scholarly journals Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions

2021 ◽  
Vol 1 (4) ◽  
pp. 580-596
Author(s):  
Cecelia Horan ◽  
Hossein Saiedian
Keyword(s):  
Open Source ◽  
Language Processing ◽  
Digital Forensics ◽  
New Technology ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Digital Forensic ◽  
Open Source Intelligence ◽  
Future Research Directions

As technology has become pivotal a part of life, it has also become a part of criminal life. Criminals use new technology developments to commit crimes, and investigators must adapt to these changes. Many people have, and will become, victims of cybercrime, making it even more important for investigators to understand current methods used in cyber investigations. The two general categories of cyber investigations are digital forensics and open-source intelligence. Cyber investigations are affecting more than just the investigators. They must determine what tools they need to use based on the information that the tools provide and how effectively the tools and methods work. Tools are any application or device used by investigators, while methods are the process or technique of using a tool. This survey compares the most common methods available to investigators to determine what kind of evidence the methods provide, and which of them are the most effective. To accomplish this, the survey establishes criteria for comparison and conducts an analysis of the tools in both mobile digital forensic and open-source intelligence investigations. We found that there is no single tool or method that can gather all the evidence that investigators require. Many of the tools must be combined to be most effective. However, there are some tools that are more useful than others. Out of all the methods used in mobile digital forensics, logical extraction and hex dumps are the most effective and least likely to cause damage to the data. Among those tools used in open-source intelligence, natural language processing has more applications and uses than any of the other options.

Gamified Self

2016 ◽  
pp. 304-327
Author(s):  
Rachelle DiGregorio ◽  
Harsha Gangadharbatla
Keyword(s):  
Technology Acceptance ◽  
Online Survey ◽  
Personal Information ◽  
New Technology ◽  
Ease Of Use ◽  
Future Research ◽  
Research Directions ◽  
Future Research Directions ◽  
Self Knowledge ◽  
Over Time

Gamified self has many dimensions, one of which is self-tracking. It is an activity in which a person collects and reflects on their personal information over time. Digital tools such as pedometers, GPS-enabled mobile applications, and number-crunching websites increasingly facilitate this practice. The collection of personal information is now a commonplace activity as a result of connected devices and the Internet. Tracking is integrated into so many digital services and devices; it is more or less unavoidable. Self-tracking engages with new technology to put the power of self-improvement and self-knowledge into people's own hands by bringing game dynamics to non-game contexts. The purpose of this chapter's research is to move towards a better understanding of how self-tracking can (and will) grow in the consumer market. An online survey was conducted and results indicate that perceptions of ease of use and enjoyment of tracking tools are less influential to technology acceptance than perceptions of usefulness. Implications and future research directions are presented.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2016 ◽  
pp. 212-244
Author(s):  
Boutheina Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2020 ◽  
pp. 700-725
Author(s):  
Boutheina A. Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

scholarly journals Advancements in Deep Learning Theory and Applications: Perspective in 2020 and beyond

2020 ◽  
Author(s):  
Md Nazmus Saadat ◽  
Muhammad Shuaib
Keyword(s):  
Deep Learning ◽  
Open Source ◽  
Language Processing ◽  
Future Research ◽  
Learning In Context ◽  
Postgraduate Students ◽  
History Of ◽  
Learning Platforms ◽  
Future Research Directions ◽  
Learning Machine

The aim of this chapter is to introduce newcomers to deep learning, deep learning platforms, algorithms, applications, and open-source datasets. This chapter will give you a broad overview of the term deep learning, in context to deep learning machine learning, and Artificial Intelligence (AI) is also introduced. In Introduction, there is a brief overview of the research achievements of deep learning. After Introduction, a brief history of deep learning has been also discussed. The history started from a famous scientist called Allen Turing (1951) to 2020. In the start of a chapter after Introduction, there are some commonly used terminologies, which are used in deep learning. The main focus is on the most recent applications, the most commonly used algorithms, modern platforms, and relevant open-source databases or datasets available online. While discussing the most recent applications and platforms of deep learning, their scope in future is also discussed. Future research directions are discussed in applications and platforms. The natural language processing and auto-pilot vehicles were considered the state-of-the-art application, and these applications still need a good portion of further research. Any reader from undergraduate and postgraduate students, data scientist, and researchers would be benefitted from this.

Recent advances in methods of lexical semantic relatedness – a survey

2012 ◽  
Vol 19 (4) ◽  
pp. 411-479 ◽  
Author(s):  
ZIQI ZHANG ◽  
ANNA LISA GENTILE ◽  
FABIO CIRAVEGNA
Keyword(s):  
Language Processing ◽  
Semantic Relatedness ◽  
Background Information ◽  
Future Research ◽  
Review Of The Literature ◽  
Research Directions ◽  
Lexical Semantic ◽  
Research Activities ◽  
Domain Boundaries ◽  
Future Research Directions

AbstractMeasuring lexical semantic relatedness is an important task in Natural Language Processing (NLP). It is often a prerequisite to many complex NLP tasks. Despite an extensive amount of work dedicated to this area of research, there is a lack of an up-to-date survey in the field. This paper aims to address this issue with a study that is focused on four perspectives: (i) a comparative analysis of background information resources that are essential for measuring lexical semantic relatedness; (ii) a review of the literature with a focus on recent methods that are not covered in previous surveys; (iii) discussion of the studies in the biomedical domain where novel methods have been introduced but inadequately communicated across the domain boundaries; and (iv) an evaluation of lexical semantic relatedness methods and a discussion of useful lessons for the development and application of such methods. In addition, we discuss a number of issues in this field and suggest future research directions. It is believed that this work will be a valuable reference to researchers of lexical semantic relatedness and substantially support the research activities in this field.

scholarly journals The Economics of Open Source Software: A Survey of the Early Literature

2002 ◽  
Vol 1 (1) ◽  
Author(s):  
Aaron Schiff
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Business Models ◽  
Recent Literature ◽  
Future Research ◽  
Research Directions ◽  
Early Literature ◽  
Future Research Directions ◽  
Closed Source

This paper reviews the recent literature on the economics of open source software. Two different sets of issues are addressed. The first looks at the incentives of programmers to participate in open source projects. The second considers the business models used by profit-making firms in the open source industry, and the effects on existing closed source firms. Some possible future research directions are also given.

scholarly journals Artificial Intelligence in the Battle against Coronavirus (COVID-19): A Survey and Future Research Directions

2020 ◽  
Author(s):  
Thanh Thi Nguyen
Keyword(s):  
Artificial Intelligence ◽  
Language Processing ◽  
Medical Image ◽  
Current Status ◽  
Future Research ◽  
Research Directions ◽  
Daily Lives ◽  
Related Data ◽  
Future Research Directions ◽  
The Internet Of Things

Artificial intelligence (AI) has been applied widely in our daily lives in a variety of ways with numerous successful stories. AI has also contributed to dealing with the coronavirus disease (COVID-19) pandemic, which has been happening around the globe. This paper presents a survey of AI methods being used in various applications in the fight against the COVID-19 outbreak and outlines the crucial roles of AI research in this unprecedented battle. We touch on a number of areas where AI plays as an essential component, from medical image processing, data analytics, text mining and natural language processing, the Internet of Things, to computational biology and medicine. A summary of COVID-19 related data sources that are available for research purposes is also presented. Research directions on exploring the potentials of AI and enhancing its capabilities and power in the battle are thoroughly discussed. We highlight 13 groups of problems related to the COVID-19 pandemic and point out promising AI methods and tools that can be used to solve those problems. It is envisaged that this study will provide AI researchers and the wider community an overview of the current status of AI applications and motivate researchers in harnessing AI potentials in the fight against COVID-19.

scholarly journals Compressing Large-Scale Transformer-Based Models: A Case Study on BERT

2021 ◽  
Vol 9 ◽  
pp. 1061-1080
Author(s):  
Prakhar Ganesh ◽  
Yao Chen ◽  
Xin Lou ◽  
Mohammad Ali Khan ◽  
Yin Yang ◽  
...  
Keyword(s):  
Language Processing ◽  
Large Scale ◽  
State Of The Art ◽  
Future Research ◽  
Research Directions ◽  
Research Attention ◽  
Model Compression ◽  
Future Research Directions ◽  
Or Applications

Abstract Pre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

scholarly journals On Language Processing Shaping Decision Making

2017 ◽  
Vol 26 (2) ◽  
pp. 146-151 ◽  
Author(s):  
Albert Costa ◽  
Marc–Lluís Vives ◽  
Joanna D. Corey
Keyword(s):  
Decision Making ◽  
Foreign Language ◽  
Language Processing ◽  
Native Language ◽  
Dual System ◽  
Future Research ◽  
Research Directions ◽  
Decision Making Processes ◽  
Future Research Directions ◽  
The Impact

Recent research has revealed that people’s preferences, choices, and judgments are affected by whether information is presented in a foreign or a native language. Here, we review this evidence, focusing on various decision-making domains and advancing a variety of potential explanations for this foreign-language effect on decision making. We interpret the findings in the context of dual-system theories of decision making, entertaining the possibility that foreign-language processing reduces the impact of intuition and/or increases the impact of deliberation on people’s choices. In closing, we suggest future research directions for progressing our understanding of how language and decision-making processes interact when guiding people’s decisions.

Diffusion of Electronic Commerce in Australia

2011 ◽  
pp. 102-114
Author(s):  
Mohammed A. Quaddus
Keyword(s):  
Diffusion Process ◽  
Electronic Commerce ◽  
New Technology ◽  
Future Research ◽  
Research Directions ◽  
Simple Diffusion ◽  
Future Research Directions ◽  
Simple Diffusion Model ◽  
High Level ◽  
Flexible Model

Diffusion is the process by which a new technology spreads in its usage among a population. This chapter analyses the diffusion process of one aspect of the consumer-to-business electronic commerce (EC) in Australia, namely Internet shopping. The chapter first reviews three popular logistics diffusion models from the literature and then applies them to the EC diffusion data. Results show that the most flexible model is not significant, while the simple diffusion model (Blackman’s) is. It was also found that the past diffusion process had been mostly influenced by the “internal” interactions between the adopters and the potential adopters of EC. Further analysis of the Blackman’s model revealed some high level policy guidelines to enhance the diffusion process further into the future. Limitations of the study and future research directions were also identified.

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