PTZ-Surveillance coverage based on artificial intelligence for smart cities

Surveillance cameras have a plethora of usages in newly born cities including smart traffic, healthcare, monitoring, and meeting security needs. One of the most famous new cites is the Egypt's new administration capital “New Cairo”. The new administration capital of Egypt mainly characterizes with the green life style via the "Green River ". In this paper, a new enhanced Artificial Intelligence (AI) algorithm is introduced for adjusting the orientation of Pan–Tilt–Zoom (PTZ) surveillance cameras in new Cairo. In other words, the new proposed algorithm is used for improving the field of view (FOV) coverage of PTZ cameras network. For validating the proposed algorithm, it is tested on many scenarios with different criterions. After that, the proposed algorithm is applied to adjust the PTZ monitoring cameras in the green river which locates on new administrative capital as an equivalent to the river Nile. In addition, it compared with several other AI algorithms through the appropriate statistical analysis. The overall experimental results indicate the prosperity of the proposed algorithm for increasing the coverage of the PTZ surveillance system.     

Inspired,but not mimicking: a conversation between artificial intelligence and human intelligenceOA

<正>How intelligent is artificial intelligence(AI)? How intelligent will it become in the future? What is the relationship between AI and human intelligence(HI)? These questions have been a hot topic of discussion in recent years, but no consensus has yet been reached. To discuss these issues, we should first understand the concept of intelligence as well as the underlying mechanisms for both HI and AI. In this NSR Forum, experts from both disciplines gathered to discuss these issues; in pa...     

Human-aligned artificial intelligence is a multiobjective problem

As the capabilities of artificial intelligence (AI) systems improve, it becomes important to constrain their actions to ensure their behaviour remains beneficial to humanity. A variety of ethical, legal and safety-based frameworks have been proposed as a basis for designing these constraints. Despite their variations, these frameworks share the common characteristic that decision-making must consider multiple potentially conflicting factors. We demonstrate that these alignment frameworks can be represented as utility functions, but that the widely used Maximum Expected Utility (MEU) paradigm provides insufficient support for such multiobjective decision-making. We show that a Multiobjective Maximum Expected Utility paradigm based on the combination of vector utilities and non-linear action–selection can overcome many of the issues which limit MEU’s effectiveness in implementing aligned AI. We examine existing approaches to multiobjective AI, and identify how these can contribute to the development of human-aligned intelligent agents.     

An efficient smart data mining framework based cloud internet of things for developing artificial intelligence of marketing information analysis

Because of the big volume of marketing data, a human analyst would be unable to uncover any useful information for marketing that could aid in the process of making decision. Smart Data Mining (SDM), which is considered an important field from Artificial Intelligence (AI) is completely assisting in the performance business management analytics and marketing information. In this study, most reliable six algorithms in SDM are applied; Naïve Bayes (NB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), ID3, and C4.5 on actual data of marketing for bank that taken from Cloud Internet of Thing (CIoT). The objectives of this study are to build an efficient framework to increase campaign of marketing for banks by identifying main characteristics that affect a success and to test the performance of CIoT and SDM algorithms. This study is expected to enhance the scientific contributions to investigating the marketing information capacities by integrating SDM with CIoT. The performances of SDM algorithms are calculated by eight measures; accuracy, balance accuracy, precision, mean absolute error, root mean absolute error, recall, F1- Score and running time. The experimental findings show that the proposed framework is successful, with higher accuracies and good performance. Results revealed that customer service & marketing tactics are essential for a Company’ success & survival. Also, the C4.5 has accomplished better achievement than the SVM, RF, LR, NB, & ID3. At the end, CIoT Platform was evaluated by response time, request rate & processing of bank data.     

30 years of intelligence models in management and business: A bibliometric review

The critical factors in the big data era are collection, analysis, and dissemination of information to improve an organization’s competitive position and enhance its products and services. In this scenario, it is imperative that organizations use Intelligence, which is understood as a process of gathering, analyzing, interpreting, and disseminating high-value data and information at the right time for use in the decision-making process. Earlier, the concept of Intelligence was associated with the military and national security sector; however, in present times, and as organizations evolve, Intelligence has been defined in several ways for the purposes of different applications. Given that the purpose of Intelligence is to obtain real value from data, information, and the dynamism of the organizations, the study of this discipline provides an opportunity to analyze the core trends related to data collection and processing, information management, decision-making process, and organizational capabilities. Therefore, the present study makes a conceptual analysis of the existing definitions of intelligence in the literature by quantifying the main bibliometric performance indicators, identifying the main authors and research areas, and evaluating the development of the field using SciMAT as a bibliometric analysis software.     

Towards an implementation framework for business intelligence in healthcare

As healthcare organizations continue to be asked to do more with less, access to information is essential for sound evidence-based decision making. Business intelligence (BI) systems are designed to deliver decision-support information and have been repeatedly shown to provide value to organizations. Many healthcare organizations have yet to implement BI systems and no existing research provides a healthcare-specific framework to guide implementation. To address this research gap, we employ a case study in a Canadian Health Authority in order to address three questions: (1) what are the most significant adverse impacts to the organization's decision processes and outcomes attributable to a lack of decision-support capabilities? (2) what are the root causes of these impacts, and what workarounds do they necessitate? and (3) in light of the issues identified, what are the key considerations for healthcare organizations in the early stages of BI implementation? Using the concept of co-agency as a guide we identified significant decision-related adverse impacts and their root causes. We found strong management support, the right skill sets and an information-oriented culture to be key implementation considerations. Our major contribution is a framework for defining and prioritizing decision-support information needs in the context of healthcare-specific processes.     

人工情感与人工智能应用研究

生活世界中人工情感研发得到快速发展,但还不能比及人类情感。语境残破和缺身涉入是人工情感不及人类情感真挚、真切的两个重要原因。建构语境与虚拟身体的协同创新有助于增强人工情感的逼真度。人工情感在教育、客服、营销、制造、安防等领域的应用,将有助于提高人机交互的有效性、体验性和真实性。随着人工情感的增强,我们既要积极支持,同时也要做好应对因人工情感所带来各种风险的防控。     

Fashion analysis and understanding with artificial intelligence

As handling fashion big data with Artificial Intelligence (AI) has become exciting challenges for computer scientists, fashion studies have received increasing attention in computer vision, machine learning and multimedia communities in the past few years. In this paper, introduce the progress in fashion research and provide a taxonomy of these fashion studies that include low-level fashion recognition, middle-level fashion understanding and high-level fashion applications. Finally, we discuss the challenges that when the fashion industry faces AI technologies.     

A perspective on natural and artificial intelligence

Abstract

Intelligence is an attribute that has, since time immemorial, drawn the line of distinction between man and machine. Artificial Intelligence (AI) refers to the endeavor of the former to introduce some of this special faculty into the latter. Just as natural intelligence has undergone major changes as regards its definitions and understanding, so has the field of AI. In order to encompass the gamut of this fundamental capability and know its origins, AI researchers have often had to deal with psychological and philosophical viewpoints on the issue. From the point of view of cognitive psychology, the Information Processing (IP) paradigm and IP systems are of special interest, and we present a brief overview of these topics. While the AI community claims to have some understanding of the concept of knowledge, the idea of consciousness, which we consider of finer grain than any other, has received little practical attention. These related terms are discussed at length in the article. Further, of late there has been a movement toward incorporating a background of common‐sense reasoning in AI systems. We emphasize the importance of this trend, especially in distributed AI. The basics of adaptability and learning are also discussed. We sum up the ideas presented and link them to the current progress in AI research with specifics aimed at making it more dynamic.     

A privacy preserving framework for federated learning in smart healthcare systems

Federated Learning (FL) is a platform for smart healthcare systems that use wearables and other Internet of Things enabled devices. However, source inference attacks (SIAs) can infer the connection between physiological data in training datasets with FL clients and reveal the identities of participants to the attackers. We propose a comprehensive smart healthcare framework for sharing physiological data, named FRESH, that is based on FL and ring signature defense from the attacks. In FRESH, physiological data are collected from individuals by wearable devices. These data are processed by edge computing devices (e.g., mobile phones, tablet PCs) that train ML models using local data. The model parameters are uploaded by edge computing devices to the central server for joint training of FL models of disease prediction. In this procedure, certificateless ring signature is used to hide the source of parameter updates during joint training for FL to effectively resist SIAs. In the proposed ring signature schema, an improved batch verification algorithm is designed to leverage additivity of linear operations on elliptic curves and to help reduce the computing workload of the server. Experimental results demonstrate that FRESH effectively reduces the success rate of SIAs and the batch verification method significantly improves the efficiency of signature verification. FRESH can be applied to large scale smart healthcare systems with FL involving large numbers of users.     

An explainable artificial intelligence approach for financial distress prediction

External stakeholders require accurate and explainable financial distress prediction (FDP) models. Complex machine learning algorithms offer high accuracy, but most of them lack explanatory power, resulting in external stakeholders being cautious in adopting them. Therefore, an explainable artificial intelligence approach including a whole process ensemble method and an explainable frame for FDP is here proposed. The ensemble algorithm from feature selection to predictor construction can achieve high accuracy according to the actual case, and the interpretation framework can meet the needs of external users by generating local explanations and global explanations. First, a two-stage scheme integrated with a filter and wrapper technique is designed for feature selection. Second, multiple ensemble models are explored and they are evaluated according to the actual case. Finally, Shapley additive explanations, counterfactual explanations and partial dependence plots are employed to enhance model interpretability. Taking financial data of Chinese listed companies from 2007 to 2020 as a dataset, the highest AUC is ensured by LightGBM with a value of 0.92. Local explanations help individual enterprises identify the key features which lead to their financial distress, and counterfactual explanations are produced to provide improvement strategies. By analyzing the features importance and the impact of feature interaction on the results, global explanations can improve the transparency and credibility of ‘black box’ models.     

Disaster City Digital Twin: A vision for integrating artificial and human intelligence for disaster management

This paper presents a vision for a Disaster City Digital Twin paradigm that can: (i) enable interdisciplinary convergence in the field of crisis informatics and information and communication technology (ICT) in disaster management; (ii) integrate artificial intelligence (AI) algorithms and approaches to improve situation assessment, decision making, and coordination among various stakeholders; and (iii) enable increased visibility into network dynamics of complex disaster management and humanitarian actions. The number of humanitarian relief actions is growing due to the increased frequency of natural and man-made crises. Various streams of research across different disciplines have focused on ICT and AI solutions for enhancing disaster management processes. However, most of the existing research is fragmented without a common vision towards a converging paradigm. Recognizing this, this paper presents the Disaster City Digital Twin as a unifying paradigm. The four main components of the proposed Digital Twin paradigm include: multi-data sensing for data collection, data integration and analytics, multi-actor game-theoretic decision making, and dynamic network analysis. For each component, the current state of the art related to AI methods and approaches are examined and gaps are identified.     

The ethical use of artificial intelligence in human resource management: a decision-making framework

Ethics and Information Technology - Artificial intelligence (AI) is increasingly inputting into various human resource management (HRM) functions, such as sourcing job applicants and selecting...     

Consumer engagement via interactive artificial intelligence and mixed reality

The use of immersive technologies has changed the consumption environment in which retailers provide services. We present findings from a study designed to investigate consumer responses toward a $17 million AI-embedded mixed reality (MR) exhibit in a retail/entertainment complex which combines advanced technology entertainment with retail shopping. Findings from our study demonstrate that the quality of AI (i.e., speech recognition and synthesis via machine learning) associated with an augmented object increases MR immersion associated with spatial immersion, MR enjoyment, and consumers’ perceptions of novel experiences. Collectively, these increase consumer engagement, and positively influence behavioral responses—specifically, purchase intentions and intentions to share experiences with social groups. Overall, findings from this study show that interactive AI and MR technology open new avenues to promote consumer engagement.     

Business Innovation based on artificial intelligence and Blockchain technology

The growing business evolution and the latest Artificial Intelligence (AI) make the different business practices to be enhanced by the ability to create new means of collaboration. Such growing technology helps to deliver brand services and even some new kinds of corporate interactions with customers and staff. AI digitization simultaneously emphasized businesses to focus on the existing strategies and regularly and early pursue new market opportunities. While digital technology research in the framework of business innovation is gaining greater interest and the privacy of data can be maintained by Blockchain technology. Therefore in this paper, Business Innovation based on artificial intelligence and Blockchain technology (BI-AIBT) has been proposed to enhance the business practices and maintain the secured interaction among the various clients. The collection of qualitative empirical data is made up of few primary respondents from two distinct business sectors. BI-AIBT has been evaluated by undertaking and exploring the difference and similarities between digitalization's impact on value development, proposal, and business capture. Besides, organizational capacities and staff skills interaction issues can be improved by BT. The experimental result suggests that digital transformation is usually regarded as essential and improves business innovation strategies. The numerical result proposed BI-AIBT improves the demand prediction ratio (97.1%), product quality ratio (98.3%), Business development ratio (98.9%), customer behavior analysis ratio (96.3%), and customer satisfaction ratio (97.2%).     

Anticipatory innovation of professional services: The case of auditing and artificial intelligence

With the rise of artificial intelligence (AI), professional services firms (PSFs) need to innovate their services to adapt to AI. However, traditional ad hoc innovations driven by individual professionals have limitations in incorporating new technology outside their expertise. Although service R&D—an organizational function for centralized coordination of service innovations in strategically targeted areas—is potentially effective, studies on service R&D have still been scarce. This case study aims to fill the gap by examining how PSFs can establish and utilize service R&D to innovate services, overcoming the challenges of AI adoption. An in-depth qualitative study was conducted on the process by which the Big Four audit firms incorporated AI into their external audit service in Japan in the 2010s. The analysis shows the detailed process of how newly created service R&D organizations advanced AI adoption in the case firms. This study contributes to the literature on innovations in services and PSFs by (1) demonstrating the neglected but critical role of service R&D as an innovation enabler beyond the existing expertise of service firms, (2) constructing a three-phase model of the evolution of the service R&D function, and (3) suggesting the significance of innovation process design for the legitimation of innovations. This study also expands our knowledge of AI adoption, presenting a process tailored to address the challenges inherent in AI adoption for PSFs.     

Managing the knowledge environment: a case study from healthcare

An organization's future is viable to the degree it can create, obtain, and leverage its intellectual capital in an effort to manage knowledge for sustained, competitive advantage in the market place. Failure to do so can spell disaster. Case in point: Due to a festering crisis between his strategic intent and the organization's operational capacity to support it, in May 2000, the Spartanburg Regional Healthcare System (SRHS) board of directors voted for its chief executive officer (CEO) to resign. His resignation signaled the need for new learning, in addition to more effective management and communication practices in improving the identifying and codifying of knowledge and then facilitating the sharing of it organization-wide. This article focuses on delineating the process principles in managing a supportive environment necessary for the sharing of knowledge to spark creative thinking in devising innovative solutions that the hospital used in adapting to its market.