Human trafficking is a multi-dimensional problem for which we have incomplete data, limited knowledge of the exploiters, and no understanding of the dynamics of the process. It is a problem that requires a larger, more complete database, understanding of key actors and their interactions in a dynamic environment. These methods exist in the areas of Data Mining, Machine Learning, Network Analysis, and Multi-agent systems. Using these methods, it is possible to create a model which is unique to detecting and preventing human trafficking. These methods can give applicable and successful solutions for different components of the problem of human trafficking. The goal is to build an intelligent system to enable collaboration and analysis, to identify and profile victims, traffickers, buyers, and exploiters, to predict human trafficking patterns, and to disrupt and destabilize human trafficking networks. In this paper, I will outline how some of these methods may be able to help analyze and model the dynamic phenomenon of human trafficking. The purpose is to see whether, using intelligent systems and appropriate collaboration and analysis tools, optimized intervention strategies can be created to profile victims and traffickers as well as impact, dissolve, and disrupt the human trafficking network in such a way that the network is unable to recover.

There are multiple and even interacting dimensions along which shape representation schemes may be compared and contrasted. In this paper, we focus on the following ques- tion. Are the building blocks in a compositional model lo- calized in space (e.g. as in part based representations) or are they holistic simplifications (e.g. as in spectral representa- tions)? Existing shape representation schemes prefer one or the other. We propose a new shape representation paradigm that encompasses both choices.

Artificial neural networks (ANNs, or simply NNs) are inspired by biological nervous systems and consist of simple processing units (artificial neurons) that are interconnected by weighted connections. Neural networks can be "trained" to solve problems that are difficult to solve by conventional computer algorithms. This paper presents the development and implementation of a generalized back-propagation multi-layer perceptron (MLP) neural network architecture described in very high speed hardware description language (VHDL). The development of hardware platforms has been complicated by the high hardware cost and quantity of the arithmetic operations required in an online MLP, i.e., one used to solve real-time problems. The challenge is thus to find an architecture that minimizes hardware costs while maximizing performance, accuracy, and parameterization. The paper describes herein a platform that offers a high degree of parameterization while maintaining performance comparable to other hardware based MLP implementations.

With the advent of RDFa and the at least partial support by major search engines, semantically structured data is more and more appearing on the Web. To enable high value use cases, links between entity descriptions need to be established. The linked data model suggests that links should be state explicitly by those who expose entity descriptions, but unlike on the normal web, incentives for doing so are unclear so that the model ultimately seems to fail in practice. In this position paper, we make the conjecture that explicit links are not needed for realizing the semantic web. We propose discuss how Record Linkage techniques are in general very well suited for the task but argue the need for a tool would allow data publishers to have an active role in producing entity descriptions that can then be linked automatically.

The CausalityWorkbench project provides an environment to test causal discovery algorithms. Via a web portal, we provide a number of resources, including a repository of datasets, models, and software packages, and a virtual laboratory allowing users to benchmark causal discovery algorithms by performing virtual experiments to study artificial causal systems. We regularly organize competitions. In this paper, we explore the opportunities offered by development applications.

Research effort, with its emphasis on evaluation and measurable progress, things began to change. Instead SHRDLU (WIN72) is perhaps the canonical example. of systems whose architecture and vocabulary were The rapid growth of efforts to found the next generation of based on linguistic theory (in this case acoustic phonetics), systems on general-purpose knowledge representation languages new approaches based on statistical modelling and Bayesian (I'm thinking of several varieties of semantic nets, probability emerged and quickly spread. "Every time I fire a from plain to partitioned, as well as KRL, KL-ONE and linguist my system's performance improves" (Fred Jellinek, their successors, ending (not yet, of course) with CYC (See head of speech recognition at IBM, c. 1980, latterly repudiated (BRA08) for all these) stumbled to a halt once their failure by Fred but widely attested). As advanced from resolution theorem provers through a number more and more problems are re-conceived as instances of of stages to the current proliferation of a range of Description the noisy channel model, the empiricist paradigm continually Logic'reasoners'; Whereas in the 1970s and 1980s there grew, so did the need to manage the impact of change and was real energy and optimism at the interface between computational conflict: enter'truth maintenance', subsequently renamed and theoretical linguistics, the overwhelming success'reason maintenance'. While still using some of But outflanking these'normal science' advances of AI, the terminology of linguistic theory, computational linguistics the paradigm shifters were coming up fast on the outside: practioners are increasingly detached from theory itself, over the last ten years machine learning has spread from which has suffered a, perhaps connected, loss of energy and small specialist niches such as speech recognition to become sense of progress.

An important issue in time series classification problems is to find representative prototypes. Especially for roughly segmented time series with spatial distortions, such as human gestures, it is complicated to find templates, which optimally represent signal classes. In this paper we present an approach to find optimal time series prototypes in subseries of class templates. Our optimization approach is based on separability measures for prototype candidates and utilizes (but is not limited to) DTW in order to tackle the problem of spatial and temporal distortions. The search for prototypes in the target space is performed by means of a brute force search as well as an evolution strategy. In our experiments with an artificial dataset we show that brute force search optimization is able to improve the time series classification result and that the application of an evolution strategy yields comparable target function scores while reducing computing time.

The present privacy legislation continue to be drafted on the basis of the Strasburg Convention of 1981. The mere fact that present privacy laws are based on principles drafted 29 years ago, when the web did not exist, shows that privacy legislation need to make a quantum leap to be in line with the realities of to-day’s real life operating environment. If the status quo is kept, the law and its application shall face serious (and sometimes insurmountable) obstacles to its implementation, making compliance costly for private business, at the same time jeopardizing effectiveness of privacy protection for individuals. A new set of rules should be drafted and established, addressing the changed environment of information and communication technology, in order to allow free flow of information at the same time assuring due protection of personal data.

The efficiency of current cargo screening processes at sea and air ports is largely unknown as few benchmarks exists against which they could be measured. Some manufacturers provide benchmarks for individual sensors but we found no benchmarks that take a holistic view of the overall screening procedures and no benchmarks that take operator variability into account. Just adding up resources and manpower used is not an effective way for assessing systems where human decision-making and operator compliance to rules play a vital role. Our aim is to develop a decision support tool (cargo-screening system simulator) that will map the right technology and manpower to the right commodity-threat combination in order to maximise detection rates. In this paper we present our ideas for developing such a system and highlight the research challenges we have identified. Then we introduce our first case study and report on the progress we have made so far.

Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemes to select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trials are carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance.