Search

Image retrieval with relevant feedback on large and high-dimensional image databases is a challenging task. In this paper, we propose an image retrieval method, called BCFIR (Binary Codes for Fast Image Retrieval). BCFIR utilizes sparse discriminant analysis to select the most important original feature set, and solve the small class problem in the relevance feedback. Besides, to increase the retrieval performance on large-scale image databases, in addition to BCFIR mapping real-valued features to short binary codes, it also applies a bagging learning strategy to improve the ability general capabilities of autoencoders. In addition, our proposed method also takes advantage of both labeled and unlabeled samples to improve the retrieval precision. The experimental results on three databases demonstrate that the proposed method obtains competitive precision compared with other state-of-the-art image retrieval methods. [ABSTRACT FROM AUTHOR]

Time series similarity search is an essential operation in time series data mining and has received much higher interest along with the growing popularity of time series data. Although many algorithms to solve this problem have been investigated, there is a challenging demand for supporting similarity search in a fast and accurate way. In this paper, we present a novel approach, TS2BC, to perform time series similarity search efficiently and effectively. TS2BC uses binary code to represent time series and measures the similarity under the Hamming Distance. Our method is able to represent original data compactly and can handle shifted time series and work with time series of different lengths. Moreover, it can be performed with reasonably low complexity due to the efficiency of calculating the Hamming Distance. We extensively compare TS2BC with state-of-the-art algorithms in classification framework using 61 online datasets. Experimental results show that TS2BC achieves better or comparative performance than other the state-of-the-art in accuracy and is much faster than most existing algorithms. Furthermore, we propose an approximate version of TS2BC to speed up the query procedure and test its efficiency by experiment. [ABSTRACT FROM AUTHOR]

Due to the evolution in the digital domain limitless multimedia is generated daily. It creates a necessity of potential and appealing image resuscitation system. In this paper, a shape and texture-based image retrieval system is proposed that estimates the resemblances of each query image with the images stored in the repository in the form of shape and textural facets and retrieves the images within an expected range of resemblance. The proposed approach employs a statistical approach for image retrieval. The proposed approach takes into account discriminative features of the input image for generating the shape and texture descriptors that produce outstanding results for image databases of restricted variety, which merely includes homogeneous patterns, this approach yielded satisfactory results. For texture images it uses the spatial gray level dependency matrix (SGLDM) and proposes an algorithm to compute the the inverse difference moment (IDM) as the optimal image representative feature. It further employs K-Nearest Neighbour (KNN) classifier for the classification and retrieval tasks. The proposed system outperforms the various other ultra-modern content-based image retrieval (CBIR) systems in many respects. [ABSTRACT FROM AUTHOR]

It is known that each word of length n contains at most n + 1 distinct palindromes. A finite rich word is a word with maximal number of palindromic factors. The definition of palindromic richness can be naturally extended to infinite words. Sturmian words and Rote complementary symmetric sequences form two classes of binary rich words, while episturmian words and words coding symmetric d-interval exchange transformations give us other examples on larger alphabets. In this paper we look for morphisms of the free monoid, which allow us to construct new rich words from already known rich words. We focus on morphisms in Class Pret. This class contains morphisms injective on the alphabet and satisfying a particular palindromicity property: for every morphism ϕ in the class there exists a palindrome w such that ϕ(a)w is a first complete return word to w for each letter a. We characterize Pret morphisms which preserve richness over a binary alphabet. We also study marked Pret morphisms acting on alphabets with more letters. In particular we show that every Arnoux-Rauzy morphism is conjugated to a morphism in Class Pret and that it preserves richness. [ABSTRACT FROM AUTHOR]

DNA strand replacement technology has the advantages of simple operation which makes it becomes a common method of DNA computing. A four bit binary number Complementer based on two-domain DNA strand displacement is proposed in this paper. It implements the function of converting binary code into complement code. Simulation experiment based on Visual DSD software is carried out. The simulation results show the correctness and feasibility of the logic model of the Complementer, and it makes useful exploration for further expanding the application of molecular logic circuit. [ABSTRACT FROM AUTHOR]

Detecting the quality of drinking water is very important in researching the current status and predicting trends of variation, as well as fundamentally solving the water pollution problem. Recently, the application and development of biosensor provide a powerful analytical tool for detecting pollutants of drinking water, but high selectivity, specificity, short response time, and low cost are particularly desired in the detection process. In this paper, RNA molecules in combination with nano-gold and magnetic nano-particles were proposed as design strategies of the biosensor. The proposed designs have distinct advantages over conventional biosensor in the following aspects: the high affinity capacity, sensitivity, and practicability. Our work provided an alternative method to monitor the environment of drinking water, and may find applications in various monitoring fields. [ABSTRACT FROM AUTHOR]

Soft set, as a parametrized family of subsets of a crisp universal set, has more ability to handle uncertain information. Pawlak introduced the concept of rough set to deal with uncertainty. He used equivalence relation to approximate a set. Many authors generalized the concept and used binary relations to approximate a set. In this paper, we used soft binary relations to approximate a set. We approximate a set by using the aftersets and foresets. In this way we get two sets of soft sets, called the lower approximation and upper approximation with respect to the aftersets and foresets. We applied these concepts on semigroups and approximations of subsemigroups, left (right) ideals, interior ideals and bi-ideals of semigroups are studied. Moreover, some examples are considered to illustrate the paper. In the last we studied the homomorphic images of the lower and upper approximations. [ABSTRACT FROM AUTHOR]

The huge amount of digital image data in e-commerce transactions brings serious problems to the rapid retrieval and storage of images. Image hashing technology can convert image data of arbitrary resolution into a binary code sequence of tens or hundreds of bits through a hash function. In view of this, based on the image content characteristics, this study improved the traditional hash function and proposed a hash method based on bilateral random projection. At the same time, the projection vectors are acquired in the low-rank sparse decomposition process of the image data matrix, and the projection vectors are group orthogonalized. In addition, this study designed contrast test to carry out research and analysis on the effectiveness of the algorithm. The results show that the proposed algorithm works well and can be applied to practice and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

The task of Extractive Multi-Document Text Summarization (EMDTS) aims at building a short summary with essential information from a collection of documents. In this paper, we propose an EMDTS method using a Genetic Algorithm (GA). The fitness function considering two unsupervised text features: sentence position and coverage. We propose the binary coding representation, selection, crossover, and mutation operators. We test the proposed method on the DUC01 and DUC02 data set, four different tasks (summary lengths 200 and 400 words), for each of the collections of documents (in total, 876 documents) are tested. Besides, we analyze the most frequently used methodologies to summarization. Moreover, different heuristics such as topline, baseline, baseline-random, and lead baseline are calculated. In the results, the proposed method achieves to improve the state-of-art results. [ABSTRACT FROM AUTHOR]

Due to the rapid increase in the amount of data generated in many fields of science and engineering, information retrieval methods tailored to large-scale datasets have become increasingly important in the last years. Semantic hashing is an emerging technique for this purpose that works on the idea of representing complex data objects, like images and text, using similarity-preserving binary codes that are then used for indexing and search. In this paper, we investigate a hashing algorithm that uses a deep variational auto-encoder to learn and predict the codes. Unlike previous approaches of this type, that learn a continuous (Gaussian) representation and then project the embedding to obtain hash codes, our method employs Bernoulli latent variables in both the training and the prediction stage. Constraining the model to use a binary encoding allow us to obtain a more interpretable representation for hashing: each factor in the generative model represents a bit that should help to reconstruct and thus identify the input pattern. Interestingly, we found that the binary constraint does not lead to a loss but an increase of search accuracy. We argue that continuous formulations learn a representation that can significantly differ from the code used for search. Minding this gap in the design of the auto-encoder can translate into more accurate retrieval results. Extensive experiments on seven datasets involving image data and text data illustrate these findings and demonstrate the advantages of our approach. [ABSTRACT FROM AUTHOR]

In this paper, we present some new connections between BCK-algebras and binary block codes. [ABSTRACT FROM AUTHOR]

Uncertainty measures are an important tool for analyzing data. There is the uncertainty of a rough set caused by its boundary region in rough set models. Thus the uncertainty measurement issue is also an important topic for rough set theory. Shannon entropy has been introduced into rough set theory. However, there are relatively few studies on the uncertainty measure in generalized rough set models. We know that the boundary region of a rough set is closely related to the upper and lower approximations in rough set models. In this paper, from the viewpoint of the upper and lower approximations, we propose new uncertainty measures, the upper rough entropy and the lower rough entropy, in generalized rough set models. Then we focus on the investigations of the upper rough entropy, and give the concepts of the upper joint entropy, the upper conditional entropy and the mutual information with respect to a general binary relation. Some important properties of these measures are obtained. The connections among these measures are given. Furthermore, comparing with the existing uncertainty measures, the upper rough entropy has high distinguishing degree. Theoretical analysis and experimental results show that the proposed entropy is better effective than some existing measures. [ABSTRACT FROM AUTHOR]

We present a binary analysis framework based on symbolic execution with the distinguishing capability to execute stepwise forward and also backward through the execution tree. It was developed internally at Bitdefender and code-named RIVER. The framework provides components such as a taint engine, a dynamic symbolic execution engine, and integration with Z3 for constraint solving. In this paper we will provide details on the framework and give an example of analysis on binary code. [ABSTRACT FROM AUTHOR]

This paper deals with automatic face recognition in the context of a real application for person identification developed for the Czech News Agency (ČTK) .We focus on popular Local Binary Patterns (LPBs) that are frequently used in this field with high recognition accuracy. One drawback of current LBP based methods is that the positions and number of the fiducial points are fixed. These points thus do not reflect the properties of a particular image whereas we believe it is beneficial to identify the most representative ones. The main contribution consists in proposing and comparing several LBP-based approaches that detect such points fully automatically. We use a set of Gabor filters for this task. Local extrema in the filter responses are detected and then used as the feature points. The number of points is further reduced by a clustering algorithm. Our approaches also differ from the other ones in the matching procedure. The proposed methods are evaluated on three standard corpora: ORL, FERET, AR face database and our Č TK dataset containing uncontrolled face images. Recognition results clearly show high quality of the proposed approaches that outperform significantly the baseline LBP approach on all corpora. The benefits of our methods are particularly evident in the case of real non-controlled images (ČTK corpus) where the accuracy is increased by more than 20% in absolute value. [ABSTRACT FROM AUTHOR]

The chromatic transient visual evoked potential (CTVEP)-based brain-computer interface (BCI) can provide safer and more comfortable stimuli than the traditional VEP-based BCIs due to its low frequency change and no luminance variation in the visual stimulation. However, it still generates relatively few codes that correspond to input commands to control the outside devices, which limits its application in the practical BCIs to some extent. Aiming to obtain more codes, we firstly proposes a new time coding technique to CTVEP-based BCI by utilizing a combination of two 4-bit binary codes to construct four 8-bit binary codes to increase the control commands to extend its application in practice. In the experiment, two time-encoded isoluminant chromatic stimuli are combined to serve as different commands for BCI control, and the results show that the high performance based on the new time coding approach with the average accuracy up to 90.28% and average information transfer rate up to 27.78 bits/min for BCI can be achieved. It turns out that the BCI system based on the proposed method is feasible, stable and efficient, which makes the method very suitable for the practical application of BCIs, such as military, entertainment and medical enterprise. [ABSTRACT FROM AUTHOR]

Binary authorship attribution refers to the process of identifying the author of a given anonymous binary file based on stylistic characteristics. It aims to automate the laborious and error-prone reverse engineering task of discovering information related to the author(s) of a binary code. Existing works typically employ machine learning methods to extract features that are unique for each author and subsequently match them against a given binary to identify the author. However, most existing works share a common critical limitation, i.e., they cannot distinguish between features representing program functionality and those representing authorship (e.g., authors' coding habits). Such distinction is crucial for effective authorship attribution because what is unique in a particular binary may be attributed to either author, compiler, or function. In this study, we present BinAuthor a system capable of decoupling program functionality from authors' coding habits in binary code. To capture coding habits, BinAuthor leverages a set of features that are based on collections of functionality-independent choices made by authors during coding. Our evaluation demonstrates that BinAuthor outperforms existing methods in several aspects. First, it successfully attributes a larger number of authors with a significantly higher accuracy (around 90 %) based on the large datasets extracted from selected open-source C + + projects in GitHub, Google Code Jam events, Planet Source Code contests, and several programming projects. Second, BinAuthor is more robust than previous methods; there is no significant drop in accuracy when the code is subjected to refactoring techniques, simple obfuscation, and processed with different compilers. Finally, decoupling authorship from functionality allows us to apply BinAuthor to real malware binaries (Citadel, Zeus, Stuxnet, Flame, Bunny, and Babar) to automatically generate evidence on similar coding habits. [ABSTRACT FROM AUTHOR]