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121 - 130 of 1,973 for bentley


CompositeMap: a novel framework for music similarity measure

With the continuing advances in data storage and communication technology, there has been an explosive growth of music information from different application domains. As an effective technique for organizing, browsing, and searching large data collections, music information retrieval is attracting more and more attention. How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems. In this paper, we introduce a novel framework based on a multimodal and adaptive similarity measure for various applications. Distinguished from previous approaches, our system can effectively combine music properties from different aspects into a compact signature via supervised learning. In addition, an incremental Locality Sensitive Hashing algorithm has been developed to support efficient retrieval processes with different kinds of queries. Experimental results based on two large music collections reveal various advantages of the proposed framework including effectiveness, efficiency, adaptiveness, and scalability.

https://dl.acm.org/ft_gateway.cfm?id=1572011&dwn=1

Hardware realization of BSB recall function using memristor crossbar arrays

The Brain-State-in-a-Box (BSB) model is an auto-associative neural network that has been widely used in optical character recognition and image processing. Traditionally, the BSB model was realized at software level and carried out on high-performance computing clusters. To improve computation efficiency and reduce resources requirement, we propose a hardware realization by utilizing memristor crossbar arrays. In this work, we explore the potential of a memristor crossbar array as an auto-associative memory. More specificly, the recall function of a multi-answer character recognition based on BSB model was realized. The robustness of the proposed BSB circuit was analyzed and evaluated based on massive Monte-Carlo simulations, considering input defects, process variations, and electrical fluctuations. The physical constrains when implementing a neural network with memristor crossbar array have also been discussed. Our results show that the BSB circuit has a high tolerance to random noise. Comparably, the correlations between memristor arrays introduces directional noise and hence dominates the quality of circuits.

https://dl.acm.org/ft_gateway.cfm?id=2228448&dwn=1

A probabilistic model for multimodal hash function learning

In recent years, both hashing-based similarity search and multimodal similarity search have aroused much research interest in the data mining and other communities. While hashing-based similarity search seeks to address the scalability issue, multimodal similarity search deals with applications in which data of multiple modalities are available. In this paper, our goal is to address both issues simultaneously. We propose a probabilistic model, called multimodal latent binary embedding (MLBE), to learn hash functions from multimodal data automatically. MLBE regards the binary latent factors as hash codes in a common Hamming space. Given data from multiple modalities, we devise an efficient algorithm for the learning of binary latent factors which corresponds to hash function learning. Experimental validation of MLBE has been conducted using both synthetic data and two realistic data sets. Experimental results show that MLBE compares favorably with two state-of-the-art models.

https://dl.acm.org/ft_gateway.cfm?id=2339678&dwn=1

Denial of service detection and analysis using idiotypic networks paradigm

In this paper we present a novel intrusion detection architecture based on Idiotypic Network Theory (INIDS), that aims at dealing with large scale network attacks featuring variable properties, like Denial of Service (DoS). The proposed architecture performs dynamic and adaptive clustering of the network traffic for taking fast and effective countermeasures against such high-volume attacks. INIDS is evaluated on the MIT'99 dataset and outperforms previous approaches for DoS detection applied to this set.

https://dl.acm.org/ft_gateway.cfm?id=1389107&dwn=1

Query-Directed Probing LSH for Cosine Similarity

Locality-sensitive hashing (LSH) considered as an efficient algorithm for large-scale similarity search has become increasingly popular. Recently, many of its variants have been applied widely in high-dimensional similarity search. To overcome the drawback of requirement for a large number of hash tables, researchers proposed the famous Multi-Probe LSH (MP-LSH). It has been used to improve the utilization of hash tables. There are two major probing sequences mentioned in MP-LSH, i.e., Step-Wise Probing (SWP) sequence and Query-Directed Probing (QDP) sequence. It is verified that QDP sequence is better than SWP sequence in number of probes and query time. However, the proposed QDP sequence is based on the E2LSH. It means that the method is only adopted for Euclidean distance. For cosine similarity, SWP sequence is still the only feasible method to perform Multi-Probe LSH.

This paper proposes an approach based on QDP sequence for cosine similarity search. Moreover, we give a set of complete theories and the corresponding proof for our method. Several experiments are performed on two types of open data sets. The experiments demonstrate our algorithm requires a small amount of probes and less time to achieve a high query quality than SWP sequence for cosine similarity.

https://dl.acm.org/ft_gateway.cfm?id=3033318&dwn=1

A multiobjective immune clustering ensemble technique applied to unsupervised SAR image segmentation

In the past few years, multiobjective clustering has been one of the most successful techniques in the field of computer vision and data clustering. This paper proposes a novel unsupervised approach for synthetic aperture radar (SAR) image segmentation, namely, multiobjective immune clustering ensemble technique (MICET). The new technique first divides the image into several regions, and a certain number of pixels are picked out from these regions to form the clustering dataset. Second, artificial immune system (AIS) and multiobjective optimization (MOO) are introduced to generate multiple clustering results, which are then combined together for the following ensemble process. Multiple runs of the multiobjective clustering method with different randomly selected image features are performed to ensure high quality components as well as necessary diversity for an efficient ensemble. Finally, each datum is assigned to one cluster according to the relationship with the clustering dataset. Experimental results show that interesting segmentation performances on SAR images can be achieved by the proposed technique despite its completely unsupervised nature.

https://dl.acm.org/ft_gateway.cfm?id=1816067&dwn=1

Cartesian genetic programming

Cartesian Genetic Programming is a form of genetic programming. It is increasing in popularity. It was developed by Julian Miller with Peter Thomson in 1997. In its classic form it uses a very simple integer based genetic representation of a program in the form of a directed graph. In a number of studies, it has been shown to be efficient in comparison with other GP techniques.

Since then, the classical form of CGP has been enhanced in various ways by including automatically defined functions.

Most recently, it has been developed by Julian Miller. Wolfgang Banzhaf and Simon Harding to include self-modification operators. This again has increased its efficiency.

The tutorial will cover the basic technique, advanced developments and applications to a variety of problem domains.

https://dl.acm.org/ft_gateway.cfm?id=1830924&dwn=1

Shedding Light on Mobile App Store Censorship

This paper studies the availability of apps and app stores across countries. Our research finds that users in specific countries do not have access to popular app stores due to local laws, financial reasons, or because countries are on a sanctions list that prohibit foreign businesses to operate within its jurisdiction. Furthermore, this paper presents a novel methodology for querying the public search engines and APIs of major app stores (Google Play Store, Apple App Store, Tencent MyApp Store) that is cross-verified by network measurements. This allows us to investigate which apps are available in which country. We primarily focused on the availability of VPN apps in Russia and China. Our results show that despite both countries having restrictive VPN laws, there are still many VPN apps available in Russia and only a handful in China. In addition, we have included findings of a global search for the availability of privacy-enhancing and other apps that are known to be censored. Finally, we observe that it is difficult to find out which apps have been removed or are unavailable on the examined app stores. As a consequence, we urge all app store providers to introduce app store transparency reports, which would include when apps were removed and for what reasons.

https://dl.acm.org/ft_gateway.cfm?id=3324965&dwn=1

Is Deductive Program Verification Mature Enough to be Taught to Software Engineers?

Software engineers working in industry seldom try to apply formal methods to solve problems. There are various reasons for this. Sometimes these reasons are understandable---the cost of using formal methods does not make economic sense in many contexts.

However, formal methods are also often greeted with scepticism. Formal methods are assumed to take too much time, require tools that are too academic, or to be too mathematical to be understood by practice-oriented software engineers.

We tested these assumptions by designing a small course around a framework for program verification, aimed at regular computer science students enrolled in a Master's programme. After four lectures and associated exercises, students were given a small verification task where they had to model and verify a real, non-trivial, C function in Why3.

A significant majority of students managed to prove a non-trivial functional specification of this C function in the time allotted, and many also pointed out inherent flaws of this function discovered during formalization. Participants reported no major difficulties or mental hurdles in learning Why3, and considered its approach to be appropriate for selected components of safety-critical software.

While formal verification tools such as Why3 still have lots of room for improvement, this experience shows that in a short amount of time, software engineers can be taught to use a program verification tool, and obtain usable results without being fully proficient in it. We further recommend that courses on formal methods should also let students explore these as techniques to be applied, instead of only focusing on the theory behind them, as we expect this to gradually lower the barrier to wider acceptance.

https://dl.acm.org/ft_gateway.cfm?id=3375265&dwn=1