Current software behavior models lack the ability to conduct semantic analysis. We propose a new model to detect abnormal behaviors based on a function semantic tree. First, a software behavior model in terms of state graph and software function is developed. Next, anomaly detection based on the model is conducted in two main steps: calculating deviation density of suspicious behaviors by comparison with state graph and detecting function sequence by function semantic rules. Deviation density can well detect control flow attacks by a deviation factor and a period division. In addition, with the help of semantic analysis, function semantic rules can accurately detect application layer attacks that fail in traditional approaches. Finally, a case study of RSS software illustrates how our approach works. Case study and a contrast experiment have shown that our model has strong expressivity and detection ability, which outperforms traditional behavior models.

In this letter, a novel collaborative representation graph based on the local and global consistency label propagation method, denoted as CRLGC, is proposed. The collaborative representation graph is used to reduce the cost time in obtaining the graph which evaluates the similarity of samples. Considering the lacking of labeled samples in real applications, a semi-supervised label propagation method is utilized to transmit the labels from the labeled samples to the unlabeled samples. Experimental results on three image data sets have demonstrated that the proposed method provides the best accuracies in most times when compared with other traditional graph-based semi-supervised classification methods.

In this paper, a feature named Non-Redundant Gradient Semantic Local Binary Patterns (NRGSLBP) is proposed for human detection as a modified version of the conventional Semantic Local Binary Patterns (SLBP). Calculations of this feature are performed for both intensity and gradient magnitude image so that texture and gradient information are combined. Moreover, and to the best of our knowledge, non-redundant patterns are adopted on SLBP for the first time, allowing better discrimination. Compared with SLBP, no additional cost of the feature dimensions of NRGSLBP is necessary, and the calculation complexity is considerably smaller than that of other features. Experimental results on several datasets show that the detection rate of our proposed feature outperforms those of other features such as Histogram of Orientated Gradient (HOG), Histogram of Templates (HOT), Bidirectional Local Template Patterns (BLTP), Gradient Local Binary Patterns (GLBP), SLBP and Covariance matrix (COV).

We present a matching method for 3D CAD assembly models consisting of multiple components. Here we need to distinguish the layouts and the materials of the components in addition to their shapes. A set of the feature quantities of an assembly model is extracted using projections from various angles. We show the effectiveness of our method experimentally for 3D CAD assembly models.

The main contribution of this paper is to characterize the hyperbentness of two infinite classes of Boolean functions via Dillon-like exponents, and give new classes of semibent functions with Dillon-like exponents and Niho exponents. In this paper, the approaches of Mesnager and Wang et al. are generalized to Charpin-Gong like functions with two additional trace terms. By using the partial exponential sums and Dickson polynomials, it also gives the necessary and sufficient conditions of the hyperbent properties for their subclasses of Boolean functions, and gives two corresponding examples on F230. Thanks to the result of Carlet et al., new classes of semibent functions are obtained by using new hyperbent functions and the known Niho bent functions. Finally, this paper extends the Works of Lisonek and Flori and Mesnager, and gives different characterizations of new hyperbent functions and new semibent functions with some restrictions in terms of the number of points on hyperelliptic curves. These results provide more nonlinear functions for designing the filter generators of stream ciphers.

Small cell networks have been promoted as an enabling solution to enhance indoor coverage and improve spectral efficiency. Users usually deploy small cells on-demand and pay no attention to global profile in residential areas or offices. The reduction of cell radius leads to dense deployment which brings intractable computation complexity for resource allocation. In this paper, we develop a semi-distributed resource allocation algorithm by dividing small cell networks into clusters with limited inter-cluster interference and selecting a reference cluster for interference estimation to reduce the coordination degree. Numerical results show that the proposed algorithm can maintain similar system performance while having low complexity and reduced information exchange overheads.

How to retrieve the closest content from an in-network cache is one of the most important issues in Information-Centric Networking (ICN). This paper proposes a novel content discovery scheme called Local Tree Hunting (LTH). By adding branch-cast functionality to a local tree for content requests to a Content-Centric Network (CCN) response node, the discovery area for caching nodes expands. Since the location of such a branch-casting node moves closer to the request node when the content is more widely cached, the discovery range, i.e. the branch size of the local tree, becomes smaller. Thus, the discovery area is autonomously adjusted depending on the content dissemination. With this feature, LTH is able to find the “almost true closest” caching node without checking all the caching nodes in the in-network cache. The performance analysis employed in Zipf's law content distribution model and which uses the Least Recently Used eviction rule shows the superiority of LTH with respect to identifying the almost exact closest cache.

Content caching networks like Information-Centric Networking (ICN) are beneficial to reduce the network traffic by storing content data on routers near to users. In ICN, it becomes an important issue to manage system resources, such as storage and network bandwidth, which are influenced by cache characteristics of each cache node. Meanwhile, cache aging techniques based on Time-To-Live (TTL) of content facilitate analyzing cache characteristics and can realize appropriate resource management by setting efficient TTLs. However, it is difficult to search for the efficient TTLs in a distributed cache system connected by multiple cache nodes. Therefore, we propose an adaptive control mechanism of the TTL value of content in distributed cache systems by using predictive models which can estimate the impact of the TTL values on network resources and cache performance. Furthermore, we show the effectiveness of the proposed mechanism.

In this work, the differential received signal strength based localization problem is addressed. Based on the measurement model, we present the constrained weighted least squares (CWLS) approach, which is difficult to be solved directly due to its nonconvex nature. However, by performing the semidefinite relaxation (SDR) technique, the CWLS problem can be relaxed into a semidefinite programming problem (SDP), which can be efficiently solved using modern convex optimization algorithms. Moreover, the SDR is proved to be tight, and hence ensures the corresponding SDP find the optimal solution of the original CWLS problem. Numerical simulations are included to corroborate the theoretical results and promising performance.

We deposited thin films of thiophene/phenylene co-oligomers (TPCOs) onto poly(tetrafluoroethylene) (PTFE) layers that were friction-transferred on substrates. These films were composed of aligned molecules in such a way that their polarizations of emissions and absorbances were larger along the drawing direction than those perpendicular to that direction. Organic field-effect transistors (OFETs) fabricated with these films indicated large mobilities, when the drawing direction of PTFE was parallel to the channel length direction. The friction-transfer technique forms the TPCO films that indicate the anisotropic optical and electronic properties.

The novel ladder-shaped polydiacetylene with a terephthalamide linker in the molecular center, namely poly(TPh-bisDA) was synthesized by photo-polymerization. The characteristics of thin films of polymer were dependent upon a casting solvent, but no significant change of backbone conformation of the PDA was observed. Obtained film is expected to be applied to the semi-conducting materials for organic field effect transistors (OFET).

We report a real time method to monitor the chemical reaction in microdroplets, which contain an organic dye, 5(6)-carboxynaphthofluorescein and a CdSe/ZnS quantum dot using fluorescence spectra. Especially, the relationship between the droplet size and the reaction rate of the two reagents was investigated by changing an injection speed.

In recent years, there has been a significant growth in the importance of data mining of graph-structured data due to this technology's rapid increase in both scale and application areas. Many previous studies have investigated decision tree learning on Semantic Web-based linked data to uncover implicit knowledge. In the present paper, we suggest a new random forest algorithm for linked data to overcome the underlying limitations of the decision tree algorithm, such as local optimal decisions and generalization error. Moreover, we designed a parallel processing environment for random forest learning to manage large-size linked data and increase the efficiency of multiple tree generation. For this purpose, we modified the previous candidate feature searching method of the decision tree algorithm for linked data to reduce the feature searching space of random forest learning and developed feature selection methods that are adjusted to linked data. Using a distributed index-based search engine, we designed a parallel random forest learning system for linked data to generate random forests in parallel. Our proposed system enables users to simultaneously generate multiple decision trees from distributed stored linked data. To evaluate the performance of the proposed algorithm, we performed experiments to compare the classification accuracy when using the single decision tree algorithm. The experimental results revealed that our random forest algorithm is more accurate than the single decision tree algorithm.

We previously proposed an inaudible non-blind digital-audio watermarking approach based on cochlear delay (CD) characteristics. There are, however, three remaining issues with regard to blind-detectability, frame synchronization related to confidentiality, and reversibility. We attempted to solve these issues in developing the proposed approach by taking blind-detectability and reversibility of audio watermarking into consideration. Frame synchronization was also incorporated into the proposed approach to improve confidentiality. We evaluated inaudibility, robustness, and reversibility with the new approach by carrying out three objective tests (PEAQ, LSD, and bit-detection or SNR) and six robustness tests. The results revealed that inaudible, robust, blindly-detectable, and semi-reversible watermarking based on CD could be accomplished.

Amplification characteristics of the signal and the noise in the semiconductor optical amplifier (SOA), without facet mirrors for the intensity modulated light, are theoretically analyzed and experimentally confirmed. We have found that the amplification factor of the temporarily varying intensity component is smaller than that of the continuous wave (CW) component, but increases up to that of the CW component in the high frequency region in the SOA. These properties are very peculiar in the SOA, which is not shown in conventional electronic devices and semiconductor lasers. Therefore, the relative intensity noise (RIN), which is defined as ratio of the square value of the intensity fluctuation to that of the CW power can be improved by the amplification by the SOA. On the other hand, the signal to the noise ratio (S/N ratio) defined for ratio of the square value of the modulated signal power to that of the intensity fluctuation have both cases of the degradation and the improvement by the amplification depending on combination of the modulation and the noise frequencies. Experimental confirmations of these peculiar characteristics are also demonstrated.

We have investigated the microwave-detected photoconductivity responses from the amorphous In--Ga--Zn--O (a-IGZO) thin films. The time constant extracted by the slope of the slow part of the reflectivity signals are correlated with TFT performances. We have evaluated the influences of the sputtering conditions on the quality of a-IGZO thin film, as well as the influences of gate insulation films and annealing conditions, by comparing the TFT characteristics with the microwave photoconductivity decay ($mu$-PCD). It is concluded that the $mu$-PCD is a promising method for in-line process monitoring for the IGZO-TFTs fabrication.

A top-k keyword query in relational databases returns k trees of tuples — where the tuples containing the query keywords are connected via primary key-foreign key relationships — in the order of relevance to the query. Existing works are classified into two categories: 1) the schema-based approach and 2) the schema-free approach. We focus on the former utilizing database schema information for more effective ranking of the query results. Ranking measures used in existing works can be classified into two categories: 1) the size of the tree (i.e., the syntactic score) and 2) ranking measures, such as TF-IDF, borrowed from the information retrieval field. However, these measures do not take into account semantic relevancy among relations containing the tuples in the query results. In this paper, we propose a new ranking method that ranks the query results by utilizing semantic relevancy among relations containing the tuples at the schema level. First, we propose a structure of semantically strongly related relations, which we call the strongly related tree (SRT). An SRT is a tree that maximally connects relations based on the lossless join property. Next, we propose a new ranking method, SRT-Rank, that ranks the query results by a new scoring function augmenting existing ones with the concept of the SRT. SRT-Rank is the first research effort that applies semantic relevancy among relations to ranking the results of keyword queries. To show the effectiveness of SRT-Rank, we perform experiments on synthetic and real datasets by augmenting the representative existing methods with SRT-Rank. Experimental results show that, compared with existing methods, SRT-Rank improves performance in terms of four quality measures — the mean normalized discounted cumulative gain (nDCG), the number of queries whose top-1 result is relevant to the query, the mean reciprocal rank, and the mean average precision — by up to 46.9%, 160.0%, 61.7%, and 63.8%, respectively. In addition, we show that the query performance of SRT-Rank is comparable to or better than those of existing methods.

Independent and identically distributed (i.i.d) assumptions are commonly used in the machine learning community. However, social media data violate this assumption due to the linkages. Meanwhile, with the variety of data, there exist many samples, i.e., Universum, that do not belong to either class of interest. These characteristics pose great challenges to dealing with social media data. In this letter, we fully take advantage of Universum samples to enable the model to be more discriminative. In addition, the linkages are also taken into consideration in the means of social dimensions. To this end, we propose the algorithm Semi-Supervised Linked samples Feature Selection with Universum (U-SSLFS) to integrate the linking information and Universum simultaneously to select robust features. The empirical study shows that U-SSLFS outperforms state-of-the-art algorithms on the Flickr and BlogCatalog.

A novel fusion method for semantic concept detection in images, called tree fusion, is proposed. Various kinds of features are given to different classifiers. Then, according to the importance of features and effectiveness of classifiers, the results of feature-classifier pairs are ranked and fused using C4.5 algorithm. Experimental results conducted on the MSRC and PASCAL VOC 2007 datasets have demonstrated the effectiveness of the proposed method over the traditional fusion methods.

In our previous work, we proposed to combine ConceptNet and WordNet for Word Sense Disambiguation (WSD). The ConceptNet was automatically disambiguated through Normalized Google Distance (NGD) similarity. In this letter, we present several techniques to enhance the performance of the ConceptNet disambiguation and use this enriched semantic knowledge in WSD task. We propose to enrich both the WordNet semantic knowledge and NGD to disambiguate the concepts in ConceptNet. Furthermore, we apply the enriched semantic knowledge to improve the performance of WSD. From a number of experiments, the proposed method has been obtained enhanced results.