Information — Open Access Journal of Information Science

To improve the invisibility and robustness of the multiplicative watermarking algorithm, an adaptive image watermarking algorithm is proposed based on the visual saliency model and Laplacian distribution in the wavelet domain. The algorithm designs an adaptive multiplicative watermark strength factor by utilizing the energy aggregation of the high-frequency wavelet sub-band, texture masking and visual saliency characteristics. Then, the image blocks with high-energy are selected as the watermark embedding space to implement the imperceptibility of the watermark. In terms of watermark detection, the Laplacian distribution model is used to model the wavelet coefficients, and a blind watermark detection approach is exploited based on the maximum likelihood scheme. Finally, this paper performs the simulation analysis and comparison of the performance of the proposed algorithm. Experimental results show that the proposed algorithm is robust against additive white Gaussian noise, JPEG compression, median filtering, scaling, rotation attack and other attacks. Full article

Classification of data with imbalanced class distribution has encountered a significant drawback by most conventional classification learning methods which assume a relatively balanced class distribution. This paper proposes a novel classification method based on data-partition and SMOTE for imbalanced learning. The proposed method differs from conventional ones in both the learning and prediction stages. For the learning stage, the proposed method uses the following three steps to learn a class-imbalance oriented model: (1) partitioning the majority class into several clusters using data partition methods such as K-Means, (2) constructing a novel training set using SMOTE on each data set obtained by merging each cluster with the minority class, and (3) learning a classification model on each training set using convention classification learning methods including decision tree, SVM and neural network. Therefore, a classifier repository consisting of several classification models is constructed. With respect to the prediction stage, for a given example to be classified, the proposed method uses the partition model constructed in the learning stage to select a model from the classifier repository to predict the example. Comprehensive experiments on KEEL data sets show that the proposed method outperforms some other existing methods on evaluation measures of recall, g-mean, f-measure and AUC. Full article

Some homomorphism theorems of neutrosophic extended triplet group (NETG) are proved in the paper [Fundamental homomorphism theorems for neutrosophic extended triplet groups, Symmetry 2018, 10(8), 321; doi:10.3390/sym10080321]. These results are revised in this paper. First, several counterexamples are given to show that some results in the above paper are not true. Second, two new notions of normal NT-subgroup and complete normal NT-subgroup in neutrosophic extended triplet groups are introduced, and their properties are investigated. Third, a new concept of perfect neutrosophic extended triplet group is proposed, and the basic homomorphism theorem of perfect neutrosophic extended triplet groups is established. Full article

A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%. Full article

In our previous work, we proposed a user interface in which a user wears a projector and a depth camera on his or her head and performs touch operations on an image projected on a flat surface. By using the head-mounted projector, images are always projected in front of the user in the direction of the user’s gaze. The image to be projected is changed according to the user’s head pose so as to fix the superimposed image on the surface, which realizes a large effective screen size. In this paper, we conducted an experiment for evaluating the accuracy of registration by measuring the positional and rotational errors between the real world and the superimposed image using our experimental system. As a result, the mean absolute errors of translation were about 10 mm when the user stopped his head, and the delay was estimated to be about 0.2 s. We also discuss the limitations of our prototype and show the direction of future development. Full article

The K-nearest neighbour classifier is very effective and simple non-parametric technique in pattern classification; however, it only considers the distance closeness, but not the geometricalplacement of the k neighbors. Also, its classification performance is highly influenced by the neighborhood size k and existing outliers. In this paper, we propose a new local mean based k-harmonic nearest centroid neighbor (LMKHNCN) classifier in orderto consider both distance-based proximity, as well as spatial distribution of k neighbors. In our method, firstly the k nearest centroid neighbors in each class are found which are used to find k different local mean vectors, and then employed to compute their harmonic mean distance to the query sample. Lastly, the query sample is assigned to the class with minimum harmonic mean distance. The experimental results based on twenty-six real-world datasets shows that the proposed LMKHNCN classifier achieves lower error rates, particularly in small sample-size situations, and that it is less sensitive to parameter k when compared to therelated four KNN-based classifiers. Full article

Network traffic classification aims to identify categories of traffic or applications of network packets or flows. It is an area that continues to gain attention by researchers due to the necessity of understanding the composition of network traffics, which changes over time, to ensure the network Quality of Service (QoS). Among the different methods of network traffic classification, the payload-based one (DPI) is the most accurate, but presents some drawbacks, such as the inability of classifying encrypted data, the concerns regarding the users’ privacy, the high computational costs, and ambiguity when multiple signatures might match. For that reason, machine learning methods have been proposed to overcome these issues. This work proposes a Multi-Objective Divide and Conquer (MODC) model for network traffic classification, by combining, into a hybrid model, supervised and unsupervised machine learning algorithms, based on the divide and conquer strategy. Additionally, it is a flexible model since it allows network administrators to choose between a set of parameters (pareto-optimal solutions), led by a multi-objective optimization process, by prioritizing flow or byte accuracies. Our method achieved 94.14% of average flow accuracy for the analyzed dataset, outperforming the six DPI-based tools investigated, including two commercial ones, and other machine learning-based methods. Full article

Extractive summarization aims to produce a concise version of a document by extracting information-rich sentences from the original texts. The graph-based model is an effective and efficient approach to rank sentences since it is simple and easy to use. However, its performance depends heavily on good text representation. In this paper, an integrated graph model (iGraph) for extractive text summarization is proposed. An enhanced embedding model is used to detect the inherent semantic properties at the word level, bigram level and trigram level. Words with part-of-speech (POS) tags, bigrams and trigrams were extracted to train the embedding models. Based on the enhanced embedding vectors, the similarity values between the sentences were calculated in three perspectives. The sentences in the document were treated as vertexes and the similarity between them as edges. As a result, three different types of semantic graphs were obtained for every document, with the same nodes and different edges. These three graphs were integrated into one enriched semantic graph in a naive Bayesian fashion. After that, TextRank, which is a graph-based ranking algorithm, was applied to rank the sentences, before the top scored sentences were selected for the summary according to the compression rate. Evaluated on the DUC 2002 and DUC 2004 datasets, our proposed method shows competitive performance compared to the state-of-the-art methods. Full article

Cryptovirological augmentations present an immediate, incomparable threat. Over the last decade, the substantial proliferation of crypto-ransomware has had widespread consequences for consumers and organisations alike. Established preventive measures perform well, however, the problem has not ceased. Reverse engineering potentially malicious software is a cumbersome task due to platform eccentricities and obfuscated transmutation mechanisms, hence requiring smarter, more efficient detection strategies. The following manuscript presents a novel approach for the classification of cryptographic primitives in compiled binary executables using deep learning. The model blueprint, a Dynamic Convolutional Neural Network (DCNN), is fittingly configured to learn from variable-length control flow diagnostics output from a dynamic trace. To rival the size and variability of equivalent datasets, and to adequately train our model without risking adverse exposure, a methodology for the procedural generation of synthetic cryptographic binaries is defined, using core primitives from OpenSSL with multivariate obfuscation, to draw a vastly scalable distribution. The library, CryptoKnight, rendered an algorithmic pool of AES, RC4, Blowfish, MD5 and RSA to synthesise combinable variants which automatically fed into its core model. Converging at 96% accuracy, CryptoKnight was successfully able to classify the sample pool with minimal loss and correctly identified the algorithm in a real-world crypto-ransomware application. Full article

The paper examines today’s debate on the legal status of AI robots, and how often scholars and policy makers confuse the legal agenthood of these artificial agents with the status of legal personhood. By taking into account current trends in the field, the paper suggests a twofold stance. First, policy makers shall seriously mull over the possibility of establishing novel forms of accountability and liability for the activities of AI robots in contracts and business law, e.g., new forms of legal agenthood in cases of complex distributed responsibility. Second, any hypothesis of granting AI robots full legal personhood has to be discarded in the foreseeable future. However, how should we deal with Sophia, which became the first AI application to receive citizenship of any country, namely, Saudi Arabia, in October 2017? Admittedly, granting someone, or something, legal personhood is—as always has been—a highly sensitive political issue that does not simply hinge on rational choices and empirical evidence. Discretion, arbitrariness, and even bizarre decisions play a role in this context. However, the normative reasons why legal systems grant human and artificial entities, such as corporations, their status, help us taking sides in today’s quest for the legal personhood of AI robots. Is citizen Sophia really conscious, or capable of suffering the slings and arrows of outrageous scholars? Full article

High resolution remote sensing (HRRS) images are widely used in many sensitive fields, and their security should be protected thoroughly. Integrity authentication is one of their major security problems, while the traditional techniques cannot fully meet the requirements. In this paper, a perceptual hashing based forensics scheme is proposed for the integrity authentication of a HRRS image. The proposed scheme firstly partitions the HRRS image into grids and adaptively pretreats the grid cells according to the entropy. Secondly, the multi-scale edge features of the grid cells are extracted by the edge chains based on the adaptive strategy. Thirdly, principal component analysis (PCA) is applied on the extracted edge feature to get robust feature, which is then normalized and encrypted with secret key set by the user to receive the perceptual hash sequence. The integrity authentication procedure is achieved via the comparison between the recomputed perceptual hash sequence and the original one. Experimental results have shown that the proposed scheme has good robustness to normal content-preserving manipulations, has good sensitivity to detect local subtle and illegal tampering of the HRRS image, and has the ability to locate the tampering area. Full article

Web page clustering is an important technology for sorting network resources. By extraction and clustering based on the similarity of the Web page, a large amount of information on a Web page can be organized effectively. In this paper, after describing the extraction of Web feature words, calculation methods for the weighting of feature words are studied deeply. Taking Web pages as objects and Web feature words as attributes, a formal context is constructed for using formal concept analysis. An algorithm for constructing a concept lattice based on cross data links was proposed and was successfully applied. This method can be used to cluster the Web pages using the concept lattice hierarchy. Experimental results indicate that the proposed algorithm is better than previous competitors with regard to time consumption and the clustering effect. Full article

Many physiology and bioinformatics research institutions and websites have opened their own data analysis services and other related Web services. It is therefore very important to be able to quickly and effectively select and extract features from the Web service pages to learn about and use these services. This facilitates the automatic discovery and recognition of Representational State Transfer or RESTful services. However, this task is still challenging. Following the description feature pattern of a RESTful service, the authors proposed a Feature Pattern Search and Replace (FPSR) method. First, they applied a regular expression to perform a matching lookup. Then, a custom string was used to substitute the relevant feature pattern to avoid the segmentation of its feature pattern and the loss of its feature information during the segmentation process. Experimental results showed in the visualization that FPSR obtained a clearer and more obvious boundary with fewer overlaps than the test without using FPSR, thereby enabling a higher accuracy rate. Therefore, FPSR allowed the authors to extract RESTful service page feature information and achieve better classification results. Full article

In this paper, a hesitant probabilistic fuzzy multiple attribute group decision making is studied. First, some Einstein operations on hesitant probability fuzzy elements such as the Einstein sum, Einstein product, and Einstein scalar multiplication are presented and their properties are discussed. Then, several hesitant probabilistic fuzzy Einstein aggregation operators, including the hesitant probabilistic fuzzy Einstein weighted averaging operator and the hesitant probabilistic fuzzy Einstein weighted geometric operator and so on, are introduced. Moreover, some desirable properties and special cases are investigated. It is shown that some existing hesitant fuzzy aggregation operators and hesitant probabilistic fuzzy aggregation operators are special cases of the proposed operators. Further, based on the proposed operators, a new approach of hesitant probabilistic fuzzy multiple attribute decision making is developed. Finally, a practical example is provided to illustrate the developed approach. Full article

Silicon Physical Unclonable Functions (sPUFs) are one of the security primitives and state-of-the-art topics in hardware-oriented security and trust research. This paper presents an efficient and dynamic ring oscillator PUFs (d-ROPUFs) technique to improve sPUFs security against modeling attacks. In addition to enhancing the Entropy of weak ROPUF design, experimental results show that the proposed d-ROPUF technique allows the generation of larger and updated challenge-response pairs (CRP space) compared with simple ROPUF. Additionally, an innovative hardware-oriented security algorithm, namely, the Optimal Time Delay Algorithm (OTDA), is proposed. It is demonstrated that the OTDA algorithm significantly improves PUF reliability under varying operating conditions. Further, it is shown that the OTDA further efficiently enhances the d-ROPUF capability to generate a considerably large set of reliable secret keys to protect the PUF structure from new cyber-attacks, including machine learning and modeling attacks. Full article

The Lightning Network (LN) is one of the most promising off-chain scaling solutions for Bitcoin, as it enables off-chain payments which are not subject to the well-known blockchain scalability limit. In this work, we introduce CLoTH, a simulator for HTLC payment networks (of which LN is the best working example). It simulates input-defined payments on an input-defined HTLC network and produces performance measures in terms of payment-related statistics (such as time to complete payments and probability of payment failure). CLoTH helps to predict issues and obstacles that might emerge in the development stages of an HTLC payment network and to estimate the effects of an optimisation action before deploying it. We conducted simulations on a recent snapshot of the HTLC payment network of LN. These simulations allowed us to identify network and payments configurations for which a payment is more likely to fail than to succeed. We proposed viable solutions to avoid such configurations. Full article

Software applications have become a fundamental part in the daily work of modern society as they meet different needs of users in different domains. Such needs are known as software requirements (SRs) which are separated into functional (software services) and non-functional (quality attributes). The first step of every software development project is SR elicitation. This step is a challenge task for developers as they need to understand and analyze SRs manually. For example, the collected functional SRs need to be categorized into different clusters to break-down the project into a set of sub-projects with related SRs and devote each sub-project to a separate development team. However, functional SRs clustering has never been considered in the literature. Therefore, in this paper, we propose an approach to automatically cluster functional requirements based on semantic measure. An empirical evaluation is conducted using four open-access software projects to evaluate our proposal. The experimental results demonstrate that the proposed approach identifies semantic clusters according to well-known used measures in the subject. Full article

Proponents of the singularity hypothesis have argued that there will come a point at which machines will overtake us not only in intelligence but that machines will also have emotional capabilities. However, human cognition is not something that takes place only in the brain; one cannot conceive of human cognition without embodiment. This essay considers the emotional nature of cognition by exploring the most human of emotions—romantic love. By examining the idea of love from an evolutionary and a physiological perspective, the author suggests that in order to account for the full range of human cognition, one must also account for the emotional aspects of cognition. The paper concludes that if there is to be a singularity that transcends human cognition, it must be embodied. As such, the singularity could not be completely non-organic; it must take place in the form of a cyborg, wedding the digital to the biological. Full article