Reviewerly/exHarmony huggingface dataset:
Privacy Leakage via De-Anonymization and Aggregation in Heterogeneous Social Networks

Though representing a promising approach for personalization, targeting, and recommendation, aggregation of user profiles from multiple social networks will inevitably incur a serious privacy leakage issue. In this paper, we propose a Novel Heterogeneous De-anonymization Scheme (NHDS) aiming at de-anonymizing heterogeneous social networks. NHDS first leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that NHDS significantly outperforms the prior schemes. Finally, we perform an empirical study on privacy leakage arising from cross-network aggregation based on four real-world social network datasets. Our findings show that 39.9 percent more information is disclosed through de-anonymization and the de-anonymized ratio is 84 percent. The detailed privacy leakage of user demographics and interests is also examined, which demonstrates the practicality of the identified privacy leakage issue.
New Discrete-Time Models of Zeroing Neural Network Solving Systems of Time-Variant Linear and Nonlinear Inequalities

In this paper, a new one-step-ahead numerical differentiation rule termed 5-instant discretization formula is proposed for the first-order derivative approximation with higher computational precision. Then, by exploiting the proposed formula to discretize the continuous-time zeroing neural network [or termed, continuous-time Zhang neural network (ZNN)] models, two new discrete-time zeroing neural network [or termed, discrete-time ZNN (DTZNN)] models are proposed, analyzed and investigated for solving systems of discrete time-variant inequalities, including the system of discrete time-variant linear inequalities and the system of discrete time-variant nonlinear inequalities. For comparative purposes, the recently developed Taylor-type DTZNN models and the widely used Euler-type DTZNN models are also presented. Theoretical analyses show that the proposed DTZNN models are convergent, and their steady-state residual errors have an O(g <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) pattern with g denoting the sampling gap. Comparative numerical experimental results further substantiate the efficacy and superiority of the proposed DTZNN models for solving the systems of discrete time-variant inequalities.
Source camera identification based on content-adaptive fusion residual networks

Source camera identification is still a hard task in forensics community, especially for the case of the query images with small size. In this paper, we propose a solution to identify the source camera of the small-size images: content-adaptive fusion residual networks. According to the differences of the image contents, firstly, the images are divided into three subsets: saturation, smoothness and others. Then, we train three fusion residual networks for saturated images, smooth images, and others, separately, through transform learning. The fusion residual networks is formed with three paralleled residual networks and the difference of three residual networks lies in the convolutional kernel size of preprocessing layer. The features learned from the last residual blocks of three residual networks are fused and fed into softmax classifier. In particular, the residual networks is designed to learn better feature representation from the input data. The convolutional operation is added in preprocessing stage and three residual blocks are used. The experiment results show that the proposed method has satisfactory performances at three levels of source camera identification: brand level, model level, and device level.
Text Filtering and Ranking for Security Bug Report Prediction

Security bug reports can describe security critical vulnerabilities in software products.Bug tracking systems may contain thousands of bug reports, where relatively few of them are security related.Therefore finding unlabelled security bugs among them can be challenging.To help security engineers identify these reports quickly and accurately, text-based prediction models have been proposed.These can often mislabel security bug reports due to a number of reasons such as class imbalance, where the ratio of non-security to security bug reports is very high.More critically, we have observed that the presence of security related keywords in both security and non-security bug reports can lead to the mislabelling of security bug reports.This paper proposes FARSEC, a framework for filtering and ranking bug reports for reducing the presence of security related keywords.Before building prediction models, our framework identifies and removes non-security bug reports with security related keywords.We demonstrate that FARSEC improves the performance of text-based prediction models for security bug reports in 90% of cases.Specifically, we evaluate it with 45,940 bug reports from Chromium and four Apache projects.With our framework, we mitigate the class imbalance issue and reduce the number of mislabelled security bug reports by 38%.
Enriching behavioral ecology with reinforcement learning methods

This article focuses on the division of labor between evolution and development in solving sequential, state-dependent decision problems. Currently, behavioral ecologists tend to use dynamic programming methods to study such problems. These methods are successful at predicting animal behavior in a variety of contexts. However, they depend on a distinct set of assumptions. Here, we argue that behavioral ecology will benefit from drawing more than it currently does on a complementary collection of tools, called reinforcement learning methods. These methods allow for the study of behavior in highly complex environments, which conventional dynamic programming methods do not feasibly address. In addition, reinforcement learning methods are well-suited to studying how biological mechanisms solve developmental and learning problems. For instance, we can use them to study simple rules that perform well in complex environments. Or to investigate under what conditions natural selection favors fixed, non-plastic traits (which do not vary across individuals), cue-driven-switch plasticity (innate instructions for adaptive behavioral development based on experience), or developmental selection (the incremental acquisition of adaptive behavior based on experience). If natural selection favors developmental selection, which includes learning from environmental feedback, we can also make predictions about the design of reward systems. Our paper is written in an accessible manner and for a broad audience, though we believe some novel insights can be drawn from our discussion. We hope our paper will help advance the emerging bridge connecting the fields of behavioral ecology and reinforcement learning.
Information hiding scheme for digital images using difference expansion and modulus function

Disguising the presence of communication has become a severe concern in this highly digitalized world due to the unauthorized data access and network policy violations that are emerging rapidly. These issues have led to the application of cryptography technique as a mean for securing data by encrypting them. However, since the encrypted data can be seen by sophisticated intruders during the transmission, this may lead to its suspicion which can results in unauthorized access. Thereby, steganography is another technique for securing communication. Steganography is the practice of concealing confidential information in the codes that make up digital files. Different from encryption, however, steganography provides security by disguising the presence of communication. In this context, this paper presents an improved information hiding implemented based on difference expansion and modulus function. The previous method has only considered the image smooth areas where the difference value is 0 or 1 while ignoring other values for hiding data. These limitations may result in decreasing the embedding capacity for all images having few smooth areas. Hence, a new scheme that considers both positive and negative difference values to conceal secret data is developed. The experimental results prove that the proposed scheme achieves better results than the existing methods.
Binary LCD Codes and Self-Orthogonal Codes From a Generic Construction

Linear codes with certain special properties have received renewed attention in recent years due to their practical applications. Among them, binary linear complementary dual (LCD) codes play an important role in implementations against side-channel attacks and fault injection attacks. Self-orthogonal codes can be used to construct quantum codes. In this paper, four classes of binary linear codes are constructed via a generic construction which has been intensively investigated in the past decade. Simple characterizations of these linear codes to be LCD or self-orthogonal are presented. Resultantly, infinite families of binary LCD codes and self-orthogonal codes are obtained. Infinite families of binary LCD codes from the duals of these four classes of linear codes are produced. Many LCD codes and self-orthogonal codes obtained in this paper are optimal or almost optimal in the sense that they meet certain bounds on general linear codes. In addition, the weight distributions of two sub-families of the proposed linear codes are established in terms of Krawtchouk polynomials.
Time-Division is Optimal for Covert Communication Over Some Broadcast Channels

We consider a covert communication scenario where a transmitter wishes to communicate simultaneously to two legitimate receivers while ensuring that the communication is not detected by an adversary, the warden. The legitimate receivers and the adversary observe the transmission from the transmitter via a three-user discrete or Gaussian memoryless broadcast channel. We focus on the case where the “no-input” symbol is not redundant, i.e., the output distribution at the warden induced by the no-input symbol is not a mixture of the output distributions induced by other input symbols, so that the covert communication is governed by the square root law, i.e., at most Θ(√n) bits can be transmitted over n channel uses. We show that for such a setting, a simple time-division strategy achieves the optimal throughputs for a non-trivial class of broadcast channels; this is not true for communicating over broadcast channels without the covert communication constraint. Our result implies that a code that uses two separate optimal point-to-point codes each designed for the constituent channels and each used for a fraction of the time is optimal in the sense that it achieves the best constants of the √n-scaling for the throughputs. Our proof strategy combines several elements in the network information theory literature, including concave envelope representations of the capacity regions of broadcast channels and El Gamal's outer bound for more capable broadcast channels.
Similarity-Maintaining Privacy Preservation and Location-Aware Low-Rank Matrix Factorization for QoS Prediction Based Web Service Recommendation

Web service recommendation plays an important role in building service-oriented systems. QoS-based Web service recommendation has recently gained much attention for providing a promising way to help users find high-quality services. To accurately predict the QoS values of candidate Web services, Web service recommendation systems usually need to collect historical QoS data from users, which will potentially pose a threat to the user's privacy. However, how to simultaneously protect user's privacy and make an accurate prediction has not been well studied. By taking these two aspects into consideration, we propose a novel QoS prediction approach for Web service recommendation in this paper. Specifically, we first design a similarity-maintaining privacy preservation (SPP) strategy, which aims to protect the user's privacy and maintain the utility of user data in the meanwhile. Then, we propose a location-aware low-rank matrix factorization (LLMF) algorithm, which employs the L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> L1-norm low-rank matrix factorization to improve the model's robustness, and combines the matrix factorization model with two kinds of location information (continent, longitude and latitude) in the prediction process. Experimental results on two publicly available real-world Web service QoS datasets demonstrate the effectiveness of our privacy-preserving QoS prediction approach.
Dissimilarity Gaussian Mixture Models for Efficient Offline Handwritten Text-Independent Identification Using SIFT and RootSIFT Descriptors

Handwriting biometrics is the science of identifying the behavioral aspect of an individual’s writing style and exploiting it to develop automated writer identification and verification systems. This paper presents an efficient handwriting identification system which combines scale-invariant feature transform (SIFT) and RootSIFT descriptors in a set of Gaussian mixture models (GMMs). In particular, a new concept of similarity and dissimilarity Gaussian mixture models (SGMM and DGMM) is introduced. While an SGMM is constructed for every writer to describe the intra-class similarity that is exhibited between the handwritten texts of the same writer, a DGMM represents the contrast or dissimilarity that exists between the writer’s style on one hand and other different handwriting styles on the other hand. Furthermore, because the handwritten text is described by a number of key point descriptors where each descriptor generates an SGMM/DGMM score, a new weighted histogram method is proposed to derive the intermediate prediction score for each writer’s GMM. The idea of weighted histogram exploits the fact that handwritings from the same writer should exhibit more similar textual patterns than dissimilar ones, hence, by penalizing the bad scores with a cost function, the identification rate can be significantly enhanced. Our proposed system has been extensively assessed using six different public datasets (including three English, two Arabic, and one hybrid language), and the results have shown the superiority of the proposed system over the state-of-the-art techniques.
Recommender systems, cultural heritage applications, and the way forward

Modern recommender systems appeared during the last decade of the 20th century and have already proven their importance in tackling with information overload and content or services personalisation in the modern big-data world. This is markedly suggested by their adoption by major industry parties in a wide variety of domains. The application of recommender systems in the Cultural Heritage domain has appeared relatively recently, particularly during the advent of the 21st century. Nevertheless, a large number of works appeared in the scientific literature, driven primarily from large-scale cultural heritage collaborative projects. This paper presents a review of the current recommender system approaches, including some basic mathematical formalism, and focuses on recommender system applications in Cultural Heritage, to identify the advantages, the benefits and prospects, the disadvantages and pitfalls. The paper discusses the diversity and some common assumptions in the reviewed works, outlines some proposals for future development, and takes a position on the way forward.
Attribute-Based Hybrid Boolean Keyword Search over Outsourced Encrypted Data

With cloud computing becoming increasingly popular, there has been a rapid increase in the number of data owners who outsource their data to the cloud while allowing users to retrieve the data. To preserve the privacy of data, data owners usually encrypt their data before outsourcing them to the cloud, and cloud servers can search across the ciphertext domain on behalf of users without learning any information about the data. However, existing work in the literature mostly supports only a single-user or single-keyword search which is not able to satisfy more desired expressive search. Thus, we propose a searchable encryption primitive with attribute-based access control for hybrid boolean keyword search over outsourced encrypted data. There exist several desirable features: (1) Data owners can set search permissions for outsourced encrypted data according to an access control policy. (2) Multiple users, whose attributes satisfy the access control policy, are allowed to perform a retrieval operation upon the encrypted data. (3) Authorized users are able to perform more expressive search, such as any required boolean keyword expression search. Additionally, this primitive is provably secure under our security model and we have also implemented the prototype to show the practicality of the primitive.
Novel secured scheme for blind audio/speech norm-space watermarking by Arnold algorithm

In this paper we propose a new scheme for blind watermarking of speech and audio signals. We used the discrete wavelet transform (DWT) after framing the signal, and then we applied the discrete cosine transform (DCT) on each frame. For correlation purpose, sub-sampling is performed to decompose the frame into two segments. For security concern, Arnold transform is employed on the watermark image in order to save detection security. The fully blind detection is accomplished without using the original speech/audio signal and the insertion parameter is not required. Experimental assessment and comparisons with other published schemes demonstrate a good tradeoff between security, capacity, imperceptibility and robustness against various signal processing attacks for both audio and speech signals.
A method for modelling operational risk with fuzzy cognitive maps and Bayesian belief networks

A main concern of risk management in financial institutions is measurement of operational risk and its value at risk as a requirement of Basel II accord. Besides risk quantification, identifying causal mechanism leading to operational loss is necessary to plan risk mitigation activities. Bayesian belief networks (BBN) is a causal modelling method able to achieve both goals simultaneously. Eliciting BBN causal model and its parameters from expert knowledge is an alternative to data driven models in case of data scarcity. However, there is still a problem with parameter extraction for complex models with a number of multi parent and multi state nodes. In this paper, we proposed a method combining fuzzy cognitive maps (FCM) and BBN in order to improve BBN capability in modelling operational risks. In the first phase, a causal model is constructed by applying FCM and then a new migration method is proposed to translate FCM parameters to BBN ones. A case study of an Iranian private bank is then given to examine and validate the proposed method.
Signal and image compression using quantum discrete cosine transform

The discrete cosine transform (DCT) is widely used in image and video compression standard formats. This is due to its ability to represent signals and images using a limited number of significant coefficients without noticeable loss of visual clarity. The classical one-dimensional discrete cosine transform (1D - DCT) and two-dimensional discrete cosine transform (2D - DCT) have computational complexities of O(Nlog2N) and O(N2log2N), respectively. Thus, as the images grow in size, the runtime of the DCT highly increases which could limit its usability in real-time applications. This paper presents a quantum DCT algorithm (QDCT) that is more efficient than its classical counterpart in terms of complexity. Furthermore, the proposed QDCT is used to develop and realize a quantum image compression technique. The developed compression technique performs a search to determine the most significant computed DCT coefficients and is derived from Grover's algorithm. It provides a generalization to the original search algorithm by utilizing two oracle operators to solve the complex unstructured search problem rather than a single one. Thus, the proposed QDCT algorithm can simultaneously calculate the DCT coefficients and identify the significant DCT coefficients through applying two oracles. The comparison of the introduced QDCT with Grover's algorithm also indicates that the QDCT algorithm is more efficient. This can be attributed to performing a rotation on the subspace rather than on the global space in Grover's algorithm. In addition, the presented quantum 1D - and 2D - DCT have reduced complexities compared to the classical algorithms which are O(N) and O(N), respectively. Therefore, the presented QDCT and compression algorithm can be applied efficiently to accomplish various transform-based quantum signal and image processing tasks.
A survey of multiple types of text summarization with their satellite contents based on swarm intelligence optimization algorithms

Due to the tremendous increment of data on the web, extracting the most important data as a conceptual brief would be valuable for certain users. Therefore, there is a massive enthusiasm concerning the generation of automatic text summary frameworks to constitute abstracts automatically from the text, web, and social network messages associated with their satellite content. This survey highlights, for the first time, how the swarm intelligence (SI) optimization techniques are performed to solve the text summarization task efficiently. Additionally, a convincing justification of why SI, especially Ant Colony Optimization (ACO), has been presented. Unfortunately, three types of text summarization tasks using SI indicate bit utilizing in the literature when contrasted with the other summarization techniques as machine learning and genetic algorithms, in spite of the fact that there are seriously promising outcomes of the SI methods. On the other hand, it has been noticed that the summarization task with multiple types has not been formalized as a multi-objective optimization (MOO) task before, despite that there are many objectives which can be considered. Moreover, the SI was not employed before to support the real-time summary approaches. Thus, a new model has been proposed to be adequate for achieving many objectives and to satisfy the real-time needs. Eventually, this study will enthuse researchers to further consider the various types of SI when solving the summarization tasks, particularly, in the short text summarization (STS) field.