scholarly journals Where are we? Using Scopus to map the literature at the intersection between artificial intelligence and research on crime

2020 ◽  
Author(s):  
Gian Maria Campedelli
Keyword(s):  
Artificial Intelligence ◽  
Scientific Collaboration ◽  
The United States ◽  
Social Phenomena ◽  
International Partnerships ◽  
Current State ◽  
Scientific Disciplines ◽  
Research Problems ◽  
Disconnected Structure ◽  
Using Data

Abstract Research on artificial intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime-related research problems. Nonetheless, studies that map the current state of the art at the intersection between AI and crime are lacking. What are the current research trends in terms of topics in this area? What is the structure of scientific collaboration when considering works investigating criminal issues using machine learning, deep learning, and AI in general? What are the most active countries in this specific scientific sphere? Using data retrieved from the Scopus database, this work quantitatively analyzes 692 published works at the intersection between AI and crime employing network science to respond to these questions. Results show that researchers are mainly focusing on cyber-related criminal topics and that relevant themes such as algorithmic discrimination, fairness, and ethics are considerably overlooked. Furthermore, data highlight the extremely disconnected structure of co-authorship networks. Such disconnectedness may represent a substantial obstacle to a more solid community of scientists interested in these topics. Additionally, the graph of scientific collaboration indicates that countries that are more prone to engage in international partnerships are generally less central in the network. This means that scholars working in highly productive countries (e.g. the United States, China) tend to mostly collaborate domestically. Finally, current issues and future developments within this scientific area are also discussed.

scholarly journals Where Are We? Using Scopus to Map the Literature at the Intersection Between Artificial Intelligence and Research on Crime

2019 ◽  
Author(s):  
Gian Maria Campedelli
Keyword(s):  
Artificial Intelligence ◽  
Scientific Collaboration ◽  
The United States ◽  
Social Phenomena ◽  
International Partnerships ◽  
Current State ◽  
Scientific Disciplines ◽  
Research Problems ◽  
Disconnected Structure ◽  
Using Data

Research on Artificial Intelligence (AI) applications has spread over many scientific disciplines. Scientists have tested the power of intelligent algorithms developed to predict (or learn from) natural, physical and social phenomena. This also applies to crime-related research problems. Nonetheless, studies that map the current state of the art at the intersection between AI and crime are lacking. What are the current research trends in terms of topics in this area? What is the structure of scientific collaboration when considering works investigating criminal issues using machine learning, deep learning, and AI in general? What are the most active countries in this specific scientific sphere? Using data retrieved from the Scopus database, this work quantitatively analyzes 692 published works at the intersection between AI and crime employing network science to respond to these questions. Results show that researchers are mainly focusing on cyber-related criminal topics and that relevant themes such as algorithmic discrimination, fairness, and ethics are considerably overlooked. Furthermore, data highlight the extremely disconnected structure of co-authorship networks. Such disconnectedness may represent a substantial obstacle to a more solid community of scientists interested in these topics. Additionally, the graph of scientific collaboration indicates that countries that are more prone to engage in international partnerships are generally less central in the network. This means that scholars working in highly productive countries (e.g. the United States, China) tend to mostly collaborate domestically. Finally, current issues and future developments within this scientific area are also discussed.

Effects of Deep Learning Technologies on Employment in the Field of Digital Communication Systems

2021 ◽  
Vol 12 (4) ◽  
pp. 35-42
Author(s):  
Thomas Alan Woolman ◽  
Philip Lee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Communication Systems ◽  
Digital Communication ◽  
The United States ◽  
Learning Technologies ◽  
Technological Unemployment ◽  
Current State ◽  
Challenges And Opportunities ◽  
Digital Communication Systems

There are significant challenges and opportunities facing the economies of the United States in the coming decades of the 21st century that are being driven by elements of technological unemployment. Deep learning systems, an advanced form of machine learning that is often referred to as artificial intelligence, is presently reshaping many aspects of traditional digital communication technology employment, primarily network system administration and network security system design and maintenance. This paper provides an overview of the current state-of-the-art developments associated with deep learning and artificial intelligence and the ongoing revolutions that this technology is having not only on the field of digital communication systems but also related technology fields. This paper will also explore issues and concerns related to past technological unemployment challenges, as well as opportunities that may be present as a result of these ongoing technological upheavals.

scholarly journals Public Sense of Water Fluoridation as Reflected on Twitter 2009–2017

2019 ◽  
Vol 99 (1) ◽  
pp. 11-17 ◽  
Author(s):  
H.J. Oh ◽  
C.H. Kim ◽  
J.G. Jeon
Keyword(s):  
Information Sources ◽  
The United States ◽  
Online Information ◽  
Water Fluoridation ◽  
The Public ◽  
Network Analyses ◽  
Current State ◽  
Twitter Users ◽  
Using Data ◽  
Negative Sentiment

Though controversial, water fluoridation has been hailed as one of the top-ten public-health achievements of the 20th century in the United States of America. In this article, we aim to investigate the public sense of water fluoridation as reflected on Twitter, using data from 2009 to 2017. To this end, tweets related to water fluoridation were collected using queries such as “fluoridated water or fluoride water,” “water fluoridation or fluoridation of water,” and hashtags related to water fluoridation. The collected tweets ( n = 218,748) were examined through informetric, linguistic (word sentiment, word frequency, and word network analyses), and issue tweet analyses. We found that Twitter users who tweeted about water fluoridation in English between 2009 and 2017 constituted about <0.01% of all users including non-English users. In their tweets, words such as “poison” and “waste” were the strong negative sentiment words most often used. Of the top 30 words most frequently used, words related to information sources on water fluoridation and the safety of water fluoridation appeared more often than words related to its efficacy. Additionally, the words related to information sources on water fluoridation and the safety of water fluoridation were found to be core terms in the sentences of tweet mentions. Our linguistic analyses indicate that Twitter users responded sensitively to words that emphasize negative aspects of fluoridation. This is clearly shown in our issue tweet analysis, where tweet mentions expressing negative opinions about water fluoridation accounted for at least 59.2% of all mentions. By contrast, <15% of tweet mentions were found to be positive. These findings suggest that professionals need to reevaluate the current state of online information about water fluoridation, and improve it in a way so that the public can easily access reliable information sources.

The Risks of Operational Militarization: Increased Conflict Against Militarized Police

2017 ◽  
Vol 23 (3) ◽  
Author(s):  
Kevin R. Carriere ◽  
William Encinosa
Keyword(s):  
Police Officers ◽  
Fixed Effects ◽  
Negative Binomial ◽  
The United States ◽  
Significant Relation ◽  
Current State ◽  
Local Police ◽  
Level Data ◽  
Defense Logistics Agency ◽  
Using Data

AbstractThe current state of race relations in the United States have brought to light the issue of the militarization of local police, where officers are being provided with unused equipment from the government’s war chest through the 1033 Program. But, is this increase in militarization beneficial, or does it harm relations between citizens and police? Using data on purchases provided by the Defense Logistics Agency, this paper analyzes effects of military purchases on assaults on police officers. Fixed effects negative binomial regressions on state-month level data show that stockpiling of material militarization equipment (guns, armor, and clothing) exhibits a statistically significant decrease in assaults, with guns showing no significant relation on assaults. However, operational militarization purchases (surveillance, sonar, and radar) lead to an increase of assaults, suggesting that there may be unforeseen consequences of increased militarization due to a change of structure and information gathering.

scholarly journals The U.S. AI Workforce: Understanding the Supply of AI Talent

2020 ◽  
Author(s):  
Diana Gehlhaus ◽  
Santiago Mutis
Keyword(s):  
Artificial Intelligence ◽  
United States ◽  
Size Composition ◽  
The United States ◽  
Preliminary Assessment ◽  
Competitive Edge ◽  
Current State ◽  
Key Characteristics ◽  
The U.S

As the United States seeks to maintain a competitive edge in artificial intelligence, the strength of its AI workforce will be of paramount importance. In order to understand the current state of the domestic AI workforce, Diana Gehlhaus and Santiago Mutis define the AI workforce and offer a preliminary assessment of its size, composition, and key characteristics. Among their findings: The domestic supply of AI talent consisted of an estimated 14 million workers (or about 9% of total U.S. employment) as of 2018.

scholarly journals Artificial Intelligence in China: The Current State of Industry and Development Trends

2020 ◽  
Vol 12 (4) ◽  
pp. 588-606
Author(s):  
Polina E. Strukova ◽  
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Intelligent Systems ◽  
The United States ◽  
High Tech ◽  
Government Control ◽  
High Tech Industry ◽  
Current State ◽  
Historical Factors ◽  
Recent Trends

Currently, in many countries of the world, developments in the field of artificial intelligence are given priority. Among the main countries competing for leadership in this area, China is gaining more and more weight, surpassing the United States of America in America who is considered the undisputed market leader. Despite tight government control and generous financial support for the artificial intelligence sector, which leads to the industry’s boom, China faces certain difficulties in developing this high-tech industry. Some of these difficulties are due to historical factors, while others are due to the state of the industry’s market. The country’s leaders are planning to overcome some of them by reforming related industries and introducing specific approaches to strengthen the position of Chinese companies involved in developments in machine learning, deep learning, natural language processing, computer vision fields, as well as working on projects in the field of big data analysis, autonomous intelligent systems, etc. This article provides an overview of the current state of the artificial intelligence industry in China and analyzes the recent trends of this market in China.

scholarly journals Examining the State of Public-Private Partnership (PPP) Institutionalization in the United States

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Carter Casady ◽  
Kent Eriksson ◽  
Raymond Levitt ◽  
W. Richard Scott
Keyword(s):  
United States ◽  
Case Analysis ◽  
The United States ◽  
Public Private Partnerships ◽  
Development Projects ◽  
Infrastructure Development ◽  
Private Partnership ◽  
Current State ◽  
Using Data ◽  
The U.S

Globally, public-private partnerships (PPPs) have increased in popularity as an alternative procurement model for infrastructure development projects. While PPPs have been widely researched and remain subject to extensive debate, the process of PPP institutionalization has been largely overlooked. To address this knowledge gap, we utilize a combination of both Johnson et al.’s (2006) four phases of institutionalization—innovation, local validation, diffusion, and general validation—and Mrak’s (2014) three models of PPP institutionalization—centralized, decentralized, and mixed—to examine the current state of the U.S. PPP market. Using data on 368 U.S. PPP projects from Inframation’s global transactions database, our case analysis indicates America’s PPP institutionalization process is strongly decentralized and currently in a state of diffusion. Our analysis also suggests general validation of PPPs in the U.S. will likely be predicated on shifting to a mixed PPP institutionalization model.

Migration and the Internet

2017 ◽  
Vol 14 (3) ◽  
pp. 331-342 ◽  
Author(s):  
Thomas John Cooke ◽  
Ian Shuttleworth
Keyword(s):  
United States ◽  
Information And Communication Technologies ◽  
Unobserved Heterogeneity ◽  
Panel Study ◽  
The United States ◽  
Negative Effect ◽  
Information And Communication ◽  
Using Data ◽  
And Migration ◽  
Dynamics Models

It is widely presumed that information and communication technologies, or ICTs, enable migration in several ways; primarily by reducing the costs of migration. However, a reconsideration of the relationship between ICTs and migration suggests that ICTs may just as well hinder migration; primarily by reducing the costs of not moving.  Using data from the US Panel Study of Income Dynamics, models that control for sources of observed and unobserved heterogeneity indicate a strong negative effect of ICT use on inter-state migration within the United States. These results help to explain the long-term decline in internal migration within the United States.

Top 100 most-cited articles on medical imaging applications of artificial intelligence: a bibliometric analysis (Preprint)

2020 ◽  
Author(s):  
Ying Liu ◽  
Ziyan Yu ◽  
Shuolan Jing ◽  
Honghu Jiang ◽  
Chunxia Wang
Keyword(s):  
Artificial Intelligence ◽  
Medical Imaging ◽  
Bibliometric Analysis ◽  
Clinical Medicine ◽  
Medical Physics ◽  
Medical Image Analysis ◽  
The United States ◽  
Independent Learning ◽  
Learning Ability ◽  
Core Technology

BACKGROUND Artificial intelligence (AI) has penetrated into almost every aspect of our lives and is rapidly changing our way of life. Recently, the new generation of AI taking machine learning and particularly deep convolutional neural network theories as the core technology, has stronger learning ability and independent learning evolution ability, combined with a large amount of learning data, breaks through the bottleneck limit of model accuracy, and makes the model efficient use. OBJECTIVE To identify the 100 most cited papers in artificial intelligence in medical imaging, we performed a comprehensive bibliometric analysis basing on the literature search on Web of Science Core Collection (WoSCC). METHODS The 100 top-cited articles published in “AI, Medical imaging” journals were identified using the Science Citation Index Database. The articles were further reviewed, and basic information was collected, including the number of citations, journals, authors, publication year, and field of study. RESULTS The highly cited articles in AI were cited between 72 and 1,554 times. The majority of them were published in three major journals: IEEE Transactions on Medical Imaging, Medical Image Analysis and Medical Physics. The publication year ranged from 2002 to 2019, with 66% published in a three-year period (2016 to 2018). Publications from the United States (56%) were the most heavily cited, followed by those from China (15%) and Netherlands (10%). Radboud University Nijmegen from Netherlands, Harvard Medical School in USA, and The Chinese University of Hong Kong in China produced the highest number of publications (n=6). Computer science (42%), clinical medicine (35%), and engineering (8%) were the most common fields of study. CONCLUSIONS Citation analysis in the field of artificial intelligence in medical imaging reveals interesting information about the topics and trends negotiated by researchers and elucidates which characteristics are required for a paper to attain a “classic” status. Clinical science articles published in highimpact specialized journals are most likely to be cited in the field of artificial intelligence in medical imaging.

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