Forensic Analysis of the ChatSecure Instant Messaging Application on Android Smartphones

We present the forensic analysis of the artifacts generated on Android smartphones by ChatSecure, a secure Instant Messaging application that provides strong encryption for transmitted and locally-stored data to ensure the privacy of its users. We show that ChatSecure stores local copies of both exchanged messages and files into two distinct, AES-256 encrypted databases, and we devise a technique able to decrypt them when the secret passphrase, chosen by the user as the initial step of the encryption process, is known. Furthermore, we show how this passphrase can be identified and extracted from the volatile memory of the device, where it persists for the entire execution of ChatSecure after having been entered by the user, thus allowing one to carry out decryption even if the passphrase is not revealed by the user. Finally, we discuss how to analyze and correlate the data stored in the databases used by ChatSecure to identify the IM accounts used by the user and his/her buddies to communicate, as well as to reconstruct the chronology and contents of the messages and files that have been exchanged among them. For our study we devise and use an experimental methodology, based on the use of emulated devices, that provides a very high degree of reproducibility of the results, and we validate the results it yields against those obtained from real smartphones

What is wrong with working from home?

Although the benefits of working from home are numerous, Dr Esther Canonico outlines the challenges both employers and employees can come across

Title IX: Girls Call Foul

Few pieces of legislation have had more of an effect on public education in recent years than Title IX of the 1972 Education Amendments. Female athlete participation has increased considerably as a result of this regulation. The purpose of this study, which was conducted in the state of Texas, was to find out how athletic directors—male and female head coaches—felt about their campuses’ compliance with Title IX components. The study focused on the results of a survey instrument that included 14 Likert-scale items and a number of demographic questions. The goal of this study was to (a) look at how athletic directors and head coaches of both male and female athletes perceive their school district’s level of Title IX compliance, and (b) look at specific patterns that determine whether Texas school districts are in compliance or noncompliance with specific Title IX provisions. The law focuses on equality in athletic opportunities under the following situations: (a) the choice of sports and performance levels (i.e., the successful accommodation of the desires and skills of members of both sexes); (b) the provision of equipment and supplies; (c) the arrangement of games and practice time; (4) the ability to obtain coaching and academic tutoring; (d) the provision of locker rooms, practice rooms, and competitive facilities; (e) advertising; and (f) the recruitment of coaching and academic tutoring. Descriptive and causal-comparative methods were used to analyze the data. The findings showed that regardless of their function, athletic directors, head girls coaches, and head boys coaches in Texas public high schools believed their schools complied with Title IX criteria to a high degree. A descriptive examination of the replies by respondent role revealed minor differences between male and female head coaches. Finally, the study revealed athletic directors should pay greater attention to coaching assignments, salary, and athletic facilities to comply with Title IX of the 1972 Education Amendments

Human-Machine Teamwork: An Exploration of Multi-Agent Systems, Team Cognition, and Collective Intelligence

One of the major ways through which humans overcome complex challenges is teamwork. When humans share knowledge and information, and cooperate and coordinate towards shared goals, they overcome their individual limitations and achieve better solutions to difficult problems. The rise of artificial intelligence provides a unique opportunity to study teamwork between humans and machines, and potentially discover insights about cognition and collaboration that can set the foundation for a world where humans work with, as opposed to against, artificial intelligence to solve problems that neither human or artificial intelligence can solve on its own. To better understand human-machine teamwork, it’s important to understand human-human teamwork (humans working together) and multi-agent systems (how artificial intelligence interacts as an agent that’s part of a group) to identify the characteristics that make humans and machines good teammates. This perspective lets us approach human-machine teamwork from the perspective of the human as well as the perspective of the machine. Thus, to reach a more accurate understanding of how humans and machines can work together, we examine human-machine teamwork through a series of studies. In this dissertation, we conducted 4 studies and developed 2 theoretical models: First, we focused on human-machine cooperation. We paired human participants with reinforcement learning agents to play two game theory scenarios where individual interests and collective interests are in conflict to easily detect cooperation. We show that different reinforcement models exhibit different levels of cooperation, and that humans are more likely to cooperate if they believe they are playing with another human as opposed to a machine. Second, we focused on human-machine coordination. We once again paired humans with machines to create a human-machine team to make them play a game theory scenario that emphasizes convergence towards a mutually beneficial outcome. We also analyzed survey responses from the participants to highlight how many of the principles of human-human teamwork can still occur in human-machine teams even though communication is not possible. Third, we reviewed the collective intelligence literature and the prediction markets literature to develop a model for a prediction market that enables humans and machines to work together to improve predictions. The model supports artificial intelligence operating as a peer in the prediction market as well as a complementary aggregator. Fourth, we reviewed the team cognition and collective intelligence literature to develop a model for teamwork that integrates team cognition, collective intelligence, and artificial intelligence. The model provides a new foundation to think about teamwork beyond the forecasting domain. Next, we used a simulation of emergency response management to test the different teamwork aspects of a variety of human-machine teams compared to human-human and machine-machine teams. Lastly, we ran another study that used a prediction market to examine the impact that having AI operate as a participant rather than an aggregator has on the predictive capacity of the prediction market. Our research will help identify which principles of human teamwork are applicable to human-machine teamwork, the role artificial intelligence can play in enhancing collective intelligence, and the effectiveness of human-machine teamwork compared to single artificial intelligence. In the process, we expect to produce a substantial amount of empirical results that can lay the groundwork for future research of human-machine teamwork

NETWORK ANALYSIS FOR PERFORMANCE CONTROL IN THE GOVERNANCE OF INTERCONNECTED LOCAL TRANSPORT COMPANIES

This paper discusses a network-analysis approach to the performance control of integrated built-environment systems based on efficiency, effectiveness, and adequacy. We apply this perspective to the governance of systems of local transport companies in built environments, which are frequently organized as networks. To this end, we propose a multi-dimensional grid of first- and second-order ties to locate network units and individuate the adequacy or appropriateness of network structures for performance control. In this field, issues connected to transport systems such as sustainability play a crucial role in defining structures and processes of network performance control. We empirically examine a pilot case of local public transport companies in the Forlì-Cesena area (Italy), testing network adequacy and giving evidence for the optimal localization of governance among units dedicated to providing transport services. Our results also support the hypothesis that, although structural centralization was ostensibly oriented towards increasing governance, the structure actually devolved into decentralized control at the periphery of the network, diminishing the effectiveness of initiatives

Analysis of the Influence of the Length Scales in a Boundary-Layer Model

AbstractWe consider the Janjic (NCEP Office Note 437:61, 2001) boundary-layer model, which is one of the most widely used in numerical weather prediction models. This boundary-layer model is based on a number of length scales that are, in turn, obtained from a master length multiplied by constants. We analyze the simulation results obtained using different sets of constants with respect to measurements using sonic anemometers, and interpret these results in terms of the turbulence processes in the atmosphere and of the role played by the different length scales. The simulations are run on a virtual machine on the Chameleon cloud for low-wind-speed, unstable, and stable conditions

The influence of section size on the mechanical properties of heat treated pressure vessel steels

The magnitude of differences in the cooling rates, with use of commercial spray quenching, prompted the Pressure Vessel Research Committee of the American Welding Society to sponsor a program at Lehigh University. In addition to the cooling rate program, previously reported mechanical property data have been complemented by additional data and are reported herein