950 research outputs found
Steganographer Identification
Conventional steganalysis detects the presence of steganography within single
objects. In the real-world, we may face a complex scenario that one or some of
multiple users called actors are guilty of using steganography, which is
typically defined as the Steganographer Identification Problem (SIP). One might
use the conventional steganalysis algorithms to separate stego objects from
cover objects and then identify the guilty actors. However, the guilty actors
may be lost due to a number of false alarms. To deal with the SIP, most of the
state-of-the-arts use unsupervised learning based approaches. In their
solutions, each actor holds multiple digital objects, from which a set of
feature vectors can be extracted. The well-defined distances between these
feature sets are determined to measure the similarity between the corresponding
actors. By applying clustering or outlier detection, the most suspicious
actor(s) will be judged as the steganographer(s). Though the SIP needs further
study, the existing works have good ability to identify the steganographer(s)
when non-adaptive steganographic embedding was applied. In this chapter, we
will present foundational concepts and review advanced methodologies in SIP.
This chapter is self-contained and intended as a tutorial introducing the SIP
in the context of media steganography.Comment: A tutorial with 30 page
Web Social Media Privacy Preferences and Perception
The proliferation of social media websites has led to concerns over privacy breaches, as these sites have access to users' sensitive and personal data. This study sought to investigate users' perceptions and concerns for social media websites, with the aim of developing a system that meets their requirements. To achieve this, a questionnaire was designed for privacy permissions on eight popular social media websites, and 425 completed answers were analyzed. The results revealed that users' concerns were diverse and differed across different social media platforms. Gender, age, education level, and IT proficiency were found to be weakly correlated with privacy concerns. Women expressed greater concerns than men, particularly for Twitter and Snapchat, while older users expressed greater levels of concern for Snapchat and Instagram. As education levels increased, users tended to express greater levels of concern, especially on WhatsApp and Snapchat.
Furthermore, this study identified four hierarchical clusters of users based on their preferences and concerns regarding permission privacy for social media websites. The results revealed that the majority of participants (214 users) were highly concerned about privacy on social media, indicating that they were aware of the potential risks associated with sharing personal information online which represents the third cluster. The first and fourth clusters were the most unconcerned groups regarding permission privacy, consisting of a small number of users. The second cluster, comprising 124 participants, had an average score of 1.6, indicating that they were the second most concerned about privacy. Overall, the findings of this study could be useful for social media platforms in developing privacy policies and settings that align with users' concerns and preferences
Data Mining in Internet of Things Systems: A Literature Review
The Internet of Things (IoT) and cloud technologies have been the main focus of recent research, allowing for the accumulation of a vast amount of data generated from this diverse environment. These data include without any doubt priceless knowledge if could correctly discovered and correlated in an efficient manner. Data mining algorithms can be applied to the Internet of Things (IoT) to extract hidden information from the massive amounts of data that are generated by IoT and are thought to have high business value. In this paper, the most important data mining approaches covering classification, clustering, association analysis, time series analysis, and outlier analysis from the knowledge will be covered. Additionally, a survey of recent work in in this direction is included. Another significant challenges in the field are collecting, storing, and managing the large number of devices along with their associated features. In this paper, a deep look on the data mining for the IoT platforms will be given concentrating on real applications found in the literatur
Attraction-repulsion clustering: a way of promoting diversity linked to demographic parity in fair clustering
Producción CientÃficaWe consider the problem of diversity enhancing clustering, i.e, developing clustering
methods which produce clusters that favour diversity with respect to a set of pro-
tected attributes such as race, sex, age, etc. In the context of fair clustering, diversity
plays a major role when fairness is understood as demographic parity. To promote
diversity, we introduce perturbations to the distance in the unprotected attributes that
account for protected attributes in a way that resembles attraction-repulsion of charged
particles in Physics. These perturbations are defined through dissimilarities with a
tractable interpretation. Cluster analysis based on attraction-repulsion dissimilarities
penalizes homogeneity of the clusters with respect to the protected attributes and leads
to an improvement in diversity. An advantage of our approach, which falls into a pre-
processing set-up, is its compatibility with a wide variety of clustering methods and
whit non-Euclidean data. We illustrate the use of our procedures with both synthetic
and real data and provide discussion about the relation between diversity, fairness, and
cluster structure.Ministerio de EconomÃa y Competencia and FEDER, (grant MTM2017-86061-C2-1-P)Junta de Castilla y León, (grants VA005P17 and VA002G18)Gobierno PaÃs Vasco a través del programa BERC 2018-2021Ministerio de Ciencia, Innovación, y Universidades (acreditación BCAM Severo Ochoa SEV-2017-0718)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL
Utilizing Analytical Hierarchy Process for Pauper House Programme in Malaysia
In Malaysia, the selection and evaluation of candidates for
Pauper House Programme (PHP) are done manually. In
this paper, a technique based on Analytical Hierarchy
Technique (AHP) is designed and developed in order to
make an evaluation and selection of PHP application. The
aim is to ensure the selection process is more precise,
accurate and can avoid any biasness issue. This technique
is studied and designed based on the Pauper assessment
technique from one of district offices in Malaysia. A
hierarchical indexes are designed based on the criteria that
been used in the official form of PHP application. A
number of 23 samples of data which had been endorsed
by Exco of State in Malaysia are used to test this
technique. Furthermore the comparison of those two
methods are given in this paper. All the calculations of
this technique are done in a software namely Expert
Choice version 11.5. By comparing the manual and AHP
shows that there are three (3) samples that are not
qualified. The developed technique also satisfies in term
of ease of accuracy and preciseness but need a further
study due to some limitation as explained in the
recommendation of this paper
Study about customer segmentation and application in a real case
The hospitality industry generates a huge variety of data that grows by the day, becoming
incrinsingly difficult to analyse this data manually in order to build a good data model. A
thorough understanding of current customer profiles enables better resource allocation
and leads to better definition of product and market development strategies. Dividing
customers into similar groups to help develop more objective and focused marketing
messages for each of the segments. Thus, in the present dissertation methods of
classification and segmentation of existing data in the literature review are studied. Then,
a real case study is presented, using data from Property Management Systems of eight
Portuguese hotels, four city hotels and four resort hotels. This data set consists of fortyone
attributes but, after selection of the most predictive variables, only a subset of
attributes is used for data modeling. Next, the classification and segmentation methods
studied in the literature review are applied for extracting the relevant information. The
results are analyzed and discussed to understand their suitability to study the particular
characteristics of hotel reservations.O setor de hospitalidade gera uma enorme variedade de dados que crescem a cada dia,
tornando-se fisicamente impossÃvel analisar esses dados manualmente a fim de construir
um bom modelo de dados. Um profundo entendimento dos perfis dos atuais clientes
permite uma melhor alocação de recursos e leva a uma melhor definição das estratégias
de desenvolvimento de produtos e mercados. A divisão dos clientes em grupos
semelhantes para ajudar a desenvolver mensagens de marketing mais objetivas e focadas
para cada um dos seus segmentos.
Desse modo na presente dissertação são estudados métodos de classificação e
segmentação de dados existentes na revisão da literatura. De seguida, procede-se Ã
apresentação de um estudo de um caso real, usando dados pertencentes a Sistemas de
Gestão de Propriedade de oito hotéis portugueses, quatro hóteis de cidade e quatro hóteis
de resort, este conjunto de dados é composto por quarenta e um atributos, mas, após uma
selecção das variáveis com maior poder preditivo, apenas um subconjunto de atributos é
utilizado para a modelação dos dados. Em seguida, são aplicados os métodos de
classificação e segmentação estudados na revisão de literatura de modo a extrair
informação relevante. Os resultados são analisados e discutidos para entender sua
adequação ao estudo das caracterÃsticas particulares das reservas de hotéis
Security in Data Mining- A Comprehensive Survey
Data mining techniques, while allowing the individuals to extract hidden knowledge on one hand, introduce a number of privacy threats on the other hand. In this paper, we study some of these issues along with a detailed discussion on the applications of various data mining techniques for providing security. An efficient classification technique when used properly, would allow an user to differentiate between a phishing website and a normal website, to classify the users as normal users and criminals based on their activities on Social networks (Crime Profiling) and to prevent users from executing malicious codes by labelling them as malicious. The most important applications of Data mining is the detection of intrusions, where different Data mining techniques can be applied to effectively detect an intrusion and report in real time so that necessary actions are taken to thwart the attempts of the intruder. Privacy Preservation, Outlier Detection, Anomaly Detection and PhishingWebsite Classification are discussed in this paper
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