The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines. This work aims to discover advisor-advisee relationships hidden behind scientific collaboration networks. For this purpose, we propose a novel model based on Network Representation Learning (NRL), namely Shifu2, which takes the collaboration network as input and the identified advisor-advisee relationship as output. In contrast to existing NRL models, Shifu2 considers not only the network structure but also the semantic information of nodes and edges. Shifu2 encodes nodes and edges into low-dimensional vectors respectively, both of which are then utilized to identify advisor-advisee relationships. Experimental results illustrate improved stability and effectiveness of the proposed model over state-of-the-art methods. In addition, we generate a large-scale academic genealogy dataset by taking advantage of Shifu2.

Analysis on Strategies of Leading Players: Market players can use this analysis to gain competitive advantage over their competitors in the Artificial Intelligence for Accounting market. Study on Key Market Trends: This section of the report offers a deeper analysis of the latest and future trends of the Artificial Intelligence for Accounting market. Market Forecasts: Buyers of the report will have access to accurate and validated estimates of the total market size in terms of value and volume. The report also provides consumption, production, sales, and other forecasts for the Artificial Intelligence for Accounting market. Regional Growth Analysis: All major regions and countries have been covered in the report.

A woman who was walking her dog in Milton Keynes, England, saw a delivery robot plunge "straight into the canal." Starship Technologies has urged residents not to worry if they see one of its robots in distress. The company noted, however, that people should report any sightings of their machines "swimming or in any other odd situation." ESPECIALLY BALL HANDLING: A man, shooting hoops in the nude at a park in Longwood, Fla., at 7:30 on a Sunday night, told arresting officers that he thought playing naked would help improve his basketball skills. A 23-year-old man kicked several people out of his family's home in Glassboro, N.J., stole a neighbor's pickup truck and got into an accident involving two other vehicles.

We answer the question which conjunctive queries are uniquely characterized by polynomially many positive and negative examples, and how to construct such examples efficiently. As a consequence, we obtain a new efficient exact learning algorithm for a class of conjunctive queries. At the core of our contributions lie two new polynomial-time algorithms for constructing frontiers in the homomorphism lattice of finite structures. We also discuss implications for the unique characterizability and learnability of schema mappings and of description logic concepts.

Multimodal emotion recognition from speech is an important area in affective computing. Fusing multiple data modalities and learning representations with limited amounts of labeled data is a challenging task. In this paper, we explore the use of modality-specific "BERT-like" pretrained Self Supervised Learning (SSL) architectures to represent both speech and text modalities for the task of multimodal speech emotion recognition. By conducting experiments on three publicly available datasets (IEMOCAP, CMU-MOSEI, and CMU-MOSI), we show that jointly fine-tuning "BERT-like" SSL architectures achieve state-of-the-art (SOTA) results. We also evaluate two methods of fusing speech and text modalities and show that a simple fusion mechanism can outperform more complex ones when using SSL models that have similar architectural properties to BERT.

Every year, around 1.3 million people all over the world are killed by road mishaps with approximately 20 to 50 million life-threatening injuries(International Transport Forum, 2018; World Health Organisation, 2018). Notwithstanding, there is a disparity in road traffic death from 9.3 to 26.6 per 100,000 population among countries based on their income level, while the global rate is still 18.2 per 100,000 population (World Health Organisation, 2018). Moreover, traffic collision at intersections is a significant threat to upholding road safety. As a whole, 45% of severe injuries occur at intersections, including 22% of fatal crashes (Li, Jia, et al., 2016). Drivers often inadvertently fail to break immediately at the onset of red light or deliberately run through the red light signal and also miscalculate the motif of the right angle vehicle [in a right-hand driving condition] while crossing the intersection (Zhang et al., 2018). Especially at the onset of yellow signal, drivers get confused with decision measurement either to stop or to run and to get involved in rear-end collision or right-angle collision or uncomfortable hard brake, often resulting in injuries or death (Gazis et al., 1960; Majhi & Senathipathi, 2019).

Robots are becoming an important way to deliver health care, and personality is vital to understanding their effectiveness. Despite this, there is a lack of a systematic overarching understanding of personality in health care human robot interaction (H-HRI). To address this, the authors conducted a review that identified 18 studies on personality in H-HRI. This paper presents the results of that systematic literature review. Insights are derived from this review regarding the methodologies, outcomes, and samples utilized. The authors of this review discuss findings across this literature while identifying several gaps worthy of attention. Overall, this paper is an important starting point in understanding personality in H-HRI.

The focus of the 2018 Logic and Practice of Programming workshop was on logic and declarative languages for the practice of programming. Of particular interest were languages (1) that have a clear semantic foundation, so that they can be used for concise modeling of complex application problems, facilitating formal proofs and automated analysis, and (2) that are also implementable, so that the implementations can run as specified, as part of real applications. Also of interest were (a) the design of declarative languages, libraries, and tools that facilitate the construction of complex systems and applications, (b) approaches to integrate declarative and procedural programming, and (c) the use of declarative languages to facilitate other programming paradigms, e.g., distributed programming. The target audience for these languages was students who wish to model complex application problems, and practitioners who want to use them. The goal of the workshop was to bring together the best people and best languages, tools, and ideas to help improve logic languages for the practice of programming and to improve the practice of programming with logic and declarative programming.

Immigration and Customs Enforcement (ICE) this week signed a deal with Clearview AI to licence the facial recognition company's technology. According to a federal purchase order unearthed by the nonprofit Tech Inquiry (via The Verge), an ICE mission support office in Dallas is paying $224,000 for "Clearview licenses." Engadget has contacted Clearview and ICE for details on the scope of this agreement, as well as what ICE plans to do with those licenses. ICE and Clearview signed the deal just as the company is set to defend itself in court. Lawsuits filed in a number of states accuse Clearview of violating privacy and safety laws. It can identify a person by matching their photo against billions of images it has scraped from social media and other internet services.

Once a year, a call goes out to thousands of employees in Adobe offices around the world. Anyone in the company, from engineers and data scientists, to UX designers and product managers, have a chance to put forth new and innovative ideas that evolve the way brands engage consumers digitally. Over time, Adobe Sneaks have become an important innovation engine for Adobe, working its way into our apps and delivering popular features such as an AI assistant in Adobe Analytics and AI-driven asset tagging in Adobe Experience Manager. The submissions this year, focused on AI and mixed reality technologies, were whittled down to a final set of 7 projects that we share publicly. And while these projects are not yet available, around 60 percent of Sneaks eventually make it into an Adobe product.