In the realm of planning, this approach was pioneered by Howey, Long, and Fox As witnessed by the different planning competitions (Long who developed VAL (Howey, Long, and Fox 2004) that, 2000; Coles et al. 2012; Vallati et al. 2015), planning algorithms given a planning problem and potential solution, certifies and systems are becoming more and more scalable that the solution actually solves the given problem. Also, and efficient, which makes them suited for more realistic certifying unsolvability for planning was tackled by Eriksson, applications. Given that many applications of planning Röger, and Helmert (2017) who provided unsolvability are safety-critical, increasing the trustworthiness of certificates and checkers for state-space search algorithms planning algorithms and systems--i.e. the likelihood that and by Eriksson and Helmert (2020) for property they compute correct results--could be instrumental in their directed SATbased planning.

The Dead Sea Scrolls are tangible evidence of the Bible's ancient scribal culture. Palaeography - the study of ancient handwriting - can provide access to this scribal culture. However, one of the problems of traditional palaeography is to determine writer identity when the writing style is near uniform. This is exemplified by the Great Isaiah Scroll (1QIsaa). To this end, we used pattern recognition and artificial intelligence techniques to innovate the palaeography of the scrolls regarding writer identification and to pioneer the microlevel of individual scribes to open access to the Bible's ancient scribal culture. Although many scholars believe that 1QIsaa was written by one scribe, we report new evidence for a breaking point in the series of columns in this scroll. Without prior assumption of writer identity, based on point clouds of the reduced-dimensionality feature-space, we found that columns from the first and second halves of the manuscript ended up in two distinct zones of such scatter plots, notably for a range of digital palaeography tools, each addressing very different featural aspects of the script samples. In a secondary, independent, analysis, now assuming writer difference and using yet another independent feature method and several different types of statistical testing, a switching point was found in the column series. A clear phase transition is apparent around column 27. Given the statistically significant differences between the two halves, a tertiary, post-hoc analysis was performed. Demonstrating that two main scribes were responsible for the Great Isaiah Scroll, this study sheds new light on the Bible's ancient scribal culture by providing new, tangible evidence that ancient biblical texts were not copied by a single scribe only but that multiple scribes could closely collaborate on one particular manuscript.

Language carries implicit human biases, functioning both as a reflection and a perpetuation of stereotypes that people carry with them. Recently, ML-based NLP methods such as word embeddings have been shown to learn such language biases with striking accuracy. This capability of word embeddings has been successfully exploited as a tool to quantify and study human biases. However, previous studies only consider a predefined set of conceptual biases to attest (e.g., whether gender is more or less associated with particular jobs), or just discover biased words without helping to understand their meaning at the conceptual level. As such, these approaches are either unable to find conceptual biases that have not been defined in advance, or the biases they find are difficult to interpret and study. This makes existing approaches unsuitable to discover and interpret biases in online communities, as such communities may carry different biases than those in mainstream culture. This paper proposes a general, data-driven approach to automatically discover and help interpret conceptual biases encoded in word embeddings. We apply this approach to study the conceptual biases present in the language used in online communities and experimentally show the validity and stability of our method.

It has been shown that word embeddings can exhibit gender bias, and various methods have been proposed to quantify this. However, the extent to which the methods are capturing social stereotypes inherited from the data has been debated. Bias is a complex concept and there exist multiple ways to define it. Previous work has leveraged gender word pairs to measure bias and extract biased analogies. We show that the reliance on these gendered pairs has strong limitations: bias measures based off of them are not robust and cannot identify common types of real-world bias, whilst analogies utilising them are unsuitable indicators of bias. In particular, the well-known analogy "man is to computer-programmer as woman is to homemaker" is due to word similarity rather than societal bias. This has important implications for work on measuring bias in embeddings and related work debiasing embeddings.

DSP Group, Inc., a leading global provider of wireless and voice-processing chipset solutions for converged communications, announced software development kit (SDK) support for TensorFlow Lite for Microcontrollers for the DBMD7 family of low-cost, high-performance, multi-core AI and DSP processors. The SDK for the DBMD7 allows developers of AI IoT devices to cost-effectively deploy high-performance machine learning (ML) inference at the edge to avoid network latencies, minimize power consumption, ensure end-user privacy, and free up scarce network bandwidth. "DSP Group offers compelling hardware for many low-power applications, so we're excited to collaborate to offer machine learning software to help enable developers create products that wouldn't be possible otherwise," said Peter Warden, Staff Research Engineer at Google. TensorFlow Lite for Microcontrollers is an extension of TensorFlow Lite that addresses the need to run ML on memory-constrained devices with only kilobytes of memory. It comes with a specific set of optimized operations to allow the execution of ML models for applications such as wake-word detection, sound detection, and image wake-up. In addition, developers can add their own ML algorithms.

Broadvoice, an award-winning provider of hosted voice, unified communications (UC), and SIP trunking services for businesses, is reinforcing its commitment to its indirect sales partners with the addition of two new regional sales professionals to its channel management team. Robert Sanchez joins Broadvoice as Regional Channel Manager – Mountain West and Joseph Galluzzi is now Regional Channel Manager – Northeast. Broadvoice also has regional channel managers in California, Southwest, Midwest, and Southeast. "As a channel-first organization, Broadvoice continues to expand its partner community, so we're adding resources and expertise to stay ahead of growing demand," said Kim McLachlan, Senior Vice President of Sales and Marketing. "We're pleased to welcome two veteran telecom sales professionals to the Broadvoice team to provide in-region sales support."

"With the emergence of the Internet it has become easy and quick for anyone who creates something to get their work to the target audience. Now it takes only a few minutes for our work to have universal reach. The Internet comes not only accompanied by advantages. As everything in life it has its is advantages and entails of attention. As it provides quick reach, it also allows for copying and misusing the works a creator makes available. In our work we will address Intellectual Property in the national context, of which we will carry out a scientific study, analyzing how its protection is done and whether there is concern from both the competent authorities and our artists and authors.

"Look with thine ears," Lear tells poor blind Gloucester, and that is exactly what the rest of us should do now we know that the majority of New York theatres will not open their doors until, at the most optimistic guesstimate, the middle of next year. Zoom fatigue set in months ago, but audio is stepping into the breach to take us places that glazed screen-gazing can't. The eyes tend toward the literal, while what we only hear can bloom, the way a novel does, in the privacy of the mind, as is the case with two new productions--one radical, one retro--that use audio to light a path forward for performance in the COVID era and beyond. "A Thousand Ways" (produced by the Brooklyn-based ArKtype) was created by the duo Abigail Browde and Michael Silverstone, who go by the moniker 600 Highwaymen and are known for devising inventive, sincere theatre of a kind that makes urbane audiences fatted on cynicism feel wonder afresh. In "This Great Country," from 2012, seventeen performers, some experienced, some green, acted out scenes from "Death of a Salesman," transforming that classic into something rich and strange; "Employee of the Year," staged in 2014, had five girls under the age of eleven tell the story of one woman's adulthood.

Mining is a traditionally analogue business. After all, the industry's symbol worldwide is a hammer and pick. Yet, despite the sector's antiquated reputation, some major mining companies are taking a progressive stance and proving digitisation and automation can achieve much better operational outcomes. Known as Mine 4.0, the industry is seeing digital transformation creep into everything from trucks, drills and trains to back-office processes, such as procurement and supply chain logistics. Miners have very little control over the revenue side of their business, as the global commodities crash of 2014 to 2015, when prices plunged by more than 30 per cent, and indeed the coronavirus epidemic demonstrate.

Transfer learning is a useful machine learning framework that allows one to build task-specific models (student models) without significantly incurring training costs using a single powerful model (teacher model) pre-trained with a large amount of data. The teacher model may contain private data, or interact with private inputs. We investigate if one can leak or infer such private information without interacting with the teacher model directly. We describe such inference attacks in the context of face recognition, an application of transfer learning that is highly sensitive to personal privacy. Under black-box and realistic settings, we show that existing inference techniques are ineffective, as interacting with individual training instances through the student models does not reveal information about the teacher. We then propose novel strategies to infer from aggregate-level information. Consequently, membership inference attacks on the teacher model are shown to be possible, even when the adversary has access only to the student models. We further demonstrate that sensitive attributes can be inferred, even in the case where the adversary has limited auxiliary information. Finally, defensive strategies are discussed and evaluated. Our extensive study indicates that information leakage is a real privacy threat to the transfer learning framework widely used in real-life situations.