Yara rules are a ubiquitous tool among cybersecurity practitioners and analysts. Developing high-quality Yara rules to detect a malware family of interest can be labor- and time-intensive, even for expert users. Few tools exist and relatively little work has been done on how to automate the generation of Yara rules for specific families. In this paper, we leverage large n-grams ($n \geq 8$) combined with a new biclustering algorithm to construct simple Yara rules more effectively than currently available software. Our method, AutoYara, is fast, allowing for deployment on low-resource equipment for teams that deploy to remote networks. Our results demonstrate that AutoYara can help reduce analyst workload by producing rules with useful true-positive rates while maintaining low false-positive rates, sometimes matching or even outperforming human analysts. In addition, real-world testing by malware analysts indicates AutoYara could reduce analyst time spent constructing Yara rules by 44-86%, allowing them to spend their time on the more advanced malware that current tools can't handle. Code will be made available at https://github.com/NeuromorphicComputationResearchProgram .

As the digital age moves forward, it's becoming impossible to avoid interacting with artificial intelligence (AI) systems. Computer assistants and AIs perform an ever-growing range of tasks that are broadly intended to improve our quality of life. This extends to industry as well. But first, what do we mean by artificial intelligence? In simple terms, it's any machine (usually a computer) that does things normally associated with human intelligence, such as reasoning, learning and self-improvement.

This paper proposes an approach to analyze an event log of a business process in order to generate case-level recommendations of treatments that maximize the probability of a given outcome. Users classify the attributes in the event log into controllable and non-controllable, where the former correspond to attributes that can be altered during an execution of the process (the possible treatments). We use an action rule mining technique to identify treatments that co-occur with the outcome under some conditions. Since action rules are generated based on correlation rather than causation, we then use a causal machine learning technique, specifically uplift trees, to discover subgroups of cases for which a treatment has a high causal effect on the outcome after adjusting for confounding variables. We test the relevance of this approach using an event log of a loan application process and compare our findings with recommendations manually produced by process mining experts.

The RPA stands for "Robotic Process Automation." It supports several manual and repetitive tasks to automate like human beings. We can say that "RPA is a process of creation and training of software bot (automated programs) to automate the business process." "The RPA is the technology which allows anyone to configure the computer software or robot for emulating and integrating the actions of humans to interact within the digital systems to implement or execute the business process." RPA is the digital workforce. It interacts with the computer system in the same as the human does. It automates repetitive and tedious tasks. Robotic Process Automation is the sequence of commands which are executed by the automated programs under some defined sets of business rules. The primary purpose of RPA is to replace repetitive and boring clerical tasks into the virtual workforce by robots or machines. We train the bots (automated programs) what to do and let them do the work. The RPA is an acronym for Robotic Process Automation. The Robotic Process automation is used in both IT and Non-IT industries. Nowadays, every organization have a repetitive, boring, tedious task so, RPA is necessary to eliminate these types of task by making the automation of tasks.

The summer recess is over and MPs are returning to Westminster with very full inboxes. As Parliament throws its doors open for the new term, what challenges lie ahead for the government in the coming months? The challenges of posed by the coronavirus pandemic will be forefront of ministers' minds: how to encourage a return to something approaching normal while keeping the virus under control. As the holiday season winds up, many in the Conservative party want to see more done to encourage people back to offices in England - and ministers are urging people working from home to speak to their employers about returning to workplaces where it's safe to do so. Health Committee chair Jeremy Hunt has warned that the situation coming into winter is "potentially very perilous".

As a field of AI, Machine Reasoning (MR) uses largely symbolic means to formalize and emulate abstract reasoning. Studies in early MR have notably started inquiries into Explainable AI (XAI) -- arguably one of the biggest concerns today for the AI community. Work on explainable MR as well as on MR approaches to explainability in other areas of AI has continued ever since. It is especially potent in modern MR branches, such as argumentation, constraint and logic programming, planning. We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape. This document reports our work in-progress on MR explainability.

As the name says, these approaches rely on the availability of data to extract knowledge and train algorithms. This is opposed to, e.g., modeling approaches in which physiological, physics-based, mathematical, and other equations form the basis of algorithms, or, rule-based systems in which reasoning processes are obtained by translating domain-experts' knowledge into computer-based rules. Focusing on data-driven systems, the data plays a role in several components during the development and actual usage phases. First, we need data to extract knowledge from, i.e., to develop and train algorithms so that they learn-by-example the properties of the problem at hand and get better at solving the problem by repeatedly providing example data. Second, we need to monitor during the development phase how promising the algorithms are and make choices, e.g., concerning optimisation of parameters or choosing different MLparadigms. Methods that don't perform well at all can be discarded, and ones that seem promising can be further optimised. To assess how promising a specific method is, we need to examine how it performs on data that was not used during training. Finally, to objectively assess how well the final'best' system performs, we need to apply completely new data to it that has not been used at all thusfar during the research and development process. Thus, there are at least three stakeholders that have the interest to get as large part of the data pie as possible.

Enterprises around the world are rapidly incorporating artificial intelligence (AI) into existing and new products and processes. This effort is not just to improve such offerings and services, but to achieve a qualitatively higher level of capability not possible before. It is clear that AI carries the potential for many new opportunities, across all industries, but it is also already recognized that it brings numerous risks as well. As with any technology, senior management and board directors need to be aware of both the opportunity and the risk in order to successfully and responsibly manage the enterprise. The opportunities are great--AI can assist in robotic process automation (RPA), machine learning, natural language processing, finding new drugs and therapies, and will be essential for driverless transportation--but if the risks are downplayed or overlooked, there can be serious reputational and/or legal consequences.

With so much reliance on digital payments and other financial technology (fintech), going through some form of purportedly secure, digital verification process (often referred to as'know your customer', or KYC, processes) is often par for the course these days. But with pervasive cyber threats like data breaches and identity theft delivering blows to what the end-user hopes is an un-breach-able system, "taking a single selfie just isn't enough to ensure your customer's identity [anymore]," laments Philipp Pointner, chief product officer at digital security specialist Jumio. "It leaves banks and financial institutions vulnerable to spoofing attacks as a fraudster can easily find a picture of someone else online and pass that off as genuine." "But using solutions that employ biometrics, and specifically 3D face maps and certified liveness detection, ensures the [people] behind a transaction [are] who they say they are," Pointner recently told PYMNTS. Biometrics – working in concert with a combination of artificial intelligence (AI) and machine learning (ML) to scan, analyze and then to create what could be a varied biometric identity database capable of verifying and storing fingerprints, facial features, even voice and device data – could allow for not only tougher, more meticulous identity security, but also a deeper understanding of a financial institute's customer profile – giving banks and other fintech a truer way to "know your customer".

Orange launched its strategic plan for the next five years, Engage 2025, last December and AI-enabled innovation was one of the four pillars of the group's future success. This breaks into four parts, as shown below. The 2025 strategy states, "Our ambition is as strong as our social commitments are firm. And we will never think of one without the other" (see last section of article on inclusivity). Lugagne-Delpon said at the online briefing, "We believe that AI can bring value to almost every phase of the network lifecycle – so network planning and design to optimise the efficiency of investment, operations for advanced monitoring, smarter maintenance and better security, and also optimisation to populate a number of operation processes and also optimise the performance and the use of resources." He went on to describe a number of use cases.