Investigating HTTP response headers for the classification of devices on the Internet

2019 ◽  
Author(s):  
Arturs Lavrenovs ◽  
Gabor Visky
Keyword(s):  
The Internet

ASPECTS IN BUILDING A COMPANY WEB-SITE AND ITS ROLE FOR THE DEVELOPMENT OF BUSINESS

2018 ◽  
pp. 117-124
Author(s):  
Petar Halachev ◽  
Victoria Radeva ◽  
Albena Nikiforova ◽  
Miglena Veneva
Keyword(s):  
Life Cycle ◽  
Web Site ◽  
The Internet ◽  
A Company ◽  
The Web ◽  
Design Construction ◽  
Made In ◽  
Site Types

This report is dedicated to the role of the web site as an important tool for presenting business on the Internet. Classification of site types has been made in terms of their application in the business and the types of structures in their construction. The Models of the Life Cycle for designing business websites are analyzed and are outlined their strengths and weaknesses. The stages in the design, construction, commissioning, and maintenance of a business website are distinguished and the activities and requirements of each stage are specified.

Effectiveness of Supervised Classification Models for Hate Speech on Twitter

2020 ◽  
Author(s):  
Kunal Srivastava ◽  
Ryan Tabrizi ◽  
Ayaan Rahim ◽  
Lauryn Nakamitsu
Keyword(s):  
Supervised Classification ◽  
Hate Speech ◽  
The Internet ◽  
Classification Models ◽  
Personal Attack ◽  
Political Beliefs ◽  
The Many ◽  
Primary Medium ◽  
Offensive Speech

<div> <div> <div> <p>Abstract </p> <p>The ceaseless connectivity imposed by the internet has made many vulnerable to offensive comments, be it their physical appearance, political beliefs, or religion. Some define hate speech as any kind of personal attack on one’s identity or beliefs. Of the many sites that grant the ability to spread such offensive speech, Twitter has arguably become the primary medium for individuals and groups to spread these hurtful comments. Such comments typically fail to be detected by Twitter’s anti-hate system and can linger online for hours before finally being taken down. Through sentiment analysis, this algorithm is able to distinguish hate speech effectively through the classification of sentiment. </p> </div> </div> </div>

Classification of soils of Russia: Discussion on the internet site

2010 ◽  
Vol 43 (12) ◽  
pp. 1344-1350 ◽  
Author(s):  
M. I. Gerasimova ◽  
S. F. Khokhlov
Keyword(s):  
The Internet ◽  
Internet Site

scholarly journals SEMANTIC CLASSIFICATION OF METAPHORS IN UKRAINIAN AND ENGLISH INSCRIPTIONS ON CLOTHING

2021 ◽  
pp. 23-33
Author(s):  
Natalia STRIUK
Keyword(s):  
The Internet ◽  
Semantic Classification ◽  
Comparative Aspect ◽  
Figure Of Speech ◽  
Similar Images

The present research is an attempt to analyse metaphors in English and Ukrainian clothing inscriptions in a comparative aspect. The study focuses on providing a sufficient semantic classification of this versatile figure of speech in the discourse that has never been analysed in terms of metaphors. It deals with English and Ukrainian metaphorical inscriptions on clothing harvested on the Internet over a two-year period (2017-2019). The paper shows that metaphorisation is unevenly typical of English and Ukrainian linguocultural environments. The peculiarities of the source from which the units under analysis were collected allows us to identify seven main vehicle-driven categories of metaphors employed in clothing inscriptions: anthropic, zoomorphic, botanomorphic, creaturemorphic, artefactomorphic, ecomorphic and sensory. The research proves that both English and Ukrainian metaphorical clothing inscriptions have their peculiar sources; moreover, even if metaphors are built on the same or similar images, the focus is usually quite different. This study argues that metaphors on clothing inscriptions can serve as an applicable source to study social priorities, values and tendencies of two different European linguocultural environments. The outcome of the research can be used as an interesting material for sociolinguistics and linguocultural studies.

scholarly journals Machine learning: Enhancing the Perception of the Computer

2020 ◽  
Vol 9 (4) ◽  
pp. 758-761
Keyword(s):  
Machine Learning ◽  
Computer Training ◽  
Internet Traffic ◽  
The Internet ◽  
Popular Method ◽  
Exponential Increase ◽  
Computer Teaching ◽  
Traffic Detection ◽  
The Web

The advancement of technology and networking allows the use of the Web incredibly important. There is thus an exponential increase in data and information via the Internet. This flow thus is a beneficial field of study which can be defined accurately. Internet traffic detection is a very popular method of identifying information. Although so many methods have been successfully developed for classifying internet traffic, computer training technology among them is most popular. A short study of the classification of Internet traffic on various managed and non-regulated computer teaching systems was undertaken by many researchers. This paper will give various ideas to the other researcher’s and help them to learn a lot about machine learning

scholarly journals CRESS: Framework for Vulnerability Assessment of Attack Scenarios in Hardware Reverse Engineering

2021 ◽  
Author(s):  
Matthias Ludwig ◽  
Alexander Hepp ◽  
Michaela Brunner ◽  
Johanna Baehr
Keyword(s):  
Reverse Engineering ◽  
Autonomous Driving ◽  
The Internet ◽  
Changing Trends ◽  
Remediation Strategies ◽  
The Common ◽  
The One ◽  
Soft And Hardware ◽  
The Internet Of Things

Trust and security of microelectronic systems are a major driver for game-changing trends like autonomous driving or the internet of things. These trends are endangered by threats like soft- and hardware attacks or IP tampering -- wherein often hardware reverse engineering (RE) is involved for efficient attack planning. The constant publication of new RE-related scenarios and countermeasures renders a profound rating of these extremely difficult. Researchers and practitioners have no tools or framework which aid a common, consistent classification of these scenarios. In this work, this rating framework is introduced: the common reverse engineering scoring system (CRESS). The framework allows a general classification of published settings and renders them comparable. We introduce three metrics: exploitability, impact, and a timestamp. For these metrics, attributes are defined which allow a granular assessment of RE on the one hand, and attack requirements, consequences, and potential remediation strategies on the other. The system is demonstrated in detail via five case studies and common implications are discussed. We anticipate CRESS to evaluate possible vulnerabilities and to safeguard targets more proactively.

A Survey of Trust Use and Modeling in Real Online Systems

2013 ◽  
pp. 167-196
Author(s):  
Paolo Massa
Keyword(s):  
Social Relationships ◽  
Information Overload ◽  
The Internet ◽  
Online Systems ◽  
Trust Relationships ◽  
Different Types ◽  
Concept Of Information ◽  
The Web ◽  
Trust Information

This chapter discusses the concept of trust and how trust is used and modeled in online systems currently available on the Web or on the Internet. It starts by describing the concept of information overload and introducing trust as a possible and powerful way to deal with it. It then provides a classification of the systems that currently use trust and, for each category, presents the most representative examples. In these systems, trust is considered as the judgment expressed by one user about another user, often directly and explicitly, sometimes indirectly through an evaluation of the artifacts produced by that user or his/her activity on the system. We hence use the term “trust” to indicate different types of social relationships between two users, such as friendship, appreciation, and interest. These trust relationships are used by the systems in order to infer some measure of importance about the different users and influence their visibility on the system. We conclude with an overview of the open and interesting challenges for online systems that use and model trust information.

Evaluation of Decision-Making Support Systems

2011 ◽  
pp. 131-140
Author(s):  
Gloria E Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Support Systems ◽  
The Internet ◽  
Dynamic Data ◽  
Human Decision ◽  
Broad Classification ◽  
Specific Contribution ◽  
Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

scholarly journals Review of the Classification of Massive Chinese Texts Based on Spark

2018 ◽  
Vol 232 ◽  
pp. 01039
Author(s):  
Liu Yu
Keyword(s):  
Targeted Treatment ◽  
The Internet ◽  
Classification Result ◽  
News Reports ◽  
Internet Resources ◽  
Online Experience ◽  
Chinese Texts ◽  
Spark Framework

As the Internet develops rapidly, the number of texts is also growing rapidly. Whether it is the content of online emails exchanged by people, or the online novels and other literary contents, or news reports, personal blogs, Weibo or comments, they are constantly increasing the amount of text at all times. However, most of the data is not classified or processed, which causes a lot of spam, junk information, meaningless articles or advertisements. Their production not only consumes a lot of Internet resources, but also affects users' online experience and reduces the users' work and study efficiency. Therefore, it is vital accurately classify a large amount of text, judge its nature according to the classification result, and carry out targeted treatment. The classification of massive texts based on Spark framework is reviewed in this paper.

Evaluation of Decision-Making Support Systems

2011 ◽  
pp. 320-328 ◽  
Author(s):  
Gloria E. Phillips-Wren ◽  
Manuel Mora ◽  
Guisseppi Forgionne
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Support Systems ◽  
The Internet ◽  
Dynamic Data ◽  
Human Decision ◽  
Broad Classification ◽  
Specific Contribution ◽  
Decision Making Support

Decision support systems (DSSs) have been researched extensively over the years with the purpose of aiding the decision maker (DM) in an increasingly complex and rapidly changing environment (Sprague & Watson, 1996; Turban & Aronson, 1998). Newer intelligent systems, enabled by the advent of the Internet combined with artificial-intelligence (AI) techniques, have extended the reach of DSSs to assist with decisions in real time with multiple informaftion flows and dynamic data across geographical boundaries. All of these systems can be grouped under the broad classification of decision-making support systems (DMSS) and aim to improve human decision making. A DMSS in combination with the human DM can produce better decisions by, for example (Holsapple & Whinston, 1996), supplementing the DM’s abilities; aiding one or more of Simon’s (1997) phases of intelligence, design, and choice in decision making; facilitating problem solving; assisting with unstructured or semistructured problems (Keen & Scott Morton, 1978); providing expert guidance; and managing knowledge. Yet, the specific contribution of a DMSS toward improving decisions remains difficult to quantify.

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