Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set. In this paper, we investigate the possibility of reducing the dimensionality of the space structure-based representation while maintaining the same representation ability. A lightweight representation scheme is proposed by taking a set of representative objects as the reference system (called the reference set) to position other objects in the Euclidean space. Moreover, a preclustering-based strategy is designed to select an appropriate reference set quickly. Finally, the representation scheme together with the k -means algorithm provides an efficient method to cluster the categorical data. The theoretical and the experimental analysis shows that the proposed method outperforms state-of-the-art methods in terms of both accuracy and efficiency.

In the era of information explosion, plenty of data has been generated through a variety of channels, such as social networks, crowdsourcing platforms and blogs. Conflicts and errors are constantly emerging. Truth discovery aims to find trustworthy information from conflicting data by considering source reliability. However, most traditional truth discovery approaches are designed only for structured data, and fail to meet the strong requirements to extract trustworthy information from unstructured raw text data. The major challenges of inferring reliable information on text data stem from the multifactorial property (i.e., an answer may contain multiple different key factors, which may be complex) and the diversity of word usages (i.e., different words may share similar semantic information, but the spelling of which are completely different). To solve these challenges, an ant colony optimization based text data truth discovery model is proposed. Firstly, keywords extracted from the whole answers of the specific question are grouped into a set. Then, we translate the truth discovery problem to a subset optimization problem, and the parallel ant colony optimization is utilized to find correct keywords for each question based on the hypothesis of truth discovery from the whole keywords. After that, the answers to each question can be ranked based on the similarities between keywords of user answers and identified correct keywords found by colony. The experiment results on real dataset show that even the semantic information of text data is complex, our proposed model can still find trustworthy information from complex answers compared with retrieval-based and state-of-the-art approaches.

Ant Colony Optimization (ACO) was developed to be a metaheuristic and widely used for combinatorial optimization problems. It is confirmed that the parameters setting is of vital importance for performance of ACO. This paper proposed a strategy to perform ACO Parameters Control based on Evolutionary Strength (ACOP_ES). First, we transform four parameters setting problem to decision making problem. Then, by learning the evolutionary strength curve, we set sum, peak, and kurtosis of evolutionary strength curve as the objective function, such that the continuous ant colony algorithm can be used to optimize this decision problem. Further, via characterizing an excellent evolutionary curve, a relatively optimal parameters setting can be found. Compared with existing methods, our proposed method focuses on the performance of the running process rather than the results only. To the best of our knowledge, we are among the first to introduce process performance evaluation of ACO to guide parameters control, and determine the relatively optimal parameters of ACO. At last, we apply our method to Multi-dimensional Knapsack Problem (MKP), the results look promising that parameters returned by ACOP_ES are better than the ones returned by other methods on datasets: WEISH*.DAT and WEING*.DAT.

After running BEEcube Inc for the past 7 years, I learned many lessons the hard way as an entrepreneur fresh out of engineer school. Behind the glory of being the #9 fastest growing private company in Silicon Valley in 2013, there were many untold stories about our FPGA technology based startup company. A startup company is where dreams start by smart people, and also where harsh reality squashes them. This is not one of those "unicorn" billion-dollar-valuation-in-18-month stories, but rather a bootstrap startup manage to find it's own pot of gold under the rainbow.

This paper describes the hardware implementation of a real-time, large-scale, multi-chip FPGA (Field Programmable Gate Array) based emulation engine with a capacity of 10 million ASIC (Application Specific Integrated Circuits) equivalent gates. Attainable system operation frequency can exceed 60 MHz, and the system throughput has been empirically verified to achieve 600 billion 16-bit additions per second. The emulator is custom designed to maximize the performance and resource utilization for a range of telecommunication and digital signal processing applications. With its high-speed interconnect architecture and large external I/O bandwidth, the emulator excels in prototyping real-time systems that have strict timing, logic capacity, and data rate requirements. Our development efforts are guided by such ongoing projects as ultra-wide band (UWB) and multi-channel-multi-antenna (MCMA) radio systems research.