Information, Volume 9, Issue 8 (August 2018) – 22 articles

With the widespread availability of cell-phone recording devices, source cell-phone identification has become a hot topic in multimedia forensics. At present, the research on the source cell-phone identification in clean conditions has achieved good results, but that in noisy environments is not ideal. This paper proposes a novel source cell-phone identification system suitable for both clean and noisy environments using spectral distribution features of constant Q transform (CQT) domain and multi-scene training method. Based on the analysis, it is found that the identification difficulty lies in different models of cell-phones of the same brand, and their tiny differences are mainly in the middle and low frequency bands. Therefore, this paper extracts spectral distribution features from the CQT domain, which has a higher frequency resolution in the mid-low frequency. To evaluate the effectiveness of the proposed feature, four classification techniques of Support Vector Machine (SVM), Random Forest (RF), Convolutional Neural Network (CNN) and Recurrent Neuron Network-Long Short-Term Memory Neural Network (RNN-BLSTM) are used to identify the source recording device. Experimental results show that the features proposed in this paper have superior performance. Compared with Mel frequency cepstral coefficient (MFCC) and linear frequency cepstral coefficient (LFCC), it enhances the accuracy of cell-phones within the same brand, whether the speech to be tested comprises clean speech files or noisy speech files. In addition, the CNN classification effect is outstanding. In terms of models, the model is established by the multi-scene training method, which improves the distinguishing ability of the model in the noisy environment than single-scenario training method. The average accuracy rate in CNN for clean speech files on the CKC speech database (CKC-SD) and TIMIT Recaptured Database (TIMIT-RD) databases increased from 95.47% and 97.89% to 97.08% and 99.29%, respectively. For noisy speech files with seen noisy types and unseen noisy types, the performance was greatly improved, and most of the recognition rates exceeded 90%. Therefore, the source identification system in this paper is robust to noise. Full article

Prediction of electricity consumption plays critical roles in the economy. Accurate electricity consumption forecasting is essential for policy makers to formulate electricity supply policies. However, limited data and variables generally cannot provide sufficient information to gain satisfactory prediction accuracy. To address this problem, we propose a novel improved grey forecasting model, which combines data transformation for the original data sequence and combination interpolation optimization of the background value of the GM(1,1) model, and is therefore named DCOGM(1,1). To evaluate the simulation and prediction performance of DCOGM(1,1), two case studies are carried out. In addition, the results show that DCOGM(1,1) outperforms most existing improved grey models in terms of forecasting accuracy. Finally, DCOGM(1,1) is employed to predict the total electricity consumption of Shanghai City in China from 2017 to 2021. In addition, the results suggest that DCOGM(1,1) performs well compared with the traditional GM(1,1) model and other grey modification models in this context and Shanghai’s electricity consumption will increase stably in the following five years. In summary, DCOGM(1,1) proposed in our study has competent exploration and exploitation ability, and could be utilized as an effective and promising tool for short-term planning for other forecasting issues with limited source data as well. Full article

A microblog is a new type of social media for information publishing, acquiring, and spreading. Finding the significant topics of a microblog is necessary for popularity tracing and public opinion following. This paper puts forward a method to detect topics from Chinese microblogs. Since traditional methods showed low performance on a short text from a microblog, we put forward a topic detection method based on the semantic description of the microblog post. The semantic expansion of the post supplies more information and clues for topic detection. First, semantic features are extracted from a microblog post. Second, the semantic features are expanded according to a thesaurus. Here TongYiCi CiLin is used as the lexical resource to find words with the same meaning. To overcome the polysemy problem, several semantic expansion strategies based on part-of-speech are introduced and compared. Third, an approach to detect topics based on semantic descriptions and an improved incremental clustering algorithm is introduced. A dataset from Sina Weibo is employed to evaluate our method. Experimental results show that our method can bring about better results both for post clustering and topic detection in Chinese microblogs. We also found that the semantic expansion of nouns is far more efficient than for other parts of speech. The potential mechanism of the phenomenon is also analyzed and discussed. Full article

Multivariate time series data, which comprise a set of ordered observations for multiple variables, are pervasively generated in weather conditions, traffic, financial stocks, etc. Therefore, it is of great significance to analyze the correlation between multiple time series. Financial stocks generate a significant amount of multivariate time series data that can be used to build networks that reflect market behavior. However, traditional commercial complex networks cannot fully utilize the multiple attributes of stocks and redundant filter relationships and reveal a more authentic financial stock market. We propose a fusion similarity of multiple time series and construct a threshold network with similarity. Furthermore, we define the connectivity efficiency to choose the best threshold, establishing a high connectivity efficiency network with the optimal network threshold. By searching the central node in the threshold network, we have found that the network center nodes constructed by our proposed method have a more comprehensive industry coverage than the traditional techniques to build the systems, and this also proves the superiority of this method. Full article

For multiple attribute decision making, ranking and information aggregation problems are increasingly receiving attention. In a normal neutrosophic number, the ranking method does not satisfy the ranking principle. Moreover, the proposed operators do not take into account the correlation between any aggregation arguments. In order to overcome the deficiencies of the existing ranking method, based on the nonnegative normal neutrosophic number, this paper redefines the score function, the accuracy function, and partial operational laws. Considering the correlation between any aggregation arguments, the dual generalized nonnegative normal neutrosophic weighted Bonferroni mean operator and dual generalized nonnegative normal neutrosophic weighted geometric Bonferroni mean operator were investigated, and their properties are presented. Here, these two operators are applied to deal with a multiple attribute decision making problem. Example results show that the proposed method is effective and superior. Full article

Geographical location and mobile phone numbers are important parts of user privacy and lots of studies have been working on the privacy leakage problems of these two aspects. However, no researchers have ever studied the security problems that can be caused by the interaction between them. We show a new form of network attack in this paper by making full use of the relationship between them; namely, we try to break a user’s mobile phone number with the aid of a user’s geographical location that has been broken. We study the phenomenon of exposing a user’s geographical location and parts of their phone number that exist in a series of popular software products, and the possibility of the user’s mobile phone number to be broken is discussed. We choose one of the software (the largest entertainment webcast platform in China—YY) as the research object. First, taking advantage of a series of security vulnerabilities that exist in YY, a user’s accurate geographical location is broken by the trilateration localization algorithm. Then, their mobile phone number attribution can be inferred according to their geographical location. Next, a mobile phone number test set is constructed according to the mobile phone segment allocation made by the three carriers (telecommunication operators) and the exposed parts of the user’s phone number. Finally, a brute-force method is used to break the user’s mobile phone number. The great effect of a user’s geographical location on breaking a mobile phone number is proved by experiments, and security precaution suggestions are given at the end of the paper. Full article

Online health communities (OHCs) should utilize health-care knowledge for enhancing online patient support. To examine the use of existing OHCs to identify the challenges and strategies of enhancing online patients’ decision-making support, we conducted a descriptive study to evaluate the information availability, user availability and knowledge usability in 100 carefully-selected health-related websites. On the basis of criteria for effective OHCs, we used three evaluation instruments for health-care professionals to review and score the websites. Questionnaire results were examined from the perspective of information, user and knowledge support. Results corroborate that over 80% of the websites facilitate effective social functions, whereas only 33% provide health-care decision-making services to online patients. Approximately 46% of them satisfy four or five effective OHCs’ criteria. Three of them only offer the functions of patients’ charts and journals to support health data management. Although the existing OHCs are facilitated with good social interaction and support, only a few can assist patients in making effective health-care decisions, not to mention properly using health-care knowledge support. Full article

This paper is focused on aspect term extraction in aspect-based sentiment analysis (ABSA), which is one of the hot spots in natural language processing (NLP). This paper proposes MFE-CRF that introduces Multi-Feature Embedding (MFE) clustering based on the Conditional Random Field (CRF) model to improve the effect of aspect term extraction in ABSA. First, Multi-Feature Embedding (MFE) is proposed to improve the text representation and capture more semantic information from text. Then the authors use kmeans++ algorithm to obtain MFE and word clustering to enrich the position features of CRF. Finally, the clustering classes of MFE and word embedding are set as the additional position features to train the model of CRF for aspect term extraction. The experiments on SemEval datasets validate the effectiveness of this model. The results of different models indicate that MFE-CRF can greatly improve the Recall rate of CRF model. Additionally, the Precision rate also is increased obviously when the semantics of text is complex. Full article

Governments at all levels have a mandate to provide services, protect society, and make the economy prosper. While this is a long-term goal, citizens are now expecting greater and faster delivery of services from government. This paper presents a systematic literature review of service digitization carried out by the governments of several countries, which was motivated by the lack of primary studies in the literature related to the identification of the processes and methodologies adopted by these governments and private companies to provide their services to the citizen. This work also contributes to the identification of best practices, technologies and tools used for the provision and evaluation of digitized services provided and how governments are evaluating the gains from digitization. These results of this systematic literature review serve as inputs to guide current and future research of the Brazilian Government in the construction of a digital platform for the provision of its services directed to the citizen, seeking to analyze their needs and improving the services currently provided. Full article

To comprehensively describe uncertain/interval linguistic arguments and confident linguistic arguments in the decision making process by a linguistic form, this study first presents the concept of a single-valued linguistic neutrosophic interval linguistic number (SVLN-ILN), which is comprehensively composed of its uncertain/interval linguistic number (determinate linguistic argument part) and its single-valued linguistic neutrosophic number (confident linguistic argument part), and its basic operations. Then, the score function of SVLN-ILN based on the attitude index and confident degree/level is presented for ranking SVLN-ILNs. After that, SVLN-ILN weighted arithmetic averaging (SVLN-ILNWAA) and SVLN-ILN weighted geometric averaging (SVLN-ILNWGA) operators are proposed to aggregate SVLN-ILN information and their properties are investigated. Further, a multi-attribute decision-making (MADM) method based on the proposed SVLN-ILNWAA or SVLN-ILNWGA operator and the score function is established under consideration of decision makers’ preference attitudes (pessimist, moderate, and optimist). Lastly, an actual example is given to show the applicability of the established MADM approach with decision makers’ attitudes. Full article

This article presents some pressing issues on roboethics, which lie at the frontier between roboethics and information ethics. It relates them to the well-established field of marketing ethics, stressing two main points. First, that human attention and willpower is limited and susceptible to be exploited. Second, that the possibility of using consumer profiles considerably increases the possibility of manipulation. It presents the interactions with robots as a particularly intense setting, in which the humanlike presence and the possibility of tailoring communications to the profile of the human target can be especially problematic. The paper concludes with some guidelines that could be useful in limiting the potentially harmful effects of human–robot interactions in the context of information ethics. These guidelines focus on the need for transparency and the establishment of limits, especially for products and services and vulnerable collectives, as well as supporting a healthy attention and willpower. Full article

To improve the invisibility and robustness of quantization-based image watermarking algorithms, we developed an improved quantization image watermarking method based on the wavelet transform and normalization strategy used in this study. In the process of watermark encoding, the sorting strategy of wavelet coefficients is used to calculate the quantization step size. Its robustness lies in the normalization-based watermark embedding and the control of its amount of modification on each wavelet coefficient by utilizing the proper quantization parameter in a high entropy image region. In watermark detection, the original unmarked image is not required, and the probability of false alarms and the probability of detection are discussed through experimental simulation. Experimental results show the effectiveness of the proposed watermarking. Furthermore, the proposed method has stronger robustness than the alternative quantization-based watermarking algorithm. Full article

Scientifically and rationally analyzing the characteristics of land use evolution and exploring future trends in land use changes can provide the scientific reference basis for the rational development and utilization of regional land resources and sustainable economic development. In this paper, an improved hybrid artificial bee colony (ABC) algorithm based on the mutation of inferior solutions (MHABC) is introduced to combine with the cellular automata (CA) model to implement a new CA rule mining algorithm (MHABC-CA). To verify the capabilities of this algorithm, remote sensing data of three stages, 2005, 2010, and 2015, are adopted to dynamically simulate urban development of Dengzhou city in Henan province, China, using the MHABC-CA algorithm. The comprehensive validation and analysis of the simulation results are performed by two aspects of comparison, the visual features of urban land use types and the quantification analysis of simulation accuracy. Compared with a cellular automata model based on a particle swarm optimization (PSO-CA) algorithm, the experimental results demonstrate the effectiveness of the MHABC-CA algorithm in the prediction field of urban land use changes. Full article

With the rapid increase of wireless network traffic, the energy consumption of mobile network operators (MNOs) continues to increase, and the electricity bill has become an important part of the operating expenses of MNOs. The power grid as the power supplier of cellular networks is also developing rapidly. In this paper, we design two levels of bilateral matching algorithm to solve the energy management of micro-grid connected cellular networks. There are multiple retailers (sellers) and clusters (buyers) in our system model, which determine the transaction price and trading energy respectively and have a certain influence on the balance of energy supply and demand. Retailers make more profits by adjusting the price of electricity in matching algorithm M-1, depending on the energy they capture and the level of storage. At the same time, clusters adjust the electricity consumption through matching algorithm M-2 and power allocation on the basis of ensuring the quality of users’ service. Finally, the performance of the proposed scheme is evaluated by changing various parameters in the simulation. Full article

Distrust based recommender systems have drawn much more attention and became widely acceptable in recent years. Previous works have investigated using trust information to establish better models for rating prediction, but there is a lack of methods using distrust relations to derive more accurate ranking-based models. In this article, we develop a novel model, named TNDBPR (Trust Neutral Distrust Bayesian Personalized Ranking), which simultaneously leverages trust, distrust, and neutral relations for item ranking. The experimental results on Epinions dataset suggest that TNDBPR by leveraging trust and distrust relations can substantially increase various performance evaluations including $F1$ score, $AUC$, $Precision$, $Recall$, and $NDCG$. Full article

Toby Walsh in “The Singularity May Never Be Near” gives six arguments to support his point of view that technological singularity may happen, but that it is unlikely. In this paper, we provide analysis of each one of his arguments and arrive at similar conclusions, but with more weight given to the “likely to happen” prediction. Full article

In traditional databases, the entity resolution problem (which is also known as deduplication) refers to the task of mapping multiple manifestations of virtual objects to their corresponding real-world entities. When addressing this problem, in both theory and practice, it is widely assumed that such sets of virtual objects appear as the result of clerical errors, transliterations, missing or updated attributes, abbreviations, and so forth. In this paper, we address this problem under the assumption that this situation is caused by malicious actors operating in domains in which they do not wish to be identified, such as hacker forums and markets in which the participants are motivated to remain semi-anonymous (though they wish to keep their true identities secret, they find it useful for customers to identify their products and services). We are therefore in the presence of a different, and even more challenging, problem that we refer to as adversarial deduplication. In this paper, we study this problem via examples that arise from real-world data on malicious hacker forums and markets arising from collaborations with a cyber threat intelligence company focusing on understanding this kind of behavior. We argue that it is very difficult—if not impossible—to find ground truth data on which to build solutions to this problem, and develop a set of preliminary experiments based on training machine learning classifiers that leverage text analysis to detect potential cases of duplicate entities. Our results are encouraging as a first step towards building tools that human analysts can use to enhance their capabilities towards fighting cyber threats. Full article

A linguistic cubic variable (LCV) is comprised of interval linguistic variable and single-valued linguistic variable. An LCV contains decision-makers’ uncertain and certain linguistic judgments simultaneously. The advantage of the Dombi operators contains flexibility due to its changeable operational parameter. Although the Dombi operations have been extended to many studies to solve decision-making problems; the Dombi operations are not used for linguistic cubic variables (LCVs) so far. Hence, the Dombi operations of LCVs are firstly presented in this paper. A linguistic cubic variable Dombi weighted arithmetic average (LCVDWAA) operator and a linguistic cubic variable Dombi weighted geometric average (LCVDWGA) operator are proposed to aggregate LCVs. Then a multiple attribute decision making (MADM) method is developed in LCV setting on the basis of LCVDWAA and LCVDWGA operators. Finally, two illustrative examples about the optimal choice problems demonstrate the validity and the application of this method. Full article

In this article, the concept of neutrosophic homeomorphism and neutrosophic $\alpha \psi$ homeomorphism in neutrosophic topological spaces is introduced. Further, the work is extended as neutrosophic $\alpha {\psi }^{\ast }$ homeomorphism, neutrosophic $\alpha \psi$ open and closed mapping and neutrosophic $T_{\alpha \psi }$ space in neutrosophic topological spaces and establishes some of their related attributes. Full article

The success of the Silk Road has prompted the growth of many Dark Web marketplaces. This exponential growth has provided criminal enterprises with new outlets to sell illicit items. Thus, the Dark Web has generated great interest from academics and governments who have sought to unveil the identities of participants in these highly lucrative, yet illegal, marketplaces. Traditional Web scraping methodologies and investigative techniques have proven to be inept at unmasking these marketplace participants. This research provides an analytical framework for automating Dark Web scraping and analysis with free tools found on the World Wide Web. Using a case study marketplace, we successfully tested a Web crawler, developed using AppleScript, to retrieve the account information for thousands of vendors and their respective marketplace listings. This paper clearly details why AppleScript was the most viable and efficient method for scraping Dark Web marketplaces. The results from our case study validate the efficacy of our proposed analytical framework, which has relevance for academics studying this growing phenomenon and for investigators examining criminal activity on the Dark Web. Full article

The research identifies and substantiates the problem of quality deterioration in the sentiment classification of text collections identical in composition and characteristics, but staggered over time. It is shown that the quality of sentiment classification can drop up to 15% in terms of the F-measure over a year and a half. This paper presents three different approaches to improving text classification by sentiment in continuously-updated text collections in Russian: using a weighing scheme with linear computational complexity, adding lexicons of emotional vocabulary to the feature space and distributed word representation. All methods are compared, and it is shown which method is most applicable in certain cases. Experiments comparing the methods on sufficiently representative text collections are described. It is shown that suggested approaches could reduce the deterioration of sentiment classification results for collections staggered over time. Full article