Which symbol grounding problem should we try to solve?

Floridi and Taddeo propose a condition of ‘zero semantic commitment’ for solutions to the grounding problem, and a solution to it. I argue briefly that their condition cannot be fulfilled, not even by their own solution. After a look at Luc Steel's very different competing suggestion, I suggest that we need to re-think what the problem is and what role the ‘goals’ in a system play in formulating the problem. On the basis of a proper (syntactic) understanding of computing, I come to the conclusion that the only sensible grounding problem is how we can explain and re-produce the behavioural ability and function of meaning in artificial computational agents.     

Scheduling evacuation operations

In this paper, we consider a large-scale evacuation problem after a major disaster. The evacuation is assumed to occur by means of a fleet of buses, thus leading to scheduling the evacuation operations by buses [(bus evacuation problem (BEP)]. We propose time-indexed formulations as well as heuristic algorithms such as greedy algorithms and a matheuristic. This matheuristic uses the former formulation to improve the best solution obtained by the greedy heuristics. In computational experiments, we analyze and evaluate the efficiency of the proposed solution algorithms.     

Assessment of crowdsourcing and gamification loss in user-assisted object segmentation

There has been a growing interest in applying human computation – particularly crowdsourcing techniques – to assist in the solution of multimedia, image processing, and computer vision problems which are still too difficult to solve using fully automatic algorithms, and yet relatively easy for humans. In this paper we focus on a specific problem – object segmentation within color images – and compare different solutions which combine color image segmentation algorithms with human efforts, either in the form of an explicit interactive segmentation task or through an implicit collection of valuable human traces with a game. We use Click’n’Cut, a friendly, web-based, interactive segmentation tool that allows segmentation tasks to be assigned to many users, and Ask’nSeek, a game with a purpose designed for object detection and segmentation. The two main contributions of this paper are: (i) We use the results of Click’n’Cut campaigns with different groups of users to examine and quantify the crowdsourcing loss incurred when an interactive segmentation task is assigned to paid crowd-workers, comparing their results to the ones obtained when computer vision experts are asked to perform the same tasks. (ii) Since interactive segmentation tasks are inherently tedious and prone to fatigue, we compare the quality of the results obtained with Click’n’Cut with the ones obtained using a (fun, interactive, and potentially less tedious) game designed for the same purpose. We call this contribution the assessment of the gamification loss, since it refers to how much quality of segmentation results may be lost when we switch to a game-based approach to the same task. We demonstrate that the crowdsourcing loss is significant when using all the data points from workers, but decreases substantially (and becomes comparable to the quality of expert users performing similar tasks) after performing a modest amount of data analysis and filtering out of users whose data are clearly not useful. We also show that – on the other hand – the gamification loss is significantly more severe: the quality of the results drops roughly by half when switching from a focused (yet tedious) task to a more fun and relaxed game environment.     

Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: A Case Study of an International Bank

The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six‐step procedure. The back‐propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.     

METIS-II: low resource machine translation

METIS-II was an EU-FET MT project running from October 2004 to September 2007, which aimed at translating free text input without resorting to parallel corpora. The idea was to use “basic” linguistic tools and representations and to link them with patterns and statistics from the monolingual target-language corpus. The METIS-II project has four partners, translating from their “home” languages Greek, Dutch, German, and Spanish into English. The paper outlines the basic ideas of the project, their implementation, the resources used, and the results obtained. It also gives examples of how METIS-II has continued beyond its lifetime and the original scope of the project. On the basis of the results and experiences obtained, we believe that the approach is promising and offers the potential for development in various directions.     

搜索引擎是如何最大化关键字广告收益的

通过什么样的规则来安排哪个广告给哪个关键字,才能最大化当天的收益呢?此问题可以抽象成"On-line带权二部图最大匹配问题"。     

Energy-Harvesting Microresonator Based on the Forces Generated by the Karman Street Around a Rectangular Prism

This paper explores the feasibility of using the unsteady forces generated by the Karman street around a microprism in the laminar flow regime as a means to generate mechanical work. In particular, the study has the objective of assessing whether this physical effect could be used for practical energy-harvesting purposes. The confined flow, either of a liquid or a gas, around a rectangular microprism, of which different aspect ratios have been studied, is considered at Reynolds 200 so as to make sure that the Karman street is developed and that the flow remains 2-D. The microprism is allowed to move freely in the direction perpendicular to the flow. The equation of motion of the prism includes a term that simulates its coupling with a vibration-driven micropower generator. Two different couplings have been considered: a velocity-damped resonant generator and a Coulomb-damped resonant generator. Different configurations have been studied, and comparisons have been carried out between them so as to draw a first set of design guidelines. Finally, it is shown that this type of energy-harvesting microdevice, which is basically 2-D, would yield a reasonable compromise between power output, manufacturing simplicity, and cost.     

Congestion-aware channel assignment for multi-channel wireless mesh networks

In this paper, we propose a distributed congestion-aware channel assignment (DCACA) algorithm for multi-channel wireless mesh networks (MC–WMNs). The frequency channels are assigned according to the congestion measures which indicate the congestion status at each link. Depending on the selected congestion measure (e.g., queueing delay, packet loss probability, and differential backlog), various design objectives can be achieved. Our proposed distributed algorithm is simple to implement as it only requires each node to perform a local search. Unlike most of the previous channel assignment schemes, our proposed algorithm assigns not only the non-overlapped (i.e., orthogonal) frequency channels, but also the partially-overlapped channels. In this regard, we introduce the channel overlapping and mutual interference matrices which model the frequency overlapping among different channels. Simulation results show that in the presence of elastic traffic (e.g., TCP Vegas or TCP Reno) sources, our proposed DCACA algorithm increases the aggregate throughput and also decreases the average packet round-trip compared with the previously proposed Load-Aware channel assignment algorithm. Furthermore, in a congested IEEE 802.11b network setting, compared with the use of three non-overlapped channels, the aggregate network throughput can further be increased by 25% and the average round-trip time can be reduced by more than one half when all the 11 partially-overlapped channels are used.     

Precise selective deposition of microparticles on electrodes of microelectronic chips

We examined the high precision deposition of toner and polymer microparticles with a typical size of approximately 10 microm on electrode arrays with electrodes of 100 microm and below using custom-made microelectronic chips. Selective desorption of redundant particles was employed to obtain a given particle pattern from preadsorbed particle layers. Microparticle desorption was regulated by dielectrophoretic attracting forces generated by individual pixel electrodes, tangential detaching forces of an air flow, and adhesion forces on the microchip surface. A theoretical consideration of the acting forces showed that without pixel voltage, the tangential force applied for particle detachment exceeded the particle adhesion force. When the pixel voltage was switched on, however, the sum of attracting forces was larger than the tangential detaching force, which was crucial for desorption efficiency. In our experiments, appropriately large dielectrophoretic forces were achieved by applying high voltages of up to 100 V on the pixel electrodes. In addition, electrode geometries on the chip's surface as well as particle size influenced the desorption quality. We further demonstrated the compatibility of this procedure to complementary metal oxide semiconductor chip technology, which should allow for an easy technical implementation with respect to high-resolution microparticle deposition.