Crime facilitation purposes of social networking sites: A review and analysis of the ‘cyberbanging’ phenomenon

There have been growing claims in media circles and law-enforcement settings that street gangs and criminal groups are turning to Internet-based social networking sites for various reasons ranging from the showcasing of their images and exploits to the suspected recruitment of members. The present study investigates whether such a trend is, in fact, in place. The presence of street gangs on these Internet sites is referred to as cyberbanging. While there is some anecdotal evidence suggesting that gangs are turning to social networking sites, there is little available research on exactly how street gangs and criminal groups use the Internet. Our main argument is that gang culture is in many ways an individualized phenomenon and this feature ties in directly with recent assessments of the Internet as a setting that is governed by a process of networked individualism. This theoretical link between the individualized gang setting and the presence of gang members on social networking sites helps us understand why recruitment is improbable even in a context where people are openly diffusing their image and exploits to a growing number of Internet users. The empirical segment of this research adds to this general outlook. Based on a keyword search of over 50 street gang names, the three main social networking sites (Twitter, Facebook, and MySpace) were monitored for street gang presence. Results illustrate that gang presence on social networking sites is linked primarily to promoting a general gang or street culture through individual displays. In regard to the visitors to such sites, there is no evidence that they are being tricked or manipulated in any way. They are, however, showing their curiosity in regard to such groups and, for those who share their comments and opinions, signs of support are evident. Thus, whereas criminal gangs are not proactively using the Internet to convert anyone into being gang members, social networking sites are creating a new venue for people who share or are sensitive to the values underlying street gang lifestyle to come together. These sites essentially create a new convergence setting for gang members to interact with a wider number of people who would probably never have been exposed to their lifestyles and exploits through physical interactions. The study's conclusion extends these findings toward further research in this area, as well as outlining the more relevant implications for law-enforcement monitoring of this growing phenomenon.     

面向缺失多元时间序列的图神经网络异常检测算法

针对真实物联网环境中的缺失多元时间序列异常检测难题,提出一种融合缺失信息图嵌入的多元时间序列异常检测算法;基于预插值与异常检测任务融合的联合学习框架,设计一个基于时序高斯核函数的图神经网络（graph neural network, GNN）预插值模块,实现了预插值与异常检测任务的共同优化;提出一种时间序列数据缺失信息嵌入的图结构学习方法,采用图注意力机制融合缺失信息掩蔽矩阵和时空特征向量,有效建模多元时间序列缺失数据分布的潜在联系。在真实物联网传感器数据集上验证了提出算法的性能,实验结果表明,该方法在缺失多元时间序列异常检测任务上显著优于主流两阶段方法,预插值模块对比实验部分充分证明了基于高斯核函数的GNN预插值层的有效性。     

Optimal weapons allocation against layered defenses

We solve the problem of optimal allocation of weapons to targets in the presence of layered regional defenses. The general solution technique is an integer program transformable to a minimum-cost network flow. This model assumes the defense has perfect weapons. Results of a small sample scenario are included. Additionally, a representative attrition algorithm is described and the two models combined to form a hybrid algorithm. The hybrid algorithm allows for less-than-perfect weapons while maintaining optimality.     

Some recent findings in bayesian attribute single and double sampling

This article deals with several items, including theoretical and applied results. Specific topics include (1) a discrete, economically based, attributes acceptance sampling model and its adaptations, (2) relevant costs, (3) relevant prior distributions, (4) comparison of single- and double-sampling results, and (5) reasons for marginal implementation success following excellent implementation efforts. The basic model used is one developed by Guthrie and Johns; adaptations include provisions for fixed costs as well as modifications to permit double sampling. Optimization is exact, rather than approximate. Costs incorporated into the model are for sampling inspection, lot acceptance, and lot rejection. For each of these three categories a fixed cost is included as well as two variable costs, one for each item and the other for each defective item. Discrete prior distributions for the number of defectives in a lot are used exclusively. These include the mixed binomial and Polya distributions. Single- and double-sampling results are compared. Double sampling regularly performs at only slightly lower cost per lot than single sampling. Also, some cost and prior distribution sensitivity results are presented. Comments are provided regarding actual implementation experiences in industry. Practical deficiencies with the Bayesian approach are described, and a recommendation for future research is offered.     

A single-machine replacement model with learning

The replacement or upgrade of productive resources over time is an important decision for a manufacturing organization. The type of technology used in the productive resources determines how effectively the manufacturing operations can support the product and marketing strategy of the organization. Increasing operating costs (cost of maintenance, labor, and depreciation) over time force manufacturing organizations to periodically consider replacement or upgrade of their existing productive resources. We assume that there is a setup cost associated with the replacement of a machine, and that the setup cost is a nonincreasing function of the number of replacements made so far due to learning in setups. The operating cost of a newer machine is assumed to be lower than the operating cost of an older machine in any given period, except perhaps in the first period of operation of the new machine when the cost could be unusually high due to higher initial depreciation. A forward dynamic programming algorithm is developed which can be used to solve finite-horizon problems. We develop procedures to find decision and forecast horizons such that choices made during the decision horizon based only on information over the forecast horizon are also optimal for any longer horizon problem. Thus, we are able to obtain optimal results for what is effectively an infinite-horizon problem while only requiring data over a finite period of time. We present a numerical example to illustrate the decision/forecast horizon procedure, as well as a study of the effects of considering learning in making a series of machine replacement decisions. © 1993 John Wiley & Sons. Inc.     

THE NEXUS OF GLOBALIZATION AND NEXT-GENERATION NONPROLIFERATION

Although globalization has created opportunities for nuclear and biological proliferation dangers to take root and grow, it also has opened the door to new solutions. Original ideas and approaches are needed to develop a stronger, more flexible next-generation nonproliferation strategy that accounts for the increasingly important integration of economic, political, and technological issues. The foundation of this strategy should focus on tapping the power of market-based mechanisms, understanding how commercially driven decisions affect proliferation threats, establishing new partnerships, and forging cohesion among the current nonproliferation mechanisms. The implementation of such a strategy will require forceful leadership, a cultural shift from both policymakers and the range of stakeholders, and consensus building within the international community.     

A generalized recursive algorithm for a class of non-stationary regeneration (scheduling) problems

Recent efforts in the field of dynamic programming have explored the feasibility of solving certain classes of integer programming problems by recursive algorithms. Special recursive algorithms have been shown to be particularly effective for problems possessing a 0–1 attribute matrix displaying the “nesting property” studied by, Ignall and Veinott in inventory theory and by Glover in network flows. This paper extends the class of problem structures that has been shown amenable to recursive exploitation by providing an efficient dynamic programming approach for a general transportation scheduling problem. In particular, we provide alternative formulations lor the scheduling problem and show how the most general of these formulations can be readily solved vis a vis recursive techniques.