity on the web, or to express their creativity through photography, art, or writing apps." Brain-machine interfaces might even one day help make video games more realistic. Gabe Newell, the co-founder and president of video game giant Valve, said recently that his company is trying to connect human brains to computers. The company is working to develop open-source brain-computer interface software, he said.
AI is also a real revolution within risk assessment, notably through the enhanced use of alternative data. This is true both for traditional risks and emerging risks such as climate change, helping all financial players — banks and insurers alike — to reconsider how they price risks. Those who have developed a strong expertise in leveraging alternative data and agile modeling have been able to truly benefit from their investment during the ongoing health crisis, which has deeply challenged traditional models. Lastly, the positive impact of AI on customers should not be underestimated. Financial services are confronted with an aggressive competitive landscape as well as demand from customers for improved personalisation, driving improved customer orientation in these organisations. The capacity to build 360° customer views and optimise customer journeys, notably on claims management, are two examples of areas where AI has significantly supported deep transformation within banks and insurance companies, with yet much more to be delivered.
Quote for the day:
"Leadership is a potent combination of strategy and character. But if you must be without one, be without the strategy." -- Norman Schwarzkopf