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The automatic price evaluation is an important and challenging issue for the used mobile phone recycling. However, due to complicated relationships between attributes and price as well as insufficient sample data, current approaches cannot achieve accurate price evaluation for the used mobile phones, which significantly reduce the efficiency for the used mobile phone recycling. To this end, a tensor based approach is proposed in this paper, which can achieve accurate and efficient price evaluation for used mobile phones. In the proposed approach, first, the mutual information based attributes selection mechanism and the boxplot based sample selection mechanism are employed to select the most relevant attributes and suitable price samples from used mobile phone dataset. Then, a tensor model is constructed to establish multi-dimensional relationships between selected attributes and prices of used mobile phones. Finally, the missing values in the constructed tensor model is completed through the gradient descent and CANDECOMP/PARAFAC decomposition algorithms. The completed tensor model can be directly used for the price evaluation of used mobile phones based on their attributes without further calculation. Based on the real price dataset of used mobile phones, the experiments indicate that the proposed approach outperforms most of current approaches for the price evaluation for the used mobile phones. • Mutual information is used to select the most relevant attributes. • Boxplot is used to select suitable price samples. • Tensor model is used to establish relationships between attributes and prices. • CP decomposition based algorithm is used to complete missing values in tensor model. • The completed tensor model can be directly used for the price evaluation. [ABSTRACT FROM AUTHOR]

• A framework for audio restoration that can be used in three different use cases. • Creation of a training dataset that covers the two use cases by applying techniques of adding noise and eliminating audio. • Training the Deep Learning model to solve the use case of audios with background noise. • Training the Deep Learning model to solve the use case of audios with loss of information, e.g. calls with interferences. • An exhaustive evaluation using objective and subjective methods have been conducted. People communicate daily with their mobile phones and in some cases, the quality of the communication may be vital. Thus, there is a clear interest in improving the quality of communication in cases of low signal or interferences. This paper shows how deep learning techniques are used to restore audio files that simulate situations of background noise and loss of signal. Its main distinguishing feature is the direct use of the waveform instead of a spectrogram representation which lets the model be adapted to real-time communications or broadcasting. The results show that our proposal improves performance compared to Wave-U-Net. After restoring the audio, the difference between the original and the restored audio is, on average, less than 2%. In addition, a subjective test was carried out with 113 people who detected a significant improvement in the restored audio compared to the damaged one. [ABSTRACT FROM AUTHOR]

Non-contact identification of cattle is the basis for cattle management, insurance claims, and traceability of milk sources and meat products. In order to lower the threshold of deep learning technology in practical application and improve the recognition accuracy, this paper uses the localization and classification paradigm, fills the image and proposes the LKA (Locate Key Area) module, which further improves the identification accuracy of cattle without using any labeling information. To avoid the impact of different collection devices, we use three brands of mobile phones and two cameras to collect data. Due to the fact that CNN features do not have scale invariance, the image is filled as a square, and the filling value is discussed. Finally, (0,0,0) is selected for filling. In order to reduce the labeling of new data, the face, torso, and body of the cattle are first detected, and then automatically trimmed to build a classification dataset. In order to further improve the accuracy of classification, LKA module is proposed. This module locates key areas based on the results of class activation map and uses shallow features for monitoring. Thereby completing fine-grained feature extraction without supervision information. Finally, the features of different parts are adjusted for fusion recognition. Experiments were conducted on the head, torso, and whole of cattle. Based on the ResNet50 network architecture, after filling the image into a square, the recognition accuracy has been improved by 0.30%, 0.21%, and 0.43%, respectively. After further adding LKA modules, the accuracy rates were improved by 0.59%, 0.59%, and 0.45%, respectively. In the fusion experiment, ResNet50_LKA and MobileNetV3_LKA achieves 100% accuracy. Experiments have realized dataset construction and fine-grained feature extraction without labeled information, and achieved high recognition accuracy, proving the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Product defects are a widespread concern for manufacturers when conducting quality and customer relationship management. Prior approaches addressed many electronic products however cell phones are still unexplored. Moreover, prior work mainly focused on the lexicon, probabilistic graphic, failure mode, and effect analysis models but the utilization of word embeddings and language models are not explored. State-of-the-art contextual word embeddings and language models generate automated features and both are very effective in capturing the semantic and contextual dimensions of the text. This paper addresses these dimensions and proposes a novel approach that integrates word2vec and Bidirectional Encoder Representations from Transformers (BERT) models with smoke and domain-specific lexicon approaches to detect product defects for cell phones. Compared to existing studies, smoke and domain-specific approaches surpass the traditional sentiment methods. Moreover, BERT and word2vec presented the best performance with the logistic regression model by achieving an accuracy of 90% on training data and 84% on validation data. A case study in the Apple iPhone and Samsung cell phone industry proves the predominant performance of our approach and provides great potential in defect and customer relationship management. [ABSTRACT FROM AUTHOR]

Finding the accurate position of an eye is crucial for mobile iris recognition system in order to extract the iris region quickly and correctly. Unfortunately, this is very difficult to accomplish when a person is wearing eyeglasses because of the interference from the eyeglasses. This paper proposes an eye detection method that is robust to eyeglass interference in mobile environment. The proposed method comprises two stages: eye candidate generation and eye validation. In the eye candidate generation stage, multi-scale window masks consisting of 2 × 3 subblocks are used to generate all image blocks possibly containing an eye image. In the ensuing eye validation stage, two methods are employed to determine which blocks actually contain true eye images and locate their precise positions as well: the first method searches for the glint of an NIR illuminator on the pupil region. If this first method fails, the next method computes the intensity difference between the assumed pupil and its surrounding region using multi-scale 3 × 3 window masks. Experimental results show that the proposed method detects the eye position more accurately and quickly than competing methods in the presence of interference from eyeglass frames. [ABSTRACT FROM AUTHOR]

• Developing a novel optimization model for a closed-loop supply chain network. • Applying the model for a cellphone network in the Peel region, Ontario, Canada. • Extending the model to a multi-objective one by adding two new objectives. • Using a fuzzy QFD method to rank the qualitative factors in the third objective. • Finding the efficient solutions by the weighted-sums and ε -constraint methods. A Closed-Loop Supply Chain Network (CLSCN) includes forward and reverse supply chains. In this research, a dynamic cellphone closed-loop supply chain is considered, which contains multiple plants, retailers, demand markets, and collection centers. The returned cellphones are significant parts of electronic waste, and there is an increasing demand for this product due to the technology advancement and population growth. In this investigation, a Mixed-Integer Linear Programming (MILP) model is proposed and solved with the GAMS software to configure a CLSCN in the Peel Region in Ontario, Canada. The results of the optimization model show which facilities should be open for minimization of the total cost of the network. Then, the model is extended to a multi-objective model by adding two objectives considering CO 2 emissions and the quality aspects of the CLSCN. To handle the qualitative factors, a fuzzy Quality Function Deployment (QFD) model is discussed in this paper. Then, weighted-sums and ε -constraint methods are utilized to detect efficient solutions for the CLSCN. Finally, the results are discussed and the conclusions are provided. Based on the results, the weights of the objective functions have significant effects on both the selection of the facilities and the flows of the products between them. [ABSTRACT FROM AUTHOR]

Abstract: A 2010 survey (Nielsen) showed that 22.7% of the time spent on the Internet is on a social network. Moreover, there is an increasing demand to access social networks by mobile phones, i.e., around 30% globally. Social networking has become a reality, and it generates an incredible amount of information that is sometimes difficult for users to process, especially from mobile phones. Several links, activities, and recommendations are proposed by networked friends every hour, which together are nearly impossible to manage. There is a need to filter and make accessible such information to users, which is the motivation behind developing a mobile recommender that exploits social network information. Thus, in this paper, we propose the design and the implementation of a SOcial Mobile Activity Recommender (SOMAR) that can integrate Facebook social network mobile data and sensor data to propose activities to the user (e.g., concert or computer science seminar). The recommendations are completely calculated in situ in the mobile device with an embedded data mining component. We analyze how to compute and update the social graph in case of changes in social relationships or user context. The paper also presents a case study to analyze the performance of the method. [Copyright &y& Elsevier]

Abstract: Mobile devices can now handle a great deal of information thanks to the convergence of diverse functionalities. Mobile environments have already shown great potential in terms of providing customized services to users because they can record meaningful and private information continually for long periods of time. Until now, most of this information has been generally ignored because of the limitations of mobile devices in terms of power, memory capacity and speed. In this paper, we propose a novel method that efficiently infers landmarks for users to overcome these problems. This method uses an effective probabilistic Bayesian network model for analyzing various kinds of log data in mobile environments, which were modularized in this paper to decrease complexity. We also present a cooperative inference method, and the proposed methods were evaluated with mobile log data generated and collected in the real world. [Copyright &y& Elsevier]

Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common. Therefore, forensic analysis of mobile device images takes on special importance. This paper explores the branch of forensic analysis which is based on the identification of the source, specifically on the grouping or clustering of images according to their source acquisition. In contrast with other state of the art techniques for source identification, hierarchical clustering does not involve a priori knowledge of the number of images or devices to be identified or training data for a future classification stage. That is, a grouping by classes with all the input images is performed. The proposal is based on the combination of hierarchical and flat clustering and the use of Sensor Pattern Noise (SPN). There has been a series of experiments which emulate similar situations to those that may occur in reality to test the robustness and reliability of the results of the technique. The results are satisfactory in all the experiments, obtaining high rates of success. [ABSTRACT FROM AUTHOR]

Abstract: With the widespread usage of mobile terminals, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which continues to be the main shopping mode. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users’ preferences by analyzing users’ positions, without requiring users’ explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows the new proposed mobile recommender system achieves much better user satisfaction than the benchmark method, without losing obvious recommendation performances. [Copyright &y& Elsevier]

Abstract: This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of a statistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy. [Copyright &y& Elsevier]

Abstract: Recommendation systems that provide appropriate solutions to users to reduce their decision complexity have become popular in the Internet world. Designing and evaluating such systems remain essential challenges to researchers and practitioners. Toward that end, a critical task is how to obtain user preferences. Mobile phones have become indispensable in everyday life, yet fierce market competition, characterized by rapid introductions of different models with novel designs and advanced features, have made consumers’ purchase decision making increasingly complex. As a well-established, multiple criteria decision technique, analytic hierarchy processing (AHP) provides an intuitive model of a hierarchical structure capable of supporting complex product comparisons and evaluations by consumers. In this paper, we illustrate the application of an AHP-based mechanism to develop a Web-based recommendation system and empirically evaluate the prototype by conducting a controlled experiment with 244 mobile phone users, focusing on both content and system satisfaction. Our evaluation includes benchmark systems built on rank-based analysis and an equal weight-based system as comparative baselines. Overall, the results suggest the viability and value of using AHP to construct effective recommendation systems. Subjects appear satisfied with the recommendations by the AHP-based system, though its relatively demanding input requirements may need mitigation and adequate interface designs. This study contributes to research and practice in recommender systems in general and helps develop mobile phone recommendation systems for online stores and consumers in particular. [ABSTRACT FROM AUTHOR]

Abstract: Personal context is the most significant information for providing contextualized mobile recommendation services at a certain time and place. However, it is very difficult for service providers to be aware of the personal contexts, because each person’s activities and preferences are very ambiguous and depending on numerous unknown factors. In order to deal with this problem, we have focused on discovering social relationships (e.g., family, friends, colleagues and so on) between people. We have assumed that the personal context of a certain person is interrelated with those of other people, and investigated how to employ his neighbor’s contexts, which possibly have a meaningful influence on his personal context. It indicates that we have to discover implicit social networks which express the contextual dependencies between people. Thereby, in this paper, we propose an interactive approach to build meaningful social networks by interacting with human experts. Given a certain social relation (e.g., isFatherOf), this proposed systems can evaluate a set of conditions (which are represented as propositional axioms) asserted from the human experts, and show them a social network resulted from data mining tools. More importantly, social network ontology has been exploited to consistently guide them by proving whether the conditions are logically verified, and to refine the discovered social networks. We expect these social network is applicable to generate context-based recommendation services. In this research project, we have applied the proposed system to discover the social networks between mobile users by collecting a dataset from about two millions of users. [Copyright &y& Elsevier]

Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to discover personal mobility patterns for helping systems provide personalized service in a wireless network. The classification and the duration of each location area visited by a mobile user are used as important attributes in representing the results. A data mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways. Experiments are also made to show the effect of the proposed algorithm. [Copyright &y& Elsevier]

This paper introduces an application of multidimensional scaling for marketing-mix modification of products at the maturity stage of product life cycle. The application is based upon the concept that marketers would benefit from mining the consumer behaviors to assist their marketing decisions. Using the preference map of multidimensional scaling method and influence-satisfaction matrix, the proposed methodology promotes marketers to analyze their current level of consumer satisfaction in the market and modify the marketing-mix based upon the current consumer preference and satisfaction. The proposed methodology adopts multidimensional preference (MDPREF) algorithm, which is designed to do multidimensional scaling of preference or evaluation data. While most applications with the multidimensional scaling have been to understand the position of a set of brands or products, the proposed methodology with MDPREF algorithm is to understand the similarities and dissimilarities among a set of marketing elements of a brand in consumer’s perception. A case example for the mobile phone category is provided. [Copyright &y& Elsevier]

Abstract: As mobile communication market grows rapidly, in Korea, competitive strategies in mobile phone manufacturers are crucial. However, the way the mobile phone manufacturers adopt a pricing policy is not flexible enough to maximize the profit under the competitive environment. In this paper, we derive a dynamic pricing model for considering the price change of product itself but also the relative price change of competing products. Scenario analysis is performed to find optimal pricing policy based on genetic algorithm. Our proposed model is expected to provide adaptable pricing policy in a competitive market for a mobile phone set. [Copyright &y& Elsevier]

Purpose of this paper: Online product recommendation mechanism (agents) are becoming increasingly available on websites to assist consumers with reducing information overload, provide advice in finding suitable products, and facilitate online consumer decision-making. Central of these services is consumers’ satisfaction with recommendation results. Traditional recommendation mechanism (TRM) is based content and/or collaborative filtering approach. However, the remaining problem concerning TRM is how to analyze the causal relationships between quantitative and qualitative factors, and investigate their impact on the central routes and peripheral routes through which both quantitative and qualitative factors can affect customer online shopping decisions. It is well known that qualitative factors are hard to codify yet they have a significant effect on a customer’s decision-making process in the form of causal relationships with quantitative factors. Thus, a new online recommendation mechanism is required that incorporates qualitative factors systematically with quantitative factors to analyze their combined influence on customers’ purchasing decision-making process. So, our study suggest that causal maps based recommendation mechanism (CMRM). Design/methodology/approach: ELM was applied to build hypotheses concerning how consumers’ decision satisfaction and online shopping behavior are affected by CMRM. Specifically, the performance of the proposed CMRM is analyzed empirically by garnering the experiment data from 250 qualified respondents who were asked to refer to the proposed CMRM before making purchasing decisions on mobile phones. Findings: Statistical results proved that the proposed CMRM could enhance consumers’ decision satisfaction, attitude towards the recommended products, as well as positive purchase intentions and actual purchase. Practical implications: CMRM can be easily implemented on the web, allowing target consumers to experience a real recommendation process. And, a wide variety of qualitative factors that seem crucial to most consumers can be pre-defined through a survey, and incorporated into causal maps. Thus, such causal maps will improve the personalization effect on the target consumer’s purchase intentions. [Copyright &y& Elsevier]

Abstract: In the present and next generation wireless networks, cellular system remains the major method of telecommunication infrastructure. Since the characteristic of the resource constraint, call admission control is required to address the limited resource problem in wireless network. The call dropping probability and call blocking probability are the major performance metrics for quality of service (QoS) in wireless network. Many call admission control mechanisms have been proposed in the literature to decrease connection dropping probability for handoffs and new call blocking probability in cellular communications. In this paper, we proposed an adaptive call admission control and bandwidth reservation scheme using fuzzy logic control concept to reduce the forced termination probability of multimedia handoffs. Meanwhile, we adopt particle swarm optimization (PSO) technique to adjust the parameters of the membership functions in the proposed fuzzy logic systems. The simulation results show that the proposed scheme can achieve satisfactory performance when performance metrics are measured in terms of the forced termination probability for the handoffs, the call blocking probability for the new connections and bandwidth utilization. [Copyright &y& Elsevier]

The recent trend of combining internet with cellular phone gives an important chance to mobile telecommunication industry. It is mainly due to the fact that the mobile internet service based on cellular phone is not only more profitable than usual calling service but also it creates new incomes. With this background, mobile telecommunication companies wish to predict the subscribers of mobile internet service from the existing cellular phone users. In this case classification models play important roles and the improvement of classification accuracy has significant target marketing effects due to its huge market size. One of the main efforts made to improve the classification accuracy is a combined classification. This paper proposes a new combined classifier based on both ensemble (bagging) and dynamic classifier selection schemes (DCS). The proposed method is applied to the call data of subscribers and its result confirms the superiority of our approach over bagging and DCS methods. [Copyright &y& Elsevier]

[Display omitted] • This is the first study using platform-offered aspects for spam detection. • This is also the first study to address Chinese reviews for aspect-oriented fraud labelling. • Cross-platform reviews are integrated for spam group clustering. • We achieve high performance in spam clustering. Currently, it is common to see untruthful opinions (also known as review spam, fraud or shilling attack) that resemble each other explicitly or implicitly across multiple business-to-customer websites or opinion sharing communities. Unfortunately, these fake recommendations can be fabricated by individual spammers or results of a manipulation campaign. Considering its severe harmfulness in influencing a product's reputation, grouped spam is more urgent to detect than individual fraud. Most state-of-the-art techniques of labeling grouped spam, e.g., Frequent Itemset Mining (FIM) or Latent Dirichlet Allocation (LDA), are completely unsupervised and incapable of making good use of officially recommended topics, such as appearance , speed and standby are three suggested aspects along a cell phone product in JD.com. In this paper, we introduce a novel approach based on aspect-oriented sentiment mining that can identify spam groups supported by nominated topics. Experiments show that our method is effective and outperforms several state-of-the-art solutions with statistical significance on two metrics, content duplication and burstiness of time. [ABSTRACT FROM AUTHOR]

• Today there are few researches related to identifying the source of a digital videos. • The work focuses on identifying the source of a digital videos from mobile devices. • Both the effect of compression and the details of the scene are studied in this work. • The dataset used comes from public repositories to perform source identification. The continuous growth of technologies like 4G or 5G has led to a massive use of mobile devices such as smartphones and tablets. This phenomenon, combined with the fact that people use mobile phones for a longer period of time, results in mobile phones becoming the main source of creation of visual information. However, its reliability as a true representation of reality cannot be taken for granted due to the constant increase in editing software. This makes it easier to alter original content without leaving a noticeable trace in the modification. Therefore, it is essential to introduce forensic analysis mechanisms to guarantee the authenticity or integrity of a certain digital video, particularly if it may be considered as evidence in legal proceedings. This paper explains the branch of multimedia forensic analysis that allows to determine the identification of the source of acquisition of a certain video by exploiting the unique traces left by the camera sensor of the mobile device in visual content. To do this, a technique that performs the identification of the source of acquisition of digital videos from mobile devices is presented. It involves 3 stages: (1) Extraction of the sensor fingerprint by applying the block-based technique. (2) Filtering the strong component of the PRNU signal to improve the quality of the sensor fingerprint. (3) Classification of digital videos in an open scenario, that is, where the forensic analyst does not need to have access to the device that recorded the video to find out the origin of the video. The main contribution of the proposed technique eliminates the details of the scene to improve the PRNU fingerprint. It should be noted that these techniques are applied to digital images and not to digital videos. In this work, we show that it is necessary to take this improvement into account to improve the identification of digital videos. Experimental results are also presented that support the validity of the techniques used and show promising results. [ABSTRACT FROM AUTHOR]