scholarly journals Learning future terrorist targets through temporal meta-graphs

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gian Maria Campedelli ◽  
Mihovil Bartulovic ◽  
Kathleen M. Carley
Keyword(s):  
Time Series ◽  
Strategic Behavior ◽  
Feature Space ◽  
Future Research ◽  
Real World Data ◽  
Forecasting Performance ◽  
Humanitarian Crises ◽  
The World ◽  
Temporal Sequences ◽  
Over Time

AbstractIn the last 20 years, terrorism has led to hundreds of thousands of deaths and massive economic, political, and humanitarian crises in several regions of the world. Using real-world data on attacks occurred in Afghanistan and Iraq from 2001 to 2018, we propose the use of temporal meta-graphs and deep learning to forecast future terrorist targets. Focusing on three event dimensions, i.e., employed weapons, deployed tactics and chosen targets, meta-graphs map the connections among temporally close attacks, capturing their operational similarities and dependencies. From these temporal meta-graphs, we derive 2-day-based time series that measure the centrality of each feature within each dimension over time. Formulating the problem in the context of the strategic behavior of terrorist actors, these multivariate temporal sequences are then utilized to learn what target types are at the highest risk of being chosen. The paper makes two contributions. First, it demonstrates that engineering the feature space via temporal meta-graphs produces richer knowledge than shallow time-series that only rely on frequency of feature occurrences. Second, the performed experiments reveal that bi-directional LSTM networks achieve superior forecasting performance compared to other algorithms, calling for future research aiming at fully discovering the potential of artificial intelligence to counter terrorist violence.

scholarly journals Learning and recognition of tactile temporal sequences by mice and humans

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
Michael R Bale ◽  
Malamati Bitzidou ◽  
Anna Pitas ◽  
Leonie S Brebner ◽  
Lina Khazim ◽  
...  
Keyword(s):  
Noise Amplitude ◽  
Temporal Patterning ◽  
Auditory Stream ◽  
Mouse Sequence ◽  
Sequence Recognition ◽  
The World ◽  
Physical Features ◽  
Noise Sequence ◽  
Temporal Sequences ◽  
Over Time

The world around us is replete with stimuli that unfold over time. When we hear an auditory stream like music or speech or scan a texture with our fingertip, physical features in the stimulus are concatenated in a particular order. This temporal patterning is critical to interpreting the stimulus. To explore the capacity of mice and humans to learn tactile sequences, we developed a task in which subjects had to recognise a continuous modulated noise sequence delivered to whiskers or fingertips, defined by its temporal patterning over hundreds of milliseconds. GO and NO-GO sequences differed only in that the order of their constituent noise modulation segments was temporally scrambled. Both mice and humans efficiently learned tactile sequences. Mouse sequence recognition depended on detecting transitions in noise amplitude; animals could base their decision on the earliest information available. Humans appeared to use additional cues, including the duration of noise modulation segments.

scholarly journals Learning and recognition of tactile temporal sequences by mice and humans

2017 ◽  
Author(s):  
Michael Bale ◽  
Malamati Bitzidou ◽  
Anna Pitas ◽  
Leonie Brebner ◽  
Lina Khazim ◽  
...  
Keyword(s):  
Sequence Learning ◽  
Noise Amplitude ◽  
Temporal Patterning ◽  
Auditory Stream ◽  
The World ◽  
Physical Features ◽  
Noise Sequence ◽  
Temporal Sequences ◽  
Over Time

AbstractThe world around us is replete with stimuli that unfold over time. When we hear an auditory stream like music or speech or scan a texture with our fingertip, physical features in the stimulus are concatenated in a particular order, and this temporal patterning is critical to interpreting the stimulus. To explore the capacity of mice and humans to learn tactile sequences, we developed a task in which subjects had to recognise a continuous modulated noise sequence delivered to whiskers or fingertips, defined by its temporal patterning over hundreds of milliseconds. GO and NO-GO sequences differed only in that the order of their constituent noise modulation segments was temporally scrambled. Both mice and humans efficiently performed tactile sequence learning. Mouse performance relied mainly on detecting relative changes in noise amplitude over time, whereas humans appeared to have access to more cues, including the duration of noise modulation segments.

E-Government and Related Indices

2020 ◽  
pp. 1810-1824
Author(s):  
Kathleen M. Boyer-Wright ◽  
Jeffrey E. Kottemann
Keyword(s):  
Future Research ◽  
Government Services ◽  
Telecommunications Infrastructure ◽  
The World Bank ◽  
The World ◽  
Efficacy Index ◽  
Using Data ◽  
Changes Over Time ◽  
Over Time ◽  
Qualitative Analyses

The primary United Nations E-Government Index is a composite of three component indices: telecommunications infrastructure, human capital, and online e-government services, where the first two can be seen as enablers of the third. This study investigates the addition of a complementary component index for institutional efficacy, which is hypothesized to be another enabling factor. The institutional efficacy index is operationalized using existing measures gathered and made available by the World Bank. Statistical analysis shows that the institutional efficacy index is indeed a significant, additional predictor of online e-government services across nations. Following the presentation of basic results, qualitative analyses are undertaken to develop an assortment of generic national profiles. Preliminary analyses of changes over time are also presented using data from prior years, and directions for future research are outlined.

Socio-Cultural Impacts on Drug Trafficking Trends in Europe

2015 ◽  
Vol 23 (4) ◽  
pp. 383-406 ◽  
Author(s):  
Diana S. Dolliver
Keyword(s):  
Time Series ◽  
Drug Trafficking ◽  
Multivariate Time Series ◽  
Criminological Theory ◽  
Future Research ◽  
Cross Sectional ◽  
Crime Type ◽  
Anomie Theory ◽  
The World ◽  
Global Patterns

A myriad of national and international publications have detailed global patterns of drug trafficking for decades, with recent reports identifying Europe as a global consumption “hotspot” for the majority of popular drugs in the world. Yet, despite increasing levels of drug trafficking worldwide, scholars have not routinely examined this crime-type through the lens of a socio-cultural criminological theory. As such, this empirical study employed guidance from Institutional Anomie Theory. Data were collected from fourteen countries in Europe from 1995 to 2009 and analyzed using pooled cross-sectional multivariate time series. Trafficking patterns in cannabis, heroin, cocaine, and amphetamines were operationalized using officially reported drug seizure amounts. The findings from this study emphasize the need for differentiation between drug-types in future research, but also illustrate support for use of the theoretically informed variables.

scholarly journals Accounting for year effects and sampling error in temporal analyses of population and biodiversity change - Response to Seibold et al. 2019 “Arthropod decline in grasslands and forests is associated with landscape-level drivers”

2020 ◽  
Author(s):  
Gergana N. Daskalova ◽  
Isla Heather Myers-Smith ◽  
Albert B Phillimore
Keyword(s):  
Time Series ◽  
Species Richness ◽  
Sampling Error ◽  
Random Effect ◽  
Error Variance ◽  
Future Research ◽  
Public Confidence ◽  
Biodiversity Change ◽  
Change Response ◽  
Over Time

An accumulating number of studies are reporting severe biomass, abundance and/or species richness declines of insects (Hallmann et al., 2017; Lister & Garcia, 2018; Seibold et al., 2019; Sánchez-Bayo & Wyckhuys, 2019). Collectively these studies aim to quantify the net change in invertebrate populations and/or community composition over time and to establish whether such changes can be attributed to anthropogenic drivers (Macgregor, Williams, Bell, & Thomas, 2019; Saunders, Janes, & O’Hanlon, 2019; Thomas, Jones, & Hartley, 2019; Montgomery et al., 2020; van Klink et al., 2020). Seibold et al. 2019 analysed a dataset of arthropod biomass, abundance and species richness from forest and grassland plots in a region of Germany and report significant declines of up to 78% over the time period of 2008 to 2018 (Seibold et al., 2019). However, their analysis did not account for the confounding effects of temporal pseudoreplication of observations from the same years. We show that simply by including a year random effect in the statistical models and thereby accounting for the common conditions experienced by observations from proximal sites in the same years, four of the five reported declines become non-significant out of six tests overall. To place their estimated effect sizes and those of other recent studies of insect declines in a broader geographic context, we analysed invertebrate biomass, abundance and species richness over time from 640 time series from 1167 sites around the world. We found that the average trend across the terrestrial and freshwater realms was not significantly distinguishable from no net change. Shorter time series that are likely to be most affected by sampling error variance – such as those reported in Seibold et al. 2019 – yielded the most extreme estimates of decline or increase. We suggest that the uncritical media uptake of extreme negative trends from short time series may be serving to exaggerate the speed of "insect Armageddon" and could eventually undermine public confidence in biodiversity research. We advocate that future research include all available data and use model structures that account for uncertainties to build a more robust understanding of biodiversity change during the Anthropocene and its variation among regions and taxa (Kunin, 2019; Saunders et al., 2019; Thomas et al., 2019; Didham et al., 2020; Dornelas & Daskalova, 2020).

scholarly journals Creation of a participatory database of bioenergy projects

2019 ◽  
Vol 3 (2) ◽  
pp. 67-74
Author(s):  
Benoit Delcroix ◽  
Diana Clarisse Montaño Navarro ◽  
Simon Barnabé ◽  
Patrice Mangin
Keyword(s):  
International Energy Agency ◽  
Energy Systems ◽  
Future Research ◽  
Modus Operandi ◽  
Energy Agency ◽  
Greenhouse Gases Emissions ◽  
The World ◽  
Current Database ◽  
Over Time

Bioenergy is part of the solution to decarbonize energy systems and the economy, and to decrease greenhouse gases emissions drastically. The main goal of this work is to present a participatory database of bioenergy projects, initially based on information available on the International Energy Agency website. This new database aims at being updated over time through data crowdsourcing and being easily exportable in a spreadsheet for further processing. It provides numerous information about bioenergy projects around the world like the types of technology, inputs, outputs, financial information and project status. A detailed overview of the current database is presented, as well as the modus operandi suggested to improve over time this resource through voluntary contributions. The growing quality of this database will serve future research projects and analysis, while being a relevant tool to contribute to the success of the bioenergy sector.

Private Debt Markets

2019 ◽  
pp. 185-200
Author(s):  
Douglas Cumming ◽  
Grant Fleming ◽  
Zhangxin (Frank) Liu
Keyword(s):  
Future Research ◽  
Syndicated Loans ◽  
Private Debt ◽  
Private Companies ◽  
Equity Incentives ◽  
The World ◽  
Debt Markets ◽  
Private Placements ◽  
Direct Lending ◽  
Over Time

The chapter provides an overview of private debt and private debt markets. It explains the array of different specific types of private debt investments that are observed in practice, and the role equity incentives play in private debt deals. The chapter examines evidence from different countries around the world, including developed and developing markets. The chapter also describes the motives and contexts for using private debt, including but not limited to transactions involving private placements, syndicated loans, and direct lending. Private debt is not restricted to private companies but includes public ones as well. Further, the chapter characterizes private debt investors and their evolution over time. Additionally, it reviews evidence on the returns that private debt investors enjoy. The chapter concludes by identifying gaps in existing knowledge of private debt and offering suggestions for future research.

scholarly journals Time Series Complexities and Their Relationship to Forecasting Performance

Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 89 ◽  
Author(s):  
Mirna Ponce-Flores ◽  
Juan Frausto-Solís ◽  
Guillermo Santamaría-Bonfil ◽  
Joaquín Pérez-Ortega ◽  
Juan J. González-Barbosa
Keyword(s):  
Time Series ◽  
Feature Space ◽  
Self Organization ◽  
Permutation Entropy ◽  
Complexity Measures ◽  
Method Performance ◽  
Forecasting Error ◽  
Expected Performance ◽  
Forecasting Method ◽  
Forecasting Performance

Entropy is a key concept in the characterization of uncertainty for any given signal, and its extensions such as Spectral Entropy and Permutation Entropy. They have been used to measure the complexity of time series. However, these measures are subject to the discretization employed to study the states of the system, and identifying the relationship between complexity measures and the expected performance of the four selected forecasting methods that participate in the M4 Competition. This relationship allows the decision, in advance, of which algorithm is adequate. Therefore, in this paper, we found the relationships between entropy-based complexity framework and the forecasting error of four selected methods (Smyl, Theta, ARIMA, and ETS). Moreover, we present a framework extension based on the Emergence, Self-Organization, and Complexity paradigm. The experimentation with both synthetic and M4 Competition time series show that the feature space induced by complexities, visually constrains the forecasting method performance to specific regions; where the logarithm of its metric error is poorer, the Complexity based on the emergence and self-organization is maximal.

Examining Public Opinion About LGBTQ-Related Issues in the United States and Across Multiple Nations

2019 ◽  
Vol 45 (1) ◽  
pp. 401-423 ◽  
Author(s):  
Amy Adamczyk ◽  
Yen-Chiao Liao
Keyword(s):  
United States ◽  
Public Opinion ◽  
The United States ◽  
Future Research ◽  
Field Of Study ◽  
Public Opinion Research ◽  
The World ◽  
Over Time ◽  
Partial Relation

Over the last three decades, many countries across the world, including the United States, have experienced major increases in support for LGBTQ (lesbian, gay, bisexual, transgender, and queer) individuals and related issues. In partial relation to these changes, multiple studies have examined the factors shaping public opinion. In this review, we focus on four major areas of research on public opinion in this field of study. First, we assess the terms that scholars typically use when examining attitudes and highlight the areas of public opinion research that have received the most attention. Second, we focus on the data and measurement challenges related to examining attitudes in the United States and across many nations. Third, we consider how and why attitudes and related laws have changed over time and across nations. Finally, we discuss the major micro and macro empirical forces that influence and the theoretical explanations for why there are such differences in attitudes. We end by offering several suggestions for future research.

scholarly journals Quantifying Interagency Differences in Tropical Cyclone Best-Track Wind Speed Estimates

2010 ◽  
Vol 138 (4) ◽  
pp. 1459-1473 ◽  
Author(s):  
Kenneth R. Knapp ◽  
Michael C. Kruk
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Tropical Cyclone ◽  
Temporal Changes ◽  
Data Availability ◽  
Spatially Inhomogeneous ◽  
The World ◽  
Using Data ◽  
Over Time

Abstract Numerous agencies around the world perform postseason analysis of tropical cyclone position and intensity, a process described as “best tracking.” However, this process is temporally and spatially inhomogeneous because data availability, operational techniques, and knowledge have changed over time and differ among agencies. The net result is that positions and intensities often vary for any given storm for different agencies. In light of these differences, it is imperative to analyze and document the interagency differences in tropical cyclone intensities. To that end, maximum sustained winds from different agencies were compared using data from the International Best Track Archive for Climate Stewardship (IBTrACS) global tropical cyclone dataset. Comparisons were made for a recent 5-yr period to investigate the current differences, where linear systematic differences were evident. Time series of the comparisons also showed temporal changes in the systematic differences, which suggest changes in operational procedures. Initial attempts were made to normalize maximum sustained winds by correcting for known changes in operational procedures. The result was mixed, in that the adjustments removed some but not all of the systematic differences. This suggests that more details on operational procedures are needed and that a complete reanalysis of tropical cyclone intensities should be performed.

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