Assessing the therapeutic climate of prisons

This study examined perceptions of the prison social climate in two Australian prisons from the perspective of both prison staff and prisoners. Ratings of social climate were compared between a specialist treatment prison that provides intensive rehabilitation programs to violent, sexual, and substance-using offenders and a mainstream prison that does not specialize in offender rehabilitation. The results suggested that staff and prisoners at the specialist treatment prison rated the social climate as more conducive to rehabilitation, although the differences were less pronounced for prisoners. These findings are discussed in relation to the development of specialist therapeutic prisons and how assessments of social climate might inform assessments of their success.

Factors governing colonisation by forensically important blow flies in Victoria

This study investigated factors controlling blow fly infestation of simulated remains (meat baits) in Victoria. Temperature, light and dark and level of decay are most influential in determining infestation by maggots. This knowledge will assist in more accurate forensic estimations of the minimum time since death of a corpse.

Effects of bait age, larval chemical cues and nutrient depletion on colonization by forensically important calliphorid and sarcophagid flies

Species colonization patterns on corpses and the frequency of carrion fly oviposition and larviposition are affected by decomposition stage and previous maggot colonization. This study investigated these effects on meat bait colonization by Victorian Diptera of forensic importance. Bait treatments were: 'aged' (aged for 4 days at 22 °C, allowing some decomposition); 'nutrient-depleted' [aged for 4 days at 22 °C with feeding Calliphora vicina (Robineau-Desvoidy) (Diptera: Calliphoridae) larvae]; 'extract' (fresh bait mixed with liquid formed by feeding C. vicina larvae), and 'fresh' (untreated control bait). Statistical analysis (α = 0.05) revealed that colonization frequency differed significantly among treatments (Welch's F 3,18.83 = 4.66, P < 0.05). Post hoc tests showed that fresh and extract baits were colonized extensively throughout the experiment with no significant difference, whereas the colonization of nutrient-depleted baits was significantly lower. This suggests that larval digestive enzymes, larval excreta and cuticular hydrocarbons have less effect on colonizing Diptera than the nutritional content of meat. The colonization of aged baits did not differ significantly from that of fresh, extract or nutrient-depleted baits. A further experiment testing 'very aged' (aged for 8 days at 28 °C), 'larvae-added' (fresh bait with C. vicina larvae added before placement) and 'fresh' (untreated control) baits revealed that very aged baits were colonized significantly less frequently than either fresh or larvae-added baits (Welch's F 2, 6.17 = 17.40, P < 0.05).

Image source detection : a case study on Facebook images taken by iPhones

Forged and tempered digital images become increasingly common on Facebook to aid computer frauds. The situation is worsened as many users can use a phone to take a photo and upload it to Facebook within two clicks, which highlights the need of image forensics for the cyber fraud cases. In this paper, we show the existence of the Facebook image filter which automatically changes the Facebook photos and consequently challenges the validity of forensic results. We aim to enable forensic investigators to relate a seized camera and a Facebook image. Specifically, we utilize intrinsic sensor pattern noise produced by a camera's lens to derive forensically useful information as Photo Response Non-Uniformity (PRNU) patterns. We propose to compare the PRNU patterns of a Facebook image and the flat field images produced by the candidate cameras. And we conclude this method to be effective by successfully identifying the correct iPhone from a list of four for a given Face book image.

Microphone identification using one-class classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a subcategory of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. Research results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

Identifying microphone from noisy recordings by using representative instance one class-classification approach

Rapid growth of technical developments has created huge challenges for microphone forensics - a sub-category of audio forensic science, because of the availability of numerous digital recording devices and massive amount of recording data. Demand for fast and efficient methods to assure integrity and authenticity of information is becoming more and more important in criminal investigation nowadays. Machine learning has emerged as an important technique to support audio analysis processes of microphone forensic practitioners. However, its application to real life situations using supervised learning is still facing great challenges due to expensiveness in collecting data and updating system. In this paper, we introduce a new machine learning approach which is called One-class Classification (OCC) to be applied to microphone forensics; we demonstrate its capability on a corpus of audio samples collected from several microphones. In addition, we propose a representative instance classification framework (RICF) that can effectively improve performance of OCC algorithms for recording signal with noise. Experiment results and analysis indicate that OCC has the potential to benefit microphone forensic practitioners in developing new tools and techniques for effective and efficient analysis.

The use of insects and associated arthropods in legal cases : a historical and practical perspective

 Forensic entomology has generally been recognised among law enforcement and the wider community as a science employed in the estimation of time since death. The utility of this science in contributing to the provision of time frames resulting in the focusing of valuable investigative resources has certainly been of the greatest importance. However, arthropods have been exploited extensively for their ability to provide information in a multitude of other situations, including cases of neglect, the food industry, and information relating to the cause and manner of death. This chapter will discuss the realm of information obtainable from insects and related groups in the forensic context, including and beyond the recognised time since death applications. Two areas of current research, molecular forensic entomology and entomotoxicology, will be discussed for their potential impact in the field.

A comparative analysis of decision trees vis-à-vis other computational data mining techniques in automotive insurance fraud detection

The development and application of computational data mining techniques in financial fraud detection and business failure prediction has become a popular cross-disciplinary research area in recent times involving financial economists, forensic accountants and computational modellers. Some of the computational techniques popularly used in the context of - financial fraud detection and business failure prediction can also be effectively applied in the detection of fraudulent insurance claims and therefore, can be of immense practical value to the insurance industry. We provide a comparative analysis of prediction performance of a battery of data mining techniques using real-life automotive insurance fraud data. While the data we have used in our paper is US-based, the computational techniques we have tested can be adapted and generally applied to detect similar insurance frauds in other countries as well where an organized automotive insurance industry exists.