CSI Las Vegas: Privacy, Policing, and Profiteering in Casino Structured Intelligence

This Article argues that the intricate, vast amounts of consumer information compiled through casino structured intelligence require greater protection and oversight in the contexts of both bankruptcy and law enforcement. Section II examines the various types of casino technology and information gathering that casinos perform. Section III considers the available protections of private information in terms of security breaches, law enforcement sharing, and sales in the context of a bankruptcy. Section IV discusses additional safeguards and ethical concerns that should be considered as casinos continue to increase their data mining efforts. Finally, Section V concludes that, minimally, consumers are entitled to more candid disclosures and a meaningful opportunity to protect their own privacy

A brave new world of Ambient Intelligence in the casinos of Macau: reality or fiction?

The article scrutinizes the brave new world of ambient intelligence in the casinos of the Macau, Special Administrative Region of People´s Republic of China, chiefly in regards to the (candent) issue of privacy of the casino patrons. Moreover, this scientific article provides an overview about the secondary use of big data of the casino patrons for law enforcement purposes

Generation of Evaluation Function for Lige-time Learning of An Intelligent Robot

This paper deals with a mobile robot with structured intelligence. The robot interacts with a dynamic environment. The evaluation criteria or functions are the strategy for the behavior acquisition. Generally, it is difficult for human operators to describe internal models of the robot because the organization of the robot is quite different from that of a human. In the optimization, the evaluation function is generally given by human operators beforehand. It is easy to give the evaluation functions if the environmental condition is easy and fixed. But the robot must interact with dynamic, uncertain and unknown environments or human operators. Therefore, the robot should generate the evaluation criteria by itself based on its embodiment. A human improves its behavior by using and changing its evaluation criteria as adaptive processes. The robot also has to acquire their evaluation criteria through life-time learning. Therefore, we apply genetic programming (GP) for generating evaluation functions. The result of computer simulation shows that GP can generate the evaluation function suitable to the facing environments, the given tasks, and the robot

Intelligence-led policing (ILP) as a policing model in the fight against crime

Are modern crime trends, the internationalization of crime, and its new manifestations forcing states to organize the police in a new way, and can Intelligence-led policing (ILP) respond to these challenges? In most countries, the police are organized in a traditional way, which means that the police react after a crime occurs. However, the traditional way of work especially expresses its limitation in the fight against transnational organized crime and terrorism. All this indicates the need to adjust police work to the existing circumstances and to find a new way of organizing work by which states will be able to fight successfully against crime. The ILP police model is based on proactive action. It focuses on the systematic collection and evaluation of information, through a process of quality analysis that is transformed into products of strategic and operational analysis, which will then serve as a basis for quality decision-making by police managers. Therefore, the real question is whether the ILP model can face these challenges and how to apply them in the daily work of the police. Experiences and results of the work of the police of Great Britain and the USA so far show that it can. Furthermore, citizens and the public increasingly demand that the police spend less money, and at the same time they expect better results. The ILP model is the one that enables the prioritization of tasks based on quality information, and thus the rational distribution of existing resources. Furthermore, the results of the Serbian police show that the implementation of ILP in the Balkan countries can be successful and have good results

Intelligence-led policing (ILP) as a policing model in the fight against crime

Are modern crime trends, the internationalization of crime, and its new manifestations forcing states to organize the police in a new way, and can Intelligence-led policing (ILP) respond to these challenges? In most countries, the police are organized in a traditional way, which means that the police react after a crime occurs. However, the traditional way of work especially expresses its limitation in the fight against transnational organized crime and terrorism. All this indicates the need to adjust police work to the existing circumstances and to find a new way of organizing work by which states will be able to fight successfully against crime. The ILP police model is based on proactive action. It focuses on the systematic collection and evaluation of information, through a process of quality analysis that is transformed into products of strategic and operational analysis, which will then serve as a basis for quality decision-making by police managers. Therefore, the real question is whether the ILP model can face these challenges and how to apply them in the daily work of the police. Experiences and results of the work of the police of Great Britain and the USA so far show that it can. Furthermore, citizens and the public increasingly demand that the police spend less money, and at the same time they expect better results. The ILP model is the one that enables the prioritization of tasks based on quality information, and thus the rational distribution of existing resources. Furthermore, the results of the Serbian police show that the implementation of ILP in the Balkan countries can be successful and have good results

A machine consciousness approach to autonomous mobile robotics

Proceeding of: 5th International Cognitive Robotics Workshop, 2006 (The AAAI '06 Workshop on Cognitive Robotics). Boston, Massachusetts, USA, July 16-17, 2006.In this paper we argue that machine consciousness can be successfully modelled to be the base of a control system for an autonomous mobile robot. Such a bio-inspired system provides the robot with cognitive benefits the same way that consciousness does for humans and other higher mammals. The key functions of consciousness are identified and partially applied to an original computational model, which is implemented in a software simulated mobile robot. We use a simulator to prove our assumptions and gain insight about the benefits that conscious and affective functions add to the behaviour of the robot. A particular exploration problem is analyzed and experiments results are evaluated. We conclude that this cognitive approach involving consciousness and emotion functions cannot be ignored in the design of mobile robots, as it provides efficiency and robustness in autonomous tasks. Specifically, the proposed model has revealed efficient control behaviour when dealing with unexpected situations.Publicad

A Comparison of Type-1 and Type-2 Fuzzy Logic Controllers in Robotics: A review

Most real world applications face high levels of uncertainties that can affect the operations of such applications. Hence, there is a need to develop different approaches that can handle the available uncertainties and reduce their effects on the given application. To date, Type-1 Fuzzy Logic Controllers (FLCs) have been applied with great success to many different real world applications. The traditional type-1 FLC which uses crisp type-1 fuzzy sets cannot handle high levels of uncertainties appropriately. Nevertheless it has been shown that a type-2 FLC using type-2 fuzzy sets can handle such uncertainties better and thus produce a better performance. As such, type-2 FLCs are considered to have the potential to overcome the limitations of type-1 FLCs and produce a new generation of fuzzy controllers with improved performance for many applications which require handling high levels of uncertainty. This paper will briefly introduce the interval type-2 FLC and its benefits. We will also present briefly some of the type-2 FLC real world applications

Intelligence in United Nations Peace Operations : A case study of the All Sources Information Fusion Unit in MINUSMA

More than a decade ago, the United Nations established a model for integrated missions – organising all assets under one leadership function – in order to facilitate comprehensive and coordinated mission efforts. This model creates a considerable span of control, with relatively few decision-making nodes. In turn, it requires information and decision support to reach decision-makers at the different levels of the organisation in a timely manner. One of the organisational steps taken to support decision-making was the establishment of the Joint Mission Analysis Centre (JMAC) in 2005. The All Source Information Fusion Unit (ASIFU) – an additional reinforcement to mission analysis capacity – was launched in Mali as a pilot in 2014. The purpose of the ASIFU was to provide intelligence on operational levels below the JMAC. The ASIFU was an intelligence entity that was part of the military component, under the command and control of the Force Commander (FC). This report demonstrates that the development of an intelligence community in integrated UN peace operations faces many of the challenges that other similar organisations have experienced. These include compression of organisational levels (the blurring of the lines between strategic, operational and tactical levels), challenges in disseminating and sharing products, an exaggerated and unrealistic belief in the use of technical sensors, and organisational changes that do not yield the desired results. While ASIFU is a recent invention, the introduction of intelligence structures in the UN is not new. Chesterman argued that efficient use of intelligence is a potential success factor from the strategic UN headquarters level down to the tactical level in peace operations (2006). Conversely, Dorn showed as early as 1999 the limitations of intelligence in UN peacekeeping. Ten years later, he considered that many of the same challenges remained, although intelligence, especially human intelligence (HUMINT), had been successful in supporting strong UN operations in Haiti to stabilise the gang-dominated slum areas (Dorn, 2009). Cammaert (2003) provided detailed recommendations on how intelligence in UN operations might be improved. For instance, he stressed the importance of competence standards for intelligence personnel, the utilisation of all assets and personnel for information gathering, and the reliability and consistency of the intelligence system and products. Although intelligence at all levels of the UN appears fraught with challenges, a near unanimous assessment from the existent literature is that an intelligence function is required in UN peace operations. The UN has increasingly acknowledged the need for intelligence to provide support for decision-making. As with other organisations involved in peace and stabilisation efforts, the UN needs knowledge to improve force protection, the planning of operations and the implementation of mission mandates. Two UN reports in particular have established the need for more systematic and efficient processing of information to support decision-making in UN peace operations. The so-called Brahimi report (UN, 2000) called for the increased collection and analysis of information on the relevant actors in a given mission area. Further, it stated that the UN does not have a system to process information about conflict areas – a system which covers information collection, analysis and the dissemination or distribution of products. Although the concept of intelligence in the UN has developed considerably since then, the so-called HIPPO report from 2015 (High-Level Independent Panel on Peace Operations) still called for improved support systems to enable more responsive and accountable peace operations – in other words, the UN still needs better intelligence (UN, 2015b). So where does the UN stand in terms of effective operational intelligence? To begin to answer this overarching question, this report provides a case study of the cutting edge of UN intelligence in contemporary UN peace operations1 – the ASIFU. First deployed in May 2014, the ASIFU was set up to support operational level decision-making in the United Nations Multidimensional Integrated Stabilisation Mission in Mali (MINUSMA). Comprising military personnel and military resources, the ASIFU was placed under the FC in MINUSMA’s organisational chart. The ASIFU is largely based on North Atlantic Treaty Organisation’s (NATO) intelligence doctrine, and is currently composed of personnel and resources from NATO member, or NATO-associated, states. The core aim is to provide an efficient intelligence organisation that enables MINUSMA to protect its forces and civilians under threat and to fulfil its mission mandate. First, the report explains the role and utility of ASIFU in MINUSMA. Second, it analyses the intelligence processes in the UN mission in South Sudan (UNMISS), which does not have an ASIFU. This helps provide an understanding of whether an ASIFU could also be useful there. Finally, the report evaluates how MINUSMA and UNMISS use intelligence to support the implementation of the prioritised mission mandate – the protection of civilians (POC) – for both missions. Specifically, this report tries to answer three questions: 1. How has the ASIFU affected the intelligence cycle of MINUSMA? 2. How does the intelligence cycle function in UNMISS, in the absence of an ASIFU? 3. How do intelligence units support POC mandates in MINUSMA and UNMISS? Chapter 2 defines the central concepts and describes intelligence resources in UN peace operations. Chapter 3 introduces the analytical framework – the four-phased intelligence cycle. Chapter 4 analyses ASIFU in MINUSMA, whilst Chapter 5 investigates UNMISS in order to compare MINUSMA with a UN mission that does not have an ASIFU. Both these chapters provide conclusions on intelligence resources and processes and recommendations for improvement. Chapter 6 briefly investigates how MINUSMA and UNMISS use intelligence resources to support their top priority task of POC. Chapter 7 makes recommendations on how to increase the utility of intelligence in UN peace operations. The report largely builds on information received through semi-structured interviews with UN practitioners – civilian and military – in Mali and South Sudan in June 2016. All interviewees agreed to provide information as long as they could remain anonymous. In addition, the report builds on UN policies, guidelines and handbooks, as well as other relevant literature. Both authors have previous experience with intelligence in NATO operations, and Lindboe was involved in the original construction, implementation, and deployment of the ASIFU in MINUSMA