14,771 research outputs found
Short user-generated videos classification using accompanied audio categories
This paper investigates the classification of short user-generated videos (UGVs) using the accompanied audio data since short UGVs accounts for a great proportion of the Internet UGVs and many short UGVs are accompanied by singlecategory soundtracks. We define seven types of UGVs corresponding to seven audio categories respectively. We also investigate three modeling approaches for audio feature representation, namely, single Gaussian (1G), Gaussian mixture (GMM) and Bag-of-Audio-Word (BoAW) models. Then using Support Vector Machine (SVM) with three different distance measurements corresponding to three feature representations, classifiers are trained to categorize the UGVs. The accompanying evaluation results show that these approaches are effective for categorizing the short UGVs based on their audio track. Experimental results show that a GMM representation with approximated Bhattacharyya distance (ABD) measurement produces the best performance, and BoAW representation with chi-square kernel also reports comparable results
Measuring Accuracy of Automated Parsing and Categorization Tools and Processes in Digital Investigations
This work presents a method for the measurement of the accuracy of evidential
artifact extraction and categorization tasks in digital forensic
investigations. Instead of focusing on the measurement of accuracy and errors
in the functions of digital forensic tools, this work proposes the application
of information retrieval measurement techniques that allow the incorporation of
errors introduced by tools and analysis processes. This method uses a `gold
standard' that is the collection of evidential objects determined by a digital
investigator from suspect data with an unknown ground truth. This work proposes
that the accuracy of tools and investigation processes can be evaluated
compared to the derived gold standard using common precision and recall values.
Two example case studies are presented showing the measurement of the accuracy
of automated analysis tools as compared to an in-depth analysis by an expert.
It is shown that such measurement can allow investigators to determine changes
in accuracy of their processes over time, and determine if such a change is
caused by their tools or knowledge.Comment: 17 pages, 2 appendices, 1 figure, 5th International Conference on
Digital Forensics and Cyber Crime; Digital Forensics and Cyber Crime, pp.
147-169, 201
Semantic Support for Log Analysis of Safety-Critical Embedded Systems
Testing is a relevant activity for the development life-cycle of Safety
Critical Embedded systems. In particular, much effort is spent for analysis and
classification of test logs from SCADA subsystems, especially when failures
occur. The human expertise is needful to understand the reasons of failures,
for tracing back the errors, as well as to understand which requirements are
affected by errors and which ones will be affected by eventual changes in the
system design. Semantic techniques and full text search are used to support
human experts for the analysis and classification of test logs, in order to
speedup and improve the diagnosis phase. Moreover, retrieval of tests and
requirements, which can be related to the current failure, is supported in
order to allow the discovery of available alternatives and solutions for a
better and faster investigation of the problem.Comment: EDCC-2014, BIG4CIP-2014, Embedded systems, testing, semantic
discovery, ontology, big dat
Formal Analysis of Vulnerabilities of Web Applications Based on SQL Injection (Extended Version)
We present a formal approach that exploits attacks related to SQL Injection
(SQLi) searching for security flaws in a web application. We give a formal
representation of web applications and databases, and show that our
formalization effectively exploits SQLi attacks. We implemented our approach in
a prototype tool called SQLfast and we show its efficiency on real-world case
studies, including the discovery of an attack on Joomla! that no other tool can
find
Forensic Face Recognition: A Survey
Beside a few papers which focus on the forensic aspects of automatic face recognition, there is not much published about it in contrast to the literature on developing new techniques and methodologies for biometric face recognition. In this report, we review forensic facial identification which is the forensic expertsā way of manual facial comparison. Then we review famous works in the domain of forensic face recognition. Some of these papers describe general trends in forensics [1], guidelines for manual forensic facial comparison and training of face examiners who will be required to verify the outcome of automatic forensic face recognition system [2]. Some proposes theoretical framework for application of face recognition technology in forensics [3] and automatic forensic facial comparison [4, 5]. Bayesian framework is discussed in detail and it is elaborated how it can be adapted to forensic face recognition. Several issues related with court admissibility and reliability of system are also discussed. \ud
Until now, there is no operational system available which automatically compare image of a suspect with mugshot database and provide result usable in court. The fact that biometric face recognition can in most cases be used for forensic purpose is true but the issues related to integration of technology with legal system of court still remain to be solved. There is a great need for research which is multi-disciplinary in nature and which will integrate the face recognition technology with existing legal systems. In this report we present a review of the existing literature in this domain and discuss various aspects and requirements for forensic face recognition systems particularly focusing on Bayesian framework
Intelligent indexing of crime scene photographs
The Scene of Crime Information System's automatic image-indexing prototype goes beyond extracting keywords and syntactic relations from captions. The semantic information it gathers gives investigators an intuitive, accurate way to search a database of cases for specific photographic evidence. Intelligent, automatic indexing and retrieval of crime scene photographs is one of the main functions of SOCIS, our research prototype developed within the Scene of Crime Information System project. The prototype, now in its final development and evaluation phase, applies advanced natural language processing techniques to text-based image indexing and retrieval to tackle crime investigation needs effectively and efficiently
Automatically Discovering, Reporting and Reproducing Android Application Crashes
Mobile developers face unique challenges when detecting and reporting crashes
in apps due to their prevailing GUI event-driven nature and additional sources
of inputs (e.g., sensor readings). To support developers in these tasks, we
introduce a novel, automated approach called CRASHSCOPE. This tool explores a
given Android app using systematic input generation, according to several
strategies informed by static and dynamic analyses, with the intrinsic goal of
triggering crashes. When a crash is detected, CRASHSCOPE generates an augmented
crash report containing screenshots, detailed crash reproduction steps, the
captured exception stack trace, and a fully replayable script that
automatically reproduces the crash on a target device(s). We evaluated
CRASHSCOPE's effectiveness in discovering crashes as compared to five
state-of-the-art Android input generation tools on 61 applications. The results
demonstrate that CRASHSCOPE performs about as well as current tools for
detecting crashes and provides more detailed fault information. Additionally,
in a study analyzing eight real-world Android app crashes, we found that
CRASHSCOPE's reports are easily readable and allow for reliable reproduction of
crashes by presenting more explicit information than human written reports.Comment: 12 pages, in Proceedings of 9th IEEE International Conference on
Software Testing, Verification and Validation (ICST'16), Chicago, IL, April
10-15, 2016, pp. 33-4
- ā¦