Boolean networks (BNs) are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of BNs, depending on their updating scheme (synchronous, asynchronous, deterministic, or non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), and Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behavior of BNs, so-called attractor, can provide valuable insights into systems biology (e.g., the origins of cancer). In the previous paper [1], we have studied properties of attractors of GARBNs, their relations with attractors of CRBNs, also proposed different algorithms for attractor detection. In this paper, we propose a new algorithm based on SAT-based bounded model checking to overcome inherent problems in these algorithms. Experimental results prove the effectiveness of the new algorithm. We also show that studying attractors of GARBNs can pave potential ways to study attractors of ARBNs.

Deep learning is gaining more and more lots of attractions and better performance in implementing the Intrusion Detection System (IDS), especially for feature learning. This paper presents the state-of-the-art advances and challenges in IDS using deep learning models, which have been achieved the big performance enhancements in the field of computer vision, natural language processing, and image/audio processing than the traditional methods. After providing a systematic and methodical description of the latest developments in deep learning from the points of the deployed architectures and techniques, we suggest the pros-and-cons of all the deep learning-based IDS, and discuss the importance of deep learning models as feature learning approach. For this, the author has suggested the concept of the Deep-Feature Extraction and Selection (D-FES). By combining the stacked feature extraction and the weighted feature selection for D-FES, our experiment was verified to get the best performance of detection rate, 99.918% and false alarm rate, 0.012% to detect the impersonation attacks in Wi-Fi network which can be achieved better than the previous publications. Summary and further challenges are suggested as a concluding remark.

This paper presents a Siamese architecture model with two identical Convolutional Neural Networks (CNNs) to identify code clones; two code fragments are represented as Abstract Syntax Trees (ASTs), CNN-based subnetworks extract feature vectors from the ASTs of pairwise code fragments, and the output layer produces how similar or dissimilar they are. Experimental results demonstrate that CNN-based feature extraction is effective in detecting code clones at source code or bytecode levels.

Register-transfer-level (RTL) information is hardly available for hardware Trojan detection. In this paper, four RTL Trojan features related to branching statement are proposed. The Minimum Redundancy Maximum Relevance (mRMR) feature selection is applied to the proposed Trojan features to determine the recommended feature combinations. The feature combinations are then tested using different machine learning concepts in order to determine the best approach for classifying Trojan and normal branches. The result shows that a Decision Tree classification algorithm with all the four proposed Trojan features can achieve an average true positive detection rate of 93.72% on unseen test data.

Unlike black-box cryptography, an adversary in a white-box security model has full access to the implementation of the cryptographic algorithm. Thus, white-box implementation of cryptographic algorithms is more practical. Nevertheless, in recent years, there is no white-box implementation for public key cryptography. In this paper, we propose the first white-box implementation of the identity-based signature scheme in the IEEE P1363 standard. Our main idea is to hide the private key to multiple lookup tables, so that the private key cannot be leaked during the algorithm executed in the untrusted environment. We prove its security in both black-box and white-box models. We also evaluate the performance of our white-box implementations, in order to demonstrate utility for real-world applications.

One of the major causes of mortalities around the globe is lung cancer with the least chance of survival even after the diagnosis. Computer-aided detection can play an important role, especially in initial screening and thus prevent the deaths caused by lung cancer. In this paper, a novel technique for lung nodule detection, which is the primary cause of lung cancer, is proposed using convolutional neural networks. Initially, the lung volume is segmented from a CT image using optimal thresholding which is followed by image enhancement using multi-scale dot enhancement filtering. Next, lung nodule candidates are detected from an enhanced image and certain features are extracted. The extracted features belong to intensity, shape and texture class. Finally, the classification of lung nodule candidates into nodules and non-nodules is done using a convolutional neural network. The Lung Image Database Consortium (LIDC) dataset has been used to evaluate the proposed system which achieved an accuracy of 94.80% with 6.2 false positives per scan only.

Biomedical event extraction is an important and challenging task in Information Extraction, which plays a key role for medicine research and disease prevention. Most of the existing event detection methods are based on shallow machine learning methods which mainly rely on domain knowledge and elaborately designed features. Another challenge is that some crucial information as well as the interactions among words or arguments may be ignored since most works treat words and sentences equally. Therefore, we employ a Bidirectional Long Short Term Memory (BLSTM) neural network for event extraction, which can skip handcrafted complex feature extraction. Furthermore, we propose a multi-level attention mechanism, including word level attention which determines the importance of words in a sentence, and the sentence level attention which determines the importance of relevant arguments. Finally, we train dependency word embeddings and add sentence vectors to enrich semantic information. The experimental results show that our model achieves an F-score of 59.61% on the commonly used dataset (MLEE) of biomedical event extraction, which outperforms other state-of-the-art methods.

We introduce a method to estimate the attractiveness of a food photo. It extracts image features focusing on the appearances of 1) the entire food, and 2) the main ingredients. To estimate the attractiveness of an arbitrary food photo, these features are integrated in a regression scheme. We also constructed and released a food image dataset composed of images of ten food categories taken from 36 angles and accompanied with attractiveness values. Evaluation results showed the effectiveness of integrating the two kinds of image features.

Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction. However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking. In this paper, we propose a robust tracking algorithm with low-dimensional target-specific feature extraction. First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient. Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer. Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion. Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers. Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.

Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.

Salient region detection is a fundamental problem in computer vision and image processing. Deep learning models perform better than traditional approaches but suffer from their huge parameters and slow speeds. To handle these problems, in this paper we propose the multi-feature fusion network (MFFN) - a efficient salient region detection architecture based on Convolution Neural Network (CNN). A novel feature extraction structure is designed to obtain feature maps from CNN. A fusion dense block is used to fuse all low-level and high-level feature maps to derive salient region results. MFFN is an end-to-end architecture which does not need any post-processing procedures. Experiments on the benchmark datasets demonstrate that MFFN achieves the state-of-the-art performance on salient region detection and requires much less parameters and computation time. Ablation experiments demonstrate the effectiveness of each module in MFFN.

This study presents a joint dictionary learning approach for speech emotion recognition named locality preserved joint nonnegative matrix factorization (LP-JNMF). The learned representations are shared between the learned dictionaries and annotation matrix. Moreover, a locality penalty term is incorporated into the objective function. Thus, the system's discriminability is further improved.

Target recognition in Millimeter-wave Interferometric Synthetic Aperture Radiometer (MMW InSAR) imaging is always a crucial task. However, the recognition performance of conventional algorithms degrades when facing unpredictable noise interference in practical scenarios and information-loss caused by inverse imaging processing of InSAR. These difficulties make it very necessary to develop general-purpose denoising techniques and robust feature extractors for InSAR target recognition. In this paper, we propose a denoising convolutional neural network (D-CNN) and demonstrate its advantage on MMW InSAR automatic target recognition problem. Instead of directly feeding the MMW InSAR image to the CNN, the proposed algorithm utilizes the visibility function samples as the input of the fully connected denoising layer and recasts the target recognition as a data-driven supervised learning task, which learns the robust feature representations from the space-frequency domain. Comparing with traditional methods which act on the MMW InSAR output images, the D-CNN will not be affected by information-loss accused by inverse imaging process. Furthermore, experimental results on the simulated MMW InSAR images dataset illustrate that the D-CNN has superior immunity to noise, and achieves an outstanding performance on the recognition task.

Web person search often return web pages related to several distinct namesakes. This paper proposes a new web page model for template-free person data extraction, and uses Dirichlet Process Mixture model to solve name disambiguation. The results show that our method works best on web pages with complex structure.

This paper presents the method of moments based on electric field integral equation which is capable of solving three-dimensional metallic waveguide problem with no use of another method. Metals are treated as perfectly electric conductor. The integral equation is derived in detail. In order to validate the proposed method, the numerical results are compared with those in a published paper. Three types of waveguide are considered: step discontinuity waveguide, symmetrical resonant iris waveguide, and unsymmetrical resonant iris waveguide. The numerical results are also verified by the law of conservation of energy.

Word boundary ambiguity in word segmentation has long been a fundamental challenge within Thai language processing. The Conditional Random Fields (CRF) model is among the best-known methods to have achieved remarkably accurate segmentation. Nevertheless, current advancements appear to have left the problem of compound words unaccounted for. Compound words lose their meaning or context once segmented. Hence, we introduce a dictionary-based word-merging algorithm, which merges all kinds of compound words. Our evaluation shows that the algorithm can accomplish a high-accuracy of word segmentation, with compound words being preserved. Moreover, it can also restore some incorrectly segmented words. Another problem involving a different word-chunking approach is sentence boundary ambiguity. In tackling the problem, utilizing the part of speech (POS) of a segmented word has been found previously to help boost the accuracy of CRF-based sentence segmentation. However, not all segmented words can be tagged. Thus, we propose a POS-based word-splitting algorithm, which splits words in order to increase POS tags. We found that with more identifiable POS tags, the CRF model performs better in segmenting sentences. To demonstrate the contributions of both methods, we experimented with three of their applications. With the word merging algorithm, we found that intact compound words in the product of topic extraction can help to preserve their intended meanings, offering more precise information for human interpretation. The algorithm, together with the POS-based word-splitting algorithm, can also be used to amend word-level Thai-English translations. In addition, the word-splitting algorithm improves sentence segmentation, thus enhancing text summarization.

A tremendous amount of knowledge is present in the ever-growing scientific literature. In order to efficiently grasp such knowledge, various computational tasks are proposed that train machines to read and analyze scientific documents. One of these tasks, Scientific Relation Extraction, aims at automatically capturing scientific semantic relationships among entities in scientific documents. Conventionally, only a limited number of commonly used knowledge bases, such as Wikipedia, are used as a source of background knowledge for relation extraction. In this work, we hypothesize that unannotated scientific papers could also be utilized as a source of external background information for relation extraction. Based on our hypothesis, we propose a model that is capable of extracting background information from unannotated scientific papers. Our experiments on the RANIS corpus [1] prove the effectiveness of the proposed model on relation extraction from scientific articles.

In this paper, we propose a highly efficient mobile visual search algorithm. For descriptor extraction process, we propose a low complexity feature detection which utilizes the detected local key points of the coarse octaves to guide the scale space construction and feature detection in the fine octave. The Gaussian and Laplacian operations are skipped for the unimportant area, and thus the computing time is saved. Besides, feature selection is placed before orientation computing to further reduce the complexity of feature detection by pre-discarding some unimportant local points. For the image retrieval process, we design a high-performance reranking method, which merges both the global descriptor matching score and the local descriptor similarity score (LDSS). In the calculating of LDSS, the tf-idf weighted histogram matching is performed to integrate the statistical information of the database. The results show that the proposed highly efficient approach achieves comparable performance with the state-of-the-art for mobile visual search, while the descriptor extraction complexity is largely reduced.

In this paper, we propose a design of reversible adder/subtractor blocks and arithmetic logic units (ALUs). The main concept of our approach is different from that of the existing related studies; we emphasize the function design. Our approach of investigating the reversible functions includes (a) the embedding of irreversible functions into incompletely-specified reversible functions, (b) the operation assignment, and (c) the permutation of function outputs. We give some extensions of these techniques for further improvements in the design of reversible functions. The resulting reversible circuits are smaller than that of the existing design in terms of the number of multiple-control Toffoli gates. To evaluate the quantum cost of the obtained circuits, we convert the circuits to reduced quantum circuits for experiments. The results also show the superiority of our realization of adder/subtractor blocks and ALUs in quantum cost.

Efficiency and flexibility of collections have a significant impact on the overall performance of applications. The current approaches to implement collections have two main drawbacks: (i) they limit the efficiency of collections and (ii) they have not adequate support for collection composition. So, when the efficiency and flexibility of collections is important, the programmer needs to implement them himself, which leads to the loss of reusability. This article presents neoCollection, a novel approach to encapsulate collections. neoCollection has several distinguishing features: (i) it can be applied on data elements efficiently and flexibly (ii) composition of collections can be made efficiently and flexibly, a feature that does not exist in the current approaches. In order to demonstrate its effectiveness, neoCollection is implemented as an extension to Java and C++.