55331569
'No representation without information flow': measuring efficacy and efficiency of representationan: an information theoretic approach
Representation is a key concept for semiotics and for information systems. Stamper's framework may be seen outlining what is required for the efficacy of signs in standing for (i.e., representing) something else in an organization, among many others. We explore how the efficacy and the efficiency of representation may be measured, which seems overlooked in available literature of information systems and organizational semiotics. And we approach this problem from the perspective of what we call the 'information carrying' relation between the representation and the represented. We model the represented as an information source and the representation an information carrier, and the 'representing' relationship between them as that of 'information carrying'. That is, information is carried and therefore flows. These then are further modeled mathematically as random variables and random events, and a special relationship between random events. This approach enables us to reveal a necessary condition for the efficacy and the efficiency of representation, and to measure it. To this end we extend Dretske's semantic theory of information. The conviction that we put forward here is 'No representation without information flow', based upon which the efficacy and efficiency of a representation system may be measurable.
63568794
Plenary lecture 1: an information flow based model of representation for developing informatics as an academic discipline
In informatics and information systems, a fundamental notion is representation. And yet, it would seem that in the literature of information systems this notion has been taken as self-evident and therefore treated largely intuitively, and in particular, how a possible representational relationship between two systems may be systematically identified and formalized has not been fully addressed. We believe that a mathematical model of representation helps develop informatics as a proper academic discipline. We observe that the notion of representation is formalizable. This is because it is information flow channels that underpin representational relationships between systems. We propose an information flow based model whereby to capture, formalize, analyze and reason about representation for distributed systems. We draw on semantic information theories, which not only lay solid philosophical foundations, but also contribute to forming a mathematical foundation for the model. Regularities is taken as a fundamental principle based upon which an information flow channel is constructed in which both tokens and types of a classification are taken into consideration. We introduce the notion of representation link, which connects classifications (contexts in FCA) through type level constraints and token level connections. Our model enables an analysis of information flow within a distributed system both qualitatively and quantitatively. The channel hierarchy and link hierarchy take into account background information, which enhances inference on representation links. The model allows agents to choose a channel that is the most appropriate for their purposes without violating the objectivity of information flow. 
 
This model is then applied to the problem of semantic interoperability. A semantic integration model is thus built. The process of semantic integration is examined by using the mathematical mechanism of the model. The validity of the model is verified by analyzing results of semantic integration from both content and quantity perspectives, and also by showing that the notion of channel and representation link hierarchy involved in our model helps reason over different but relevant integrations.
18718248
It is time for a computer application degree
Contemporary computer technology has spread computing activity to almost every field. The computing industry's need for application software developers who have not only proficient programming skills but also a good understanding of subjects in a specific application field has grown rapidly. Continuing to use existing degree programs in computer science to train expected application software developers would not only be a disservice to students but also hamper the further development of computer science programs themselves. Thus, a new degree program is called for in the sub-discipline of computer applications.
18971548
Coverage-preserving Data Gathering in Wireless Sensor Networks
The power is the most significant resource in wireless sensor nodes, so energy conservation is the most important factor in designing the protocol for sensor networks. In this paper, we study the problem of energy-efficient collection of the physical information from the target area. The coverage-preserving data gathering (CPDG) problem is to build up a tree, which minimizes the total energy consumption, and preserves the original coverage to ensure the result correctness. This problem can be formalized as 0-1 integer linear programming, and solved by an efficient heuristic algorithm. Furthermore, an improved algorithm is also proposed to balance the energy consumption among all the sensor nodes. The experimental results show that CPDG algorithm can conserve the energy about 30% of PEDAP protocol, which is regarded as one of the most efficient data gathering protocols as we know.
7500432
A Method to Organize A Small-World Based Publish/Subscribe Network In Large-Scale Environment
The paper proposes a Small-World Network Publish/ Subscribe (SWN-P/S) model in large-scale environment, which is a novel Event-based Middleware with Small-World Network (SWN) model. Through connecting brokers into a small world network, SWN-P/S achieves both a strong local clustering and a small average path length among vast brokers. These characteristics help to spread information through the system more rapidly. The paper also put forwards a cloud-partition method to organize brokers in terms of clustering coefficient in SWN. The method is useful to reduce the amount of routing information stored in each broker and to accelerate information dissemination. Comparisons of SWN-P/S with other construction ways for Publish/Subscribe Event-based Middleware show that SWN-P/S is more suitable for building large-scale distributed systems.
19552464
Attribute reduction and optimal decision rules acquisition for continuous valued information systems
For continuous valued information systems, the attribute values of objects for the same attribute represent not only their ordinal relationship but also their relative distances. Therefore, the classical rough set model is not suitable for deducing attribute reductions and optimal decision rules for continuous valued information systems. Though some discretization methods are proposed to transform the continuous valued information systems into discrete ones, but those methods are too categorical and may lead to loss of information in some cases. To solve such information loss problem, we propose a tolerance rough set model in this paper. With a given level, the proposed model can divide a universe into some maximal tolerance classes. Also two types of lower and upper approximations are defined accordingly. Then the reductions of the maximal tolerance class and optimal decision rules based on the proposed attribute descriptors are defined, and the approximate discernibility function for the maximal tolerance class is constructed and used to compute all the corresponding optimal decision rules via using Boolean reasoning techniques. Finally, the general reductions and consistent reductions for continuous valued information systems are discussed.
38555873
Recognizing Human Action from Videos Using Histograms of Visual Words
We propose a new method for human action recognition from video sequences using histograms of visual words. Video sequences are represented by a novel "bag-of-words" representation, where each frame corresponds to a "word". The major difference between our model and previous "bag-of-words" models for recognition problems in computer vision is that, a "word" in our representation corresponds to a whole frame. The advantage of this representation is that the large-scale feature of a frame is better captured, which turns out to be important for recognition actions. We demonstrate our approach on two publicly available datasets. Our results are comparable to other state-of-the-art approaches for action recognition.