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Cybersecurity 101: Protect your privacy from hackers, spies, and the government


"I have nothing to hide" was once the standard response to surveillance programs utilizing cameras, border checks, and casual questioning by law enforcement. Privacy used to be considered a concept generally respected in many countries with a few changes to rules and regulations here and there often made only in the name of the common good. Things have changed, and not for the better. China's Great Firewall, the UK's Snooper's Charter, the US' mass surveillance and bulk data collection -- compliments of the National Security Agency (NSA) and Edward Snowden's whistleblowing -- Russia's insidious election meddling, and countless censorship and communication blackout schemes across the Middle East are all contributing to a global surveillance state in which privacy is a luxury of the few and not a right of the many. As surveillance becomes a common factor of our daily lives, privacy is in danger of no longer being considered an intrinsic right. Everything from our web browsing to mobile devices and the Internet of Things (IoT) products installed in our homes have the potential to erode our privacy and personal security, and you cannot depend on vendors or ever-changing surveillance rules to keep them intact. Having "nothing to hide" doesn't cut it anymore. We must all do whatever we can to safeguard our personal privacy. Taking the steps outlined below can not only give you some sanctuary from spreading surveillance tactics but also help keep you safe from cyberattackers, scam artists, and a new, emerging issue: misinformation. Data is a vague concept and can encompass such a wide range of information that it is worth briefly breaking down different collections before examining how each area is relevant to your privacy and security. A roundup of the best software and apps for Windows and Mac computers, as well as iOS and Android devices, to keep yourself safe from malware and viruses. Known as PII, this can include your name, physical home address, email address, telephone numbers, date of birth, marital status, Social Security numbers (US)/National Insurance numbers (UK), and other information relating to your medical status, family members, employment, and education. All this data, whether lost in different data breaches or stolen piecemeal through phishing campaigns, can provide attackers with enough information to conduct identity theft, take out loans using your name, and potentially compromise online accounts that rely on security questions being answered correctly. In the wrong hands, this information can also prove to be a gold mine for advertisers lacking a moral backbone.

10 Common Uses for Machine Learning Applications in Business


Machine learning has advanced from the age of science fiction to a major component of modern enterprises, especially as businesses across almost all sectors use various machine learning technologies. As an example, the healthcare industry is utilizing machine learning business applications to achieve more accurate diagnoses and provide better treatment to their patients. Retailers also use machine learning to send the right goods and products to the right stores before it is out of stock. Medical researchers are also not left out when it comes to using machine learning as many introduce newer and more effective medicines with the help of this technology. Many use cases are emerging from all sectors as machine learning is being implemented in logistics, manufacturing, hospitality, travel and tourism, energy, and utilities.

Zammo unfurls conversational AI integration service

#artificialintelligence today launched a conversational AI platform that makes it simpler to engage customers via multiple voice assistants, interactive voice response (IVR)/telephony, and chatbots without having to write any code. That no-code approach, provided via the integrations the company has embedded within its software-as-a-service (SaaS) platform, enables organizations to create workflows that span multiple conversational AI technologies without the aid of an internal IT team or a systems integrator, said company CEO Alex Farr. "No one from IT is required," he said. That approach provides the added benefit of eliminating the need to force customers to embrace a specific conversational AI platform, noted Farr. Organizations can add support for conversational AI platforms based on customer preferences, he said.

What is Azure Synapse and how is it different from Azure Data Bricks?


Azure Synapse Analytics is an unlimited information analysis service aimed at large companies that was presented as the evolution of Azure SQL Data Warehouse (SQL DW), bringing together business data storage and macro or Big Data analysis. Synapse provides a single service for all workloads when processing, managing and serving data for immediate business intelligence and data prediction needs. The latter is made possible by its integration with Power BI and Azure Machine Learning, due to Synapse's ability to integrate mathematical machine learning models using the ONNX format. It provides the freedom to handle and query huge amounts of information either on demand serverless (a type of deployment that automatically scales power on demand when large amounts of data are available) for data exploration and ad hoc analysis, or with provisioned resources, at scale. As one of the few Microsoft's Power BI partners in Spain, at Bismart we have a large experience working with both Power BI and Azure Synapse.

Trifacta goes all in on the cloud


Trifacta, which has become the last pure play data prep tools provider still standing, sees its future as a broader based cloud software-as-a-service (SaaS) service. This week, it is unveiling a new Data Engineering Cloud that will deliver a fully managed service on each of the major clouds. That will be in addition to, not instead of Wrangler, its long-established on-premises prep suite. Here's a look at how the cloud leaders stack up, the hybrid market, and the SaaS players that run your company as well as their latest strategic moves. Trifacta's niche will continue to be serving as the front end design studio where the data engineer, data scientist, or business developer creates the "recipes" for data preparation and transformation.

Applications of Python


Python is a simple, open-source and object-oriented coding language. It is one of the programming languages that are easy to learn as it is a dynamic type, high-level, and interpreted coding language. This is also used for debugging of errors and motivate for instant growth of application prototypes and using it as a language to program with. Python programming language was originated by Guido Van Rossum in 1989 which is based on the DRY (Do not Repeat Yourself) principle. This blog will provide you the various uses of Python that help you to understand where one can easily implement the Python programming language and execute it in different sectors.

SuSketch: Surrogate Models of Gameplay as a Design Assistant Artificial Intelligence

This paper introduces SuSketch, a design tool for first person shooter levels. SuSketch provides the designer with gameplay predictions for two competing players of specific character classes. The interface allows the designer to work side-by-side with an artificially intelligent creator and to receive varied types of feedback such as path information, predicted balance between players in a complete playthrough, or a predicted heatmap of the locations of player deaths. The system also proactively designs alternatives to the level and class pairing, and presents them to the designer as suggestions that improve the predicted balance of the game. SuSketch offers a new way of integrating machine learning into mixed-initiative co-creation tools, as a surrogate of human play trained on a large corpus of artificial playtraces. A user study with 16 game developers indicated that the tool was easy to use, but also highlighted a need to make SuSketch more accessible and more explainable.

What characterises the HANA SQL Data Warehouse?


As known from many articles and publications, SAP offers three solutions for data warehousing. The SAP Business Warehouse (BW) was first published in 1997 and has therefore been a constant figure in the SAP Data Warehouse range for more than two decades. With HANA as a database platform, the HANA SQL Data Warehouse approach has been developing since 2015, which initially consisted of loosely coupled tools, but has since evolved into an open, yet highly integrated set of tools and methods, that can also be used to develop large data warehouse systems. Since 2019, the Data Warehouse Cloud has been completing the SAP solution as a SaaS solution. These three approaches are not in competition.

What Is Data Warehousing And Does It Still Make Sense? – Fly Spaceships With Your Mind


Data Warehousing – In today's flood of data, it is becoming increasingly difficult to maintain a clear data management system. More and more data sources are recorded via different software systems. A unified, centralized system can facilitate analysis and ensure that only one data truth exists in an organization. Data warehouse systems are built by integrating data from multiple heterogeneous sources and, in addition to centralization, performs the task of structuring data, supporting analytical reporting and structuring decision-making. The system can perform data cleansing as well as data integration and data consolidation and does not require transaction processing or recovery.

Monte Carlo Tree Search: A Review of Recent Modifications and Applications Artificial Intelligence

Monte Carlo Tree Search (MCTS) is a decision-making algorithm that consists in searching large combinatorial spaces represented by trees. In such trees, nodes denote states, also referred to as configurations of the problem, whereas edges denote transitions (actions) from one state to another. MCTS has been originally proposed in the work by Kocsis and Szepesvári (2006) and by Coulom (2006), as an algorithm for making computer players in Go. It was quickly called a major breakthrough (Gelly et al., 2012) as it allowed for a leap from 14 kyu, which is an average amateur level, to 5 dan, which is considered an advanced level but not professional yet. Before MCTS, bots for combinatorial games had been using various modifications of the min-max alpha-beta pruning algorithm (Junghanns, 1998) such as MTD(f) (Plaat, 2014) and hand-crafted heuristics. In contrast to them, MCTS algorithm is at its core aheuristic, which means that no additional knowledge is required other than just rules of a game (or a problem, generally speaking). However, it is possible to take advantage of heuristics and include them in the MCTS approach to make it more efficient and improve its convergence. Moreover, the given problem often tends to be so complex, from the combinatorial point of view, that some form of external help, e.g.