This paper reviews and proposes concerns in adopting, fielding, and maintaining artificial intelligence (AI) systems. While the AI community has made rapid progress, there are challenges in certifying AI systems. Using procedures from design and operational test and evaluation, there are opportunities towards determining performance bounds to manage expectations of intended use. A notional use case is presented with image data fusion to support AI object recognition certifiability considering precision versus distance.
Safe and Certifiable AI Systems: Concepts, Challenges, and Lessons Learned
TRUSTIFAI GMBH（TRUSTIFAI公司）；TÜV AUSTRIA HOLDING AG（TÜV奥地利控股公司）；Software Competence Center Hagenberg（哈根贝格软件竞争力中心）；Johannes Kepler University Linz - Institute for Machine Learning（林茨约瑟夫·约瑟夫斯大学-机器学习研究所）
Enhancing Artificial intelligence Policies with Fusion and Forecasting: Insights from Indian Patents Using Network Analysis
CSIR-National Institute of Science Communication and Policy Research（CSIR国家科学传播与政策研究所）；Centre for Studies in Science Policy, School of Social Sciences, Jawaharlal Nehru University（贾瓦哈拉尔·尼赫鲁大学社会科学学院科学政策研究中心）
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
Massachusetts Institute of Technology（麻省理工学院）；Universidad del Cauca（考卡大学）；University of Michigan（密歇根大学）；Universidad de Chile（智利大学）；São Paulo Federal University（圣保罗联邦大学）；Harvard TH Chan School of Public Health（哈佛陈曾熙公共卫生学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·哈利法大学科学与工程学院）；College of Health and Life Sciences, Hamad Bin Khalifa University（哈马德·本·哈利法大学健康与生命科学学院）
Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a better understanding of human health and provides optimal personalized healthcare. Advances in artificial intelligence (AI) technologies, particularly machine learning (ML), enable the fusion of these different data modalities to provide multimodal insights. To this end, in this scoping review, we focus on synthesizing and analyzing the literature that uses AI techniques to fuse multimodal medical data for different…
In this paper we demonstrate that it is possible to manage intelligence in constant time as a pre-process to information fusion through a series of processes dealing with issues such as clustering reports, ranking reports with respect to importance, extraction of prototypes from clusters and immediate classification of newly arriving intelligence reports. These methods are used when intelligence reports arrive which concerns different events which should be handled independently, when it is not known a priori to which event each intelligence report is related. We use clustering that runs as a …
JD AI Research（京东人工智能研究院）；University of Cambridge（剑桥大学）；Citadel LLC（城堡投资有限责任公司）
Deep learning methods have revolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their input signals. However, many applications in the artificial intelligence field involve multiple modalities. Therefore, it is of broad interest to study the more difficult and complex problem of modeling and learning across multiple modalities. In this paper, we provide a technical review of available models and learning methods for multimodal intelligence. The main focus of this review is the combination of vision a…
Agentic AI-Empowered Conversational Embodied Intelligence Networks in 6G
Key Laboratory of Broadband Wireless Communication and Sensor Network Technology（宽带无线通信与传感网络技术重点实验室）；Nanjing University of Posts and Telecommunications（南京邮电大学）；College of Technological Innovation（技术创新学院）
University of Alcala（阿尔卡拉大学）；Universidad Politécnica de Madrid（马德里理工大学）；TECNALIA（TECNALIA研究中心）；University of the Basque Country (UPV/EHU)（巴斯克大学）
In this paper three customised Artificial Intelligence (AI) frameworks, considering Deep Learning (convolutional neural networks), Machine Learning algorithms and data reduction techniques are proposed, for a problem of long-term summer air temperature prediction. Specifically, the prediction of average air temperature in the first and second August fortnights, using input data from previous months, at two different locations, Paris (France) and Córdoba (Spain), is considered. The target variable, mainly in the first August fortnight, can contain signals of extreme events such as heatwaves, li…
AI-Driven Non-Invasive Detection and Staging of Steatosis in Fatty Liver Disease Using a Novel Cascade Model and Information Fusion Techniques
University of Tehran（德黑兰大学）；York University（约克大学）；Northeastern University（东北大学）；Antalya Bilim University（安塔利亚比尔姆大学）；Research Institute for Gastroenterology and Liver Diseases（胃肠病学和肝病学研究所）；Shahid Beheshti University of Medical Sciences（沙希德贝赫什提医科大学）；University of Waterloo（滑铁卢大学）
LTCI, Télécom ParisTech, Université Paris-Saclay（LTCI，巴黎高等电信学院，巴黎萨克雷大学）；Université Paris-Dauphine, PSL Research University, CNRS（巴黎第九大学，巴黎文理研究大学，法国国家科学研究中心）；Universidad de Los Andes（洛斯安第斯大学）；Universidad Industrial de Santander（桑坦德工业大学）
Several tasks in artificial intelligence require to be able to find models about knowledge dynamics. They include belief revision, fusion and belief merging, and abduction. In this paper we exploit the algebraic framework of mathematical morphology in the context of propositional logic, and define operations such as dilation or erosion of a set of formulas. We derive concrete operators, based on a semantic approach, that have an intuitive interpretation and that are formally well behaved, to perform revision, fusion and abduction. Computation and tractability are addressed, and simple examples…
Artificial Intelligence (AI) plays varying roles in supporting both existing and emerging technologies. In the area of Learning and Tutoring, it plays key role in Intelligent Tutoring Systems (ITS). The fusion of ITS with Adaptive Hypermedia and Multimedia (AHAM) form the backbone of Adaptive eLearning Systems (AES) which provides personalized experiences to learners. This experience is important because it facilitates the accurate delivery of the learning modules in specific to the learner capacity and readiness. AES types vary, with Adaptive Web Based eLearning Systems (AWBES) being the popu…
Integrating Biological and Machine Intelligence: Attention Mechanisms in Brain-Computer Interfaces
The School of Biomedical Engineering（生物医学工程学院）；The Guangdong Provincial Key Laboratory of Biomedical Measurements（生物医学测量与超声成像广东省重点实验室）；Shien-Ming Wu School of Intelligent Engineering（沈铭武智能工程学院）；Institute for Super Robotics（超机器人研究所）
Facebook AI Research（Facebook AI 研究院）；University of Cambridge（剑桥大学）
Meaning has been called the "holy grail" of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences. The field of Artifical Intelligence (AI) is very much a part of that list: the development of sophisticated natural language semantics is a sine qua non for achieving a level of intelligence comparable to humans. Embodiment theories in cognitive science hold that human semantic representation depends on sensori-motor experience; the abundant evidence that human meaning representation is grounded in the perception of physical reality leads to…
This paper describes an approach to the design of a population of cooperative robots based on concepts borrowed from Systems Theory and Artificial Intelligence. The research has been developed under the SocRob project, carried out by the Intelligent Systems Laboratory at the Institute for Systems and Robotics - Instituto Superior Tecnico (ISR/IST) in Lisbon. The acronym of the project stands both for "Society of Robots" and "Soccer Robots", the case study where we are testing our population of robots. Designing soccer robots is a very challenging problem, where the robots must act not only to …
National University of Science and Technology（国家科技大学）
Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) c…
University of Calgary（卡尔加里大学）；Wuhan University（武汉大学）；China University of Geosciences (Beijing)（中国地质大学（北京））；Beijing University of Posts and Telecommunications（北京邮电大学）
The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, artificial intelligence (AI) has become a consensus tool that has been successfully applied in various fields. This artic…
The objective is to present one important aspect of the European IST-FET project "REV!GIS"1: the methodology which has been developed for the translation (interpretation) of the quality of the data into a "fitness for use" information, that we can confront to the user needs in its application. This methodology is based upon the notion of "ontologies" as a conceptual framework able to capture the explicit and implicit knowledge involved in the application. We do not address the general problem of formalizing such ontologies, instead, we rather try to illustrate this with three applications whic…
Information Fusion in Smart Agriculture: Machine Learning Applications and Future Research Directions
Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校梅塔数据科学与人工智能学院）；Indian Statistical Institute（印度统计研究所）；Department of Applied Mathematics and Scientific Computing, Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校应用数学与科学计算系）；University of Chile（智利大学）；VSB-Technical University of Ostrava（奥斯特拉瓦VSB技术大学）；Department of Mathematics, Indian Institute of Technology Roorkee（印度理工学院鲁尔基分校数学系）
With the advances of the Internet of Things (IoT) and 5G/6G wireless communications, the paradigms of mobile computing have developed dramatically in recent years, from centralized mobile cloud computing to distributed fog computing and mobile edge computing (MEC). MEC pushes compute-intensive assignments to the edge of the network and brings resources as close to the endpoints as possible, addressing the shortcomings of mobile devices with regard to storage space, resource optimisation, computational performance and efficiency. Compared to cloud computing, as the distributed and closer infras…
