Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field.

New Jersey, NJ---- 07/19/2022-- The Global Artificial Intelligence in Insurance Market Report assesses developments relevant to the insurance industry and identifies key risks and vulnerabilities for the Artificial Intelligence in Insurance Industry to make stakeholders aware with current and future scenarios. To derive complete assessment and market...

America must work with rival nations to develop international norms for developing technologies such artificial intelligence or face increasingly difficult challenges in tackling misinformation and cyberwarfare, experts have said. "I like to think of this as sort of where things were 20 years ago in tech, where we were incredibly naïve," said Eric Schmidt, former Google CEO and current Chairman of the National Security Commission on Artificial Intelligence, said Friday at the Aspen Security Forum. "I was very naive about the impact of what we were doing. I now understand that information is everything: It's incredibly powerful." Much of the security forum focused on various challenges the United States and western allies face at the international level from rival nations Russia, China and Iran.

Training and testing supervised object detection models require a large collection of images with ground truth labels. Labels define object classes in the image, as well as their locations, shape, and possibly other information such as pose. The labeling process has proven extremely time consuming, even with the presence of manpower. We introduce a novel labeling tool for 2D images as well as 3D triangular meshes: 3D Labeling Tool (3DLT). This is a standalone, feature-heavy and cross-platform software that does not require installation and can run on Windows, macOS and Linux-based distributions. Instead of labeling the same object on every image separately like current tools, we use depth information to reconstruct a triangular mesh from said images and label the object only once on the aforementioned mesh. We use registration to simplify 3D labeling, outlier detection to improve 2D bounding box calculation and surface reconstruction to expand labeling possibility to large point clouds. Our tool is tested against state of the art methods and it greatly surpasses them in terms of speed while preserving accuracy and ease of use.

Stereotypes, bias, and discrimination have been extensively documented in Machine Learning (ML) methods such as Computer Vision (CV) [18, 80], Natural Language Processing (NLP) [6], or both, in the case of large image and caption models such as OpenAI CLIP [14]. In this paper, we evaluate how ML bias manifests in robots that physically and autonomously act within the world. We audit one of several recently published CLIP-powered robotic manipulation methods, presenting it with objects that have pictures of human faces on the surface which vary across race and gender, alongside task descriptions that contain terms associated with common stereotypes. Our experiments definitively show robots acting out toxic stereotypes with respect to gender, race, and scientifically-discredited physiognomy, at scale. Furthermore, the audited methods are less likely to recognize Women and People of Color. Our interdisciplinary sociotechnical analysis synthesizes across fields and applications such as Science Technology and Society (STS), Critical Studies, History, Safety, Robotics, and AI. We find that robots powered by large datasets and Dissolution Models (sometimes called "foundation models", e.g. CLIP) that contain humans risk physically amplifying malignant stereotypes in general; and that merely correcting disparities will be insufficient for the complexity and scale of the problem. Instead, we recommend that robot learning methods that physically manifest stereotypes or other harmful outcomes be paused, reworked, or even wound down when appropriate, until outcomes can be proven safe, effective, and just. Finally, we discuss comprehensive policy changes and the potential of new interdisciplinary research on topics like Identity Safety Assessment Frameworks and Design Justice to better understand and address these harms.

In this paper, we propose five prediction intervals for the beta autoregressive moving average model. This model is suitable for modeling and forecasting variables that assume values in the interval $(0,1)$. Two of the proposed prediction intervals are based on approximations considering the normal distribution and the quantile function of the beta distribution. We also consider bootstrap-based prediction intervals, namely: (i) bootstrap prediction errors (BPE) interval; (ii) bias-corrected and acceleration (BCa) prediction interval; and (iii) percentile prediction interval based on the quantiles of the bootstrap-predicted values for two different bootstrapping schemes. The proposed prediction intervals were evaluated according to Monte Carlo simulations. The BCa prediction interval offered the best performance among the evaluated intervals, showing lower coverage rate distortion and small average length. We applied our methodology for predicting the water level of the Cantareira water supply system in S\~ao Paulo, Brazil.

Document-level relation extraction (RE) aims to identify the relations between entities throughout an entire document. It needs complex reasoning skills to synthesize various knowledge such as coreferences and commonsense. Large-scale knowledge graphs (KGs) contain a wealth of real-world facts, and can provide valuable knowledge to document-level RE. In this paper, we propose an entity knowledge injection framework to enhance current document-level RE models. Specifically, we introduce coreference distillation to inject coreference knowledge, endowing an RE model with the more general capability of coreference reasoning. We also employ representation reconciliation to inject factual knowledge and aggregate KG representations and document representations into a unified space.

Santiago Miret is a deep learning researcher at Intel Labs, where he focuses on developing artificial intelligence (AI) solutions and exploring the intersection of AI and the physical sciences. The successful design and deployment of novel material technologies in the last couple of decades has enabled tremendous innovations across various industries. Building today's smartphones, for example, would have cost about 100 million dollars in the 1980s and yielded a 14 meters tall device, both of which would be very impractical. Furthermore, materials innovations surrounding silicon have enabled advances in microelectronics and computer technologies that build the foundation of a technology-enabled world, including the recent proliferation of artificial intelligence (AI). Similar, albeit different advances, in silicon technology and perovskites, a class of semiconductor materials that transport the electric charge of light, have provided the basis for solar photovoltaic cells which enable the harvesting of renewable solar energy thereby driving a redesign of the energy industry to a more sustainable and less carbon-heavy system.

In this work we improve forecasting of Sea Surface A recent and promising line of work consists of combining Height (SSH) and current velocity (speed and direction) ML with physics-based models -- often referred to as in oceanic scenarios. We do so by resorting Physics-Informed Machine Learning (PIML). Such an approach to Random Forests so as to predict the error of a numerical aims to take advantage of both the power of pattern forecasting system developed for the Santos recognition given by ML approaches and the power of generalization Channel in Brazil. We have used the Santos Operational in unseen scenarios given by the physics-based Forecasting System (SOFS) and data collected model. in situ between the years of 2019 and 2021. This work expands on our previous work [Moreno et al., In previous studies we have applied similar methods 2022] where PIML was used to correct the error predicted for current velocity in the channel entrance, in by a numerical model of the speed of water current in a this work we expand the application to improve the measuring station. Our main contribution here consists of SHH forecast and include four other stations in the inserting a correction for the direction of the water current channel. We have obtained an average reduction and the sea surface height (SSH) predicted by the numerical of 11.9% in forecasting Root-Mean Square Error model into the PIML model. In addition, we expand the (RMSE) and 38.7% in bias with our approach. We corrections to other measurement stations in the Santos-São also obtained an increase of Agreement (IOA) in 10 Vicente-Bertioga Estuarine System region on the Brazilian of the 14 combinations of forecasted variables and coast.

Human fall is one of the very critical health issues, especially for elders and disabled people living alone. The number of elder populations is increasing steadily worldwide. Therefore, human fall detection is becoming an effective technique for assistive living for those people. For assistive living, deep learning and computer vision have been used largely. In this review article, we discuss deep learning (DL)-based state-of-the-art non-intrusive (vision-based) fall detection techniques. We also present a survey on fall detection benchmark datasets. For a clear understanding, we briefly discuss different metrics which are used to evaluate the performance of the fall detection systems. This article also gives a future direction on vision-based human fall detection techniques.