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Category Archives: Machine Learning

Parascript and SFORCE Partner to Leverage Machine Learning Eliminating Barriers to Automation – GlobeNewswire

Longmont, CO, Feb. 09, 2021 (GLOBE NEWSWIRE) -- Parascript, which provides document analysis software processing for over 100 billion documents each year, announced today the Smart-Force (SFORCE) and Parascript partnership to provide a digital workforce that augments operations by combining cognitive Robotic Process Automation (RPA) technology with customers current investments for high scalability, improved accuracy and an enhanced customer experience in Mexico and across Latin America.

Partnering with Smart-Force means we get to help solve some of the greatest digital transformation challenges in Intelligent Document Processing instead of just the low-hanging fruit. Smart-Force is forward-thinking and committed to futureproofing their customers processes, even with hard-to-automate, unstructured documents where the application of techniques such as NLP is often required, said Greg Council, Vice President of Marketing and Product Management at Parascript. Smart-Force leverages bots to genuinely collaborate with staff so that the staff no longer have to spend all their time on finding information, and performing data entry and verification, even for the most complex multi-page documents that you see in lending and insurance.

Smart-Force specializes in digital transformation by identifying processes in need of automation and implementing RPA to improve those processes so that they run faster without errors. SFORCE routinely enables increased productivity, improves customer satisfaction, and improves staff morale through leveraging the technology of Automation Anywhere, Inc., a leader in RPA, and now Parascript Intelligent Document Processing.

As intelligent automation technology becomes more ubiquitous, it has created opportunities for organizations to ignite their staff towards new ways of working freeing up time from the manual tasks to focus on creative, strategic projects, what humans are meant to do, said Griffin Pickard, Director of Technology Alliance Program at Automation Anywhere. By creating an alliance with Parascript and Smart-Force, we have enabled customers to advance their automation strategy by leveraging ML and accelerate end-to-end business processes.

Our focus at SFORCE is on RPA with Machine Learning to transform how customers are doing things. We dont replace; we compliment the technology investments of our customers to improve how they are working, said Alejandro Castrejn, Founder of SFORCE. We make processes faster, more efficient and augment their staff capabilities. In terms of RPA processes that focus on complex document-based information, we havent seen anything approach what Parascript can do.

We found that Parascript does a lot more than other IDP providers. Our customers need a point-to-point RPA solution. Where Parascript software becomes essential is in extracting and verifying data from complex documents such as legal contracts. Manual data entry and review produces a lot of errors and takes time, said Barbara Mair, Partner at SFORCE. Using Parascript software, we can significantly accelerate contract execution, customer onboarding and many other processes without introducing errors.

The ability to process simple to very complex documents such as unstructured contracts and policies within RPA leveraging FormXtra.AI represents real opportunities for digital transformation across the enterprise. FormXtra.AI and its Smart Learning allow for easy configuration, and by training the systems on client-specific data, the automation is rapidly deployed with the ability to adapt to new information introduced in dynamic production environments.

About SFORCE, S.A. de C.V.

SFORCE offers services that allow customers to adopt digital transformation at whatever pace the organization needs. SFORCE is dedicated to helping customers get the most out of their existing investments in technology. SFORCE provides point-to-point solutions that combine existing technologies with next generation technology, which allows customers to transform operations, dramatically increase efficiency as well as automate manual tasks that are rote and error-prone, so that staff can focus on high-value activities that significantly increase revenue. From exploring process automation to planning a disruptive change that ensures high levels of automation, our team of specialists helps design and implement the automation of processes for digital transformation. Visit SFORCE.

About Parascript

Parascript software, driven by data science and powered by machine learning, configures and optimizes itself to automate simple and complex document-oriented tasks such as document classification, document separation and data entry for payments, lending and AP/AR processes. Every year, over 100 billion documents involved in banking, insurance, and government are processed by Parascript software. Parascript offers its technology both as software products and as software-enabled services to our partners. Visit Parascript.

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There Is No Silver Bullet Machine Learning Solution – Analytics India Magazine

A recommendation engine is a class of machine learning algorithm that suggests products, services, information to users based on analysis of data. Robust recommendation systems are the key differentiator in the operations of big companies like Netflix, Amazon, and Byte Dance (TikTok parent) etc.

Alok Menthe, Data Scientist at Ericsson, gave an informative talk on building Custom recommendation engines for real-world problems at the Machine Learning Developers Summit (MLDS) 2021. Whenever a niche business problem comes in, it has complicated intertwined ways of working. Standard ML techniques may be inadequate and might not serve the customers purpose. That is where the need for a custom-made engine comes in. We were also faced with such a problem with our service network unit at Ericsson, he said.

Menthe said the unit wanted to implement a recommendation system to provide suggestions for assignment workflow a model to delegate the incoming projects to the most appropriate team or resource pool

Credit: Alok Menthe

There were three kinds of data available:

Pool definition data: It relates to the composition of a particular resource poolthe number of people, their competence, and other metadata.

Historical demand data: This kind of data helps in establishing a relationship between the feature demand and a particular resource pool.

Transactional data: It is used for operational purposes.

Menthe said building a custom recommendation system in this context involves the following steps:

Credit: Alok Menthe

After building our model, the most difficult part was feature engineering, which is imperative for building an efficient system. Among the two major modules classification and clusteringwe faced challenges with respect to the latter. We had only categorical information making it difficult to find distances within the objects. We went out of the box to see if we can do any special encoding for the data. We adopted data encoding techniques and frequency-based encoding in this regard, said Menthe.

Clustering module: For this module, initially the team implemented K-modes and agglomerative. However, the results were far from perfect, prompting the team to consider the good-old K-means algorithm. For evaluation purposes, it was done manually with the help of subject matter experts.

The final model had 700 resource pools condensed to 15 pool clusters.

Classification module: For this module, three kinds of algorithm iterations were usedRandom Forest, Artificial Neural Network, XGBoost. Classification accuracy was used as an evaluation metric. Finally, upon 50,00,000 training records, this module demonstrated an accuracy of 71 percent.

Menthe said this recommendation model is monitored on a fortnightly basis by validating the suggested pools against the allocated pools for project demands:

The model has proved to be successful on three fronts:

Menthe summarised the three major takeaways from this project in his concluding remarks: the need to preserve business nuances in ML solutions; thinking beyond standard ML approaches; and understanding that there is no silver bullet ML solution.

I am a journalist with a postgraduate degree in computer network engineering. When not reading or writing, one can find me doodling away to my hearts content.

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The Collision of AI’s Machine Learning and Manipulation: Deepfake Litigation Risks to Companies from a Product Liability, Privacy, and Cyber…

AI and machine-learning advances have made it possible to produce fake videos and photos that seem real, commonly known as deepfakes. Deepfake content is exploding in popularity.[i] In Star Wars: The Rise of Skywalker, for instance, a visage of Carrie Fischer graced the screen, generated through artificial intelligence models trained on historic footage. Using thousands of hours of interviews with Salvador Dali, the Dali Museum of Florida created an interactive exhibit featuring the artist.[ii] For Game of Thrones fans miffed over plot holes in the season finale, Jon Snow can be seen profusely apologizing in a deepfake video that looks all too real.[iii]

Deepfake technologyhow does it work? From a technical perspective, deepfakes (also referred to as synthetic media) are made from artificial intelligence and machine-learning models trained on data sets of real photos or videos. These trained algorithms then produce altered media that looks and sounds just like the real deal. Behind the scenes, generative adversarial networks (GANs) power deepfake creation.[iv] With GANs, two AI algorithms are pitted against one another: one creates the forgery while the other tries to detect it, teaching itself along the way. The more data is fed into GANs, the more believable the deepfake will be. Researchers at academic institutions such as MIT, Carnegie Mellon, and Stanford University, as well as large Fortune 500 corporations, are experimenting with deepfake technology.[v] Yet deepfakes are not solely the province of technical universities or AI product development groups. Anybody with an internet connection can download publicly available deepfake software and crank out content.[vi]

Deepfake risks and abuse. Deepfakes are not always fun and games. Deepfake videos can phish employees to reveal credentials or confidential information, e-commerce platforms may face deepfake circumvention of authentication technologies for purposes of fraud, and intellectual property owners may find their properties featured in videos without authorization. For consumer-facing online platforms, certain actors may attempt to leverage deepfakes to spread misinformation. Another well-documented and unfortunate abuse of deepfake technology is for purposes of revenge pornography.[vii]

In response, online platforms and consumer-facing companies have begun enforcing limitations on the use of deepfake media. Twitter, for example, announced a new policy within the last year to prohibit users from sharing synthetic or manipulated media that are likely to cause harm. Per its policy, Twitter reserves the right to apply a label or warning to Tweets containing such media.[viii] Reddit also updated its policies to ban content that impersonates individuals or entities in a misleading or deceptive manner (while still permitting satire and parody).[ix] Others have followed. Yet social media and online platforms are not the only industries concerned with deepfakes. Companies across industry sectors, including financial and healthcare, face growing rates of identity theft and imposter scams in government services, online shopping, and credit bureaus as deepfake media proliferates.[x]

Deepfake legal claims and litigation risks. We are seeing legal claims and litigation relating to deepfakes across multiple vectors:

1. Claims brought by those who object to their appearance in deepfakes. Victims of deepfake media sometimes pursue tort law claims for false light, invasion of privacy, defamation, and intentional infliction of emotional distress. At a high level, these overlapping tort claims typically require the person harmed by the deepfake to prove that the deepfake creator published something that gives a false or misleading impression of the subject person in a manner that (a) damages the subjects reputation, (b) would be highly offensive to a reasonable person, or (c) causes mental anguish or suffering. As more companies begin to implement countermeasures, the lack of sufficient safeguards against misleading deepfakes may give rise to a negligence claim. Companies could face negligence claims for failure to detect deepfakes, either alongside the deepfake creator or alone if the creator is unknown or unreachable.

2. Product liability issues related to deepfakes on platforms. Section 230 of the Communications Decency Act shields online companies from claims arising from user content published on the companys platform or website. The law typically bars defamation and similar tort claims. But e-commerce companies can also use Section 230 to dismiss product liability and breach of warranty claims where the underlying allegations focus on a third-party sellers representation (such as a product description or express warranty). Businesses sued for product liability or other tort claims should look to assert Section 230 immunity as a defense where the alleged harm stems from a deepfake video posted by a user. Note, however, the immunity may be lost where the host platform performs editorial functions with respect to the published content at issue. As a result, it is important for businesses to implement clear policies addressing harmful deepfake videos that broadly apply to all users and avoid wading into influencing a specific users content.

3. Claims from consumers who suffer account compromise due to deepfakes. Multiple claims may arise where cyber criminals leverage deepfakes to compromise consumer credentials for various financial, online service, or other accounts. The California Consumer Privacy Act (CCPA), for instance, provides consumers with a private right of action to bring claims against businesses that violate the duty to implement and maintain reasonable security procedures and practices.[xi] Plaintiffs may also bring claims for negligence, invasion of privacy claims under common law or certain state constitutions, and state unfair competition or false advertising statutes (e.g., Californias Unfair Competition Law and Consumers Legal Remedies Act).

4. Claims available to platforms enforcing Terms of Use prohibitions of certain kinds of deepfakes. Online content platforms may be able to enforce prohibitions on abusive or malicious deepfakes through claims involving breach of contract and potential violations of the Computer Fraud and Abuse Act (CFAA), among others. These claims may turn on nuanced issues around what conduct constitutes exceeding authorized access under the CFAA, or Terms of Use assent and enforceability of particular provisions.

5. Claims related to state statutes limiting deepfakes. As malicious deepfakes proliferate, several states such as California, Texas, and Virginia have enacted statutes prohibiting their use to interfere with elections or criminalizing pornographic deepfake revenge video distribution.[xii] More such statutes are pending.

Practical tips for companies managing deepfake risks. While every company and situation is unique, companies dealing with deepfakes on their platforms, or as a potential threat vector for information security attacks, can consider several practical avenues to manage risks:

While the future of deepfakes is uncertain, it is apparent that the underlying AI and machine-learning technology is very real and here to staypresenting both risks and opportunity for organizations across industries.

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Postdoctoral Research Associate in Digital Humanities and Machine Learning job with DURHAM UNIVERSITY | 246392 – Times Higher Education (THE)

Department of Computer Science

Grade 7:-33,797 - 40,322 per annumFixed Term-Full TimeContract Duration:7 monthsContracted Hours per Week:35Closing Date:13-Mar-2021, 7:59:00 AM

Durham University

Durham University is one of the world's top universities with strengths across the Arts and Humanities, Sciences and Social Sciences. We are home to some of the most talented scholars and researchers from around the world who are tackling global issues and making a difference to people's lives.

The University sits in a beautiful historic city where it shares ownership of a UNESCO World Heritage Site with Durham Cathedral, the greatest Romanesque building in Western Europe. A collegiate University, Durham recruits outstanding students from across the world and offers an unmatched wider student experience.

Less than 3 hours north of London, and an hour and a half south of Edinburgh, County Durham is a region steeped in history and natural beauty. The Durham Dales, including the North Pennines Area of Outstanding Natural Beauty, are home to breathtaking scenery and attractions. Durham offers an excellent choice of city, suburban and rural residential locations. The University provides a range of benefits including pension and childcare benefits and the Universitys Relocation Manager can assist with potential schooling requirements.

Durham University seeks to promote and maintain an inclusive and supportive environment for work and study that assists all members of our University community to reach their full potential. Diversity brings strength and we welcome applications from across the international, national and regional communities that we work with and serve.

The Department

The Department of Computer Science is rapidly expanding. A new building for the department (joint with Mathematical Sciences) has recently opened to house the expanded Department. The current Department has research strengths in (1) algorithms and complexity, (2) computer vision, imaging, and visualisation and (3) high-performance computing, cloud computing, and simulation. We work closely with industry and government departments. Research-led teaching is a key strength of the Department, which came 5th in the Complete University Guide. The department offers BSc and MEng undergraduate degrees and is currently redeveloping its interdisciplinary taught postgraduate degrees. The size of its student cohort has more than trebled in the past five years. The Department has an exceptionally strong External Advisory Board that provides strategic support for developing research and education, consisting of high-profile industrialists and academics.Computer Science is one of the very best UK Computer Science Departments with an outstanding reputation for excellence in teaching, research and employability of our students.

The Role

Postdoctoral Research Associate to work on the AHRC-funded project Visitor Interaction and Machine Curation in the Virtual Liverpool Biennial.

The project looks at virtual art exhibitions that are curated by machines, or even co-curated by humans and machines; and how audiences interact with these exhibitions in the era of online art shows. The project is in close collaboration with the 2020 (now 2021) Liverpool Biennial (http://biennial.com/). The role of the post holder is, along with the PI Leonardo Impett, to implement different strategies of user-machine interaction for virtual art exhibits; and to investigate the interaction behaviour of different types of users with such systems.

Responsibilities:

This post is fixed term until31 August 2021 as the research project is time limited and will end on 31 August 2021.

The post-holder is employed to work on research/a research project which will be led by another colleague. Whilst this means that the post-holder will not be carrying out independent research in his/her own right, the expectation is that they will contribute to the advancement of the project, through the development of their own research ideas/adaptation and development of research protocols.

Successful applicants will, ideally, be in post byFebruary 2021.

How to Apply

For informal enquiries please contactDr Leonardo Impett (leonardo.l.impett@durham.ac.uk).All enquiries will be treated in the strictest confidence.

We prefer to receive applications online via the Durham University Vacancies Site.https://www.dur.ac.uk/jobs/. As part of the application process, you should provide details of 3 (preferably academic/research) referees and the details of your current line manager so that we may seek an employment reference.

Applications are particularly welcome from women and black and minority ethnic candidates, who are under-represented in academic posts in the University.We are committed to equality: if for any reason you have taken a career break or periods of leave that may have impacted on your career path, such as maternity, adoption or parental leave, you may wish to disclose this in your application.The selection committee will recognise that this may have reduced the quantity of your research accordingly.

What to Submit

All applicants are asked to submit:

The Requirements

Essential:

Qualifications

Experience

Skills

Desirable:

Experience

Skills

DBS Requirement:Not Applicable.

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Postdoctoral Research Associate in Digital Humanities and Machine Learning job with DURHAM UNIVERSITY | 246392 - Times Higher Education (THE)

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Gurucul XDR Uses Machine Learning & Integration for Real-Time Threat Detection, Incident Response – Integration Developers

To improve speed and intelligence of threat detection and response, Guruculs cloud-native XDR platform is adding machine learning, integration risk scoring and more.

by Anne Lessman

Tags: cloud-native, Gurucul, integration, machine learning, real-time, threat detection,

The latest upgrade to the Gurucul XDR platform adds extended detection and response alongside improved risk scoring to strengthen security operations effectiveness and productivity.

Improvements to Guruculs cloud-native solution also sport features to enable intelligent investigations and risk-based response automation. New features include extended data linking, additions to its out-of-the-box integrations, contextual machine learning (ML) analytics and risk-prioritized alerting.

The driving force behind these updates is to provide users a single pane of risk, according to Gurucul CEO Saryu Nayyar.

Most XDR products are based on legacy platforms limited to siloed telemetry and threat detection, which makes it difficult to provide unified security operations capabilities, Nayyar said.

Gurucul Cloud-native XDR is vendor-agnostic and natively built on a Big Data architecture designed to process, contextually link, analyze, detect, and risk score using data at massive scale. It also uses contextual Machine Learning models alongside a risk scoring engine to provide real-time threat detection, prioritize risk-based alerts and support automated response, Nayyar.added.

Gurucul XDR provides the following capabilities that are proven to improve incident response times:

AI/ML Suggestive Investigation and Automated Intelligent Responses: Traditional threat hunting tools and SIEMs focus on a limited number of use cases since they rely on data and alerts from a narrow set of resources. With cloud adoption increasing at a record pace, threat hunting must span hybrid on-premises and cloud environments and ingest data from vulnerability management, IoT, medical, firewall, network devices and more.

Guruculs approach provides agentless, out-of-the-box integrations that support a comprehensive set of threat hunting applications. These include: Insider threat detection, Data exfiltration, Phishing, Endpoint forensics, Malicious processes and Network threat analytics.

Incident Timeline, Visualizations, and Reporting: Automated Incident Timelines create a smart link of the entire attack lifecycle for pre-and post-incident analysis. Timelines can span days and even years of data in easy-to-understand visualizations.

Guruculs visualization and dashboarding enables analysts to view threats from different perspectives using several widgets, including TreeMap, Bubble Chart, etc., that provide full drill-down capabilities into events without leaving the interface. The unique scorecard widget generates a spider chart representation of cyber threat hunting outcomes such as impact, sustaining mitigation measures, process improvements scores, etc.

Risk Prioritized Automated Response: Integration with Gurucul SOAR enables analysts to invoke more than 50 actions and 100 playbooks upon detection of a threat to minimize damages.

Entity Based Threat Hunting: Perform contextual threat hunting or forensics on entities. Automate and contain any malicious or potential threat from a single interface.

Red Team Data Tagging: Teams can leverage red team exercise data and include supervised learning techniques as part of a continuous AI-based threat hunting process.

According to Gartner, XDR products aim to solve the primary challenges with SIEM products, such as effective detection of and response to targeted attacks, including native support for behavior analysis, threat intelligence, behavior profiling and analytics.

Further, the primary value propositions of an XDR product are to improve security operations productivity and enhance detection and response capabilities by including more security components into a unified whole that offers multiple streams of telemetry, Gartner added.

The result, the firm said, is to present options for multiple forms of detection and . . multiple methods of response.

Gurucul XDR provides the following capabilities that are proven to improve incident response times by nearly 70%:

Surgical Response

Intelligent Centralized Investigation

Rapid Incident Correlation and Causation

Gurucul XDR is available immediately from Gurucul and its business partners worldwide.

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– Retracing the evolution of classical music with machine learning – Design Products & Applications

05 February 2021

Researchers in EPFLs Digital and Cognitive Musicology Lab in the College of Humanities used an unsupervised machine learning model to reveal how modes such as major and minor have changed throughout history.

Many people may not be able to define what a minor mode is in music, but most would almost certainly recognise a piece played in a minor key. Thats because we intuitively differentiate the set of notes belonging to the minor scale which tend to sound dark, tense, or sad from those in the major scale, which more often connote happiness, strength, or lightness.

But throughout history, there have been periods when multiple other modes were used in addition to major and minor or when no clear separation between modes could be found at all.

Understanding and visualising these differences over time is what Digital and Cognitive Musicology Lab (DCML) researchers Daniel Harasim, Fabian Moss, Matthias Ramirez, and Martin Rohrmeier set out to do in a recent study, which has been published in the open-access journal Humanities and Social Sciences Communications. For their research, they developed a machine learning model to analyze more than 13,000 pieces of music from the 15th to the 19th centuries, spanning the Renaissance, Baroque, Classical, early Romantic, and late-Romantic musical periods.

We already knew that in the Renaissance [1400-1600], for example, there were more than two modes. But for periods following the Classical era [1750-1820], the distinction between the modes blurs together. We wanted to see if we could nail down these differences more concretely, Harasim explains.

Machine listening (and learning)

The researchers used mathematical modelling to infer both the number and characteristics of modes in these five historical periods in Western classical music. Their work yielded novel data visualizations showing how musicians during the Renaissance period, like Giovanni Pierluigi da Palestrina, tended to use four modes, while the music of Baroque composers, like Johann Sebastian Bach, revolved around the major and minor modes. Interestingly, the researchers could identify no clear separation into modes of the complex music written by Late Romantic composers, like Franz Liszt.

Harasim explains that the DCMLs approach is unique because it is the first time that unlabelled data have been used to analyse modes. This means that the pieces of music in their dataset had not been previously categorized into modes by a human.

We wanted to know what it would look like if we gave the computer the chance to analyse the data without introducing human bias. So, we applied unsupervised machine learning methods, in which the computer 'listens' to the music and figures out these modes on its own, without metadata labels.

Although much more complex to execute, this unsupervised approach yielded especially interesting results which are, according to Harasim, more cognitively plausible with respect to how humans hear and interpret music.

We know that musical structure can be very complex and that musicians need years of training. But at the same time, humans learn about these structures unconsciously, just as a child learns a native language. Thats why we developed a simple model that reverse engineers this learning process, using a class of so-called Bayesian models that are used by cognitive scientists, so that we can also draw on their research.

From class project to publicationand beyond

Harasim notes with satisfaction that this study has its roots in a class project that he and his co-authors Moss and Ramirez did together as students in EPFL professor Robert Wests course, Applied Data Analysis. He hopes to take the project even further by applying their approach to other musical questions and genres.

For pieces within which modes change, it would be interesting to identify exactly at what point such changes occur. I would also like to apply the same methodology to jazz, which was the focus of my PhD dissertation because the tonality in jazz is much richer than just two modes.

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