Unknown Users Research Articles

SAMPARK: Secure and lightweight communication protocols for smart parking management

Many vehicles on the road create significant problems, like parking, traffic congestion, fuel/energy consumption, and so on. Specifically, finding an appropriate parking spot is challenging for vehicle users while moving. Renting private parking spots is worthwhile using information and communications technology to solve parking challenges and offers mutual benefits to vehicle users and parking spot owners. However, security and privacy are prime concerns, as unknown vehicle users use private parking spots. The existing parking schemes are vulnerable to important security and privacy attributes and also inefficient due to high computational costs.We propose secure and lightweight communication protocols for smart parking management (named as SAMPARK) using lightweight cryptographic primitives, (i) to send available parking information and (ii) to reserve a parking spot ahead of time. Security proofs are discussed based on the random oracle model to confirm the security robustness of SAMPARK, considering vital vehicular communication attacks for security analysis. The test-bed results (on Raspberry Pi 3B+) show that the SAMPARK is relatively efficient in computation time, storage cost, and communication overhead.

Enhanced Clustering Technique for Efficient Identification of Independent Groups in Social Networks

The main aim of this paper is to develop a new approach for identifying independent groups among users communicating in social networks using social media applications at any instant. Grouping of users as independent clusters is of dynamic nature as communication between known and unknown users can happen randomly at any point of time. It is becoming inherent to identify the groups, where the members of the group have strong relationship who communicate frequently and consistently via social media applications. Louvain’s algorithm will identify the clusters in the community detection process but keeps the lightweight nodes in the original groups without making them into one group by considering the dependence relations. The concept of Bernstein conditions is enhanced and applied to identify the dependency among the users of social networks by formulating equivalence relations, which adhere to the properties of Reflexivity, Symmetricity and Transitivity. Then, the equivalence classes are identified which denote the individual groups of clusters where the users of one cluster are loosely coupled with the users of any other cluster but tightly coupled among the users of the same group. The strength of relationship among the users within the same and different clusters is identified with respect to the quantum of messages being propagated among the users using Louvain’s algorithm and the results of equivalence class approach are compared using the same set of communication sequences to show the relation dependency among the members in various clusters.

Active learning based on computer vision and human–robot interaction for the user profiling and behavior personalization of an autonomous social robot

Social robots coexist with humans in situations where they have to exhibit proper communication skills. Since users may have different features and communicative procedures, personalizing human–robot interactions is essential for the success of these interactions. This manuscript presents Active Learning based on computer vision and human–robot interaction for user recognition and profiling to personalize robot behavior. The system identifies people using Intel-face-detection-retail-004 and FaceNet for face recognition and obtains users’ information through interaction. The system aims to improve human–robot interaction by (i) using online learning to allow the robot to identify the users and (ii) retrieving users’ information to fill out their profiles and adapt the robot’s behavior. Since user information is necessary for adapting the robot for each interaction, we hypothesized that users would consider creating their profile by interacting with the robot more entertaining and easier than taking a survey. We validated our hypothesis with three scenarios: the participants completed their profiles using an online survey, by interacting with a dull robot, or with a cheerful robot. The results show that participants gave the cheerful robot a higher usability score (82.14/100 points), and they were more entertained while creating their profiles with the cheerful robot than in the other scenarios. Statistically significant differences in the usability were found between the scenarios using the robot and the scenario that involved the online survey. Finally, we show two scenarios in which the robot interacts with a known user and an unknown user to demonstrate how it adapts to the situation.

Fair Incentive Mechanism With Imperfect Quality in Privacy-Preserving Crowdsensing

Mobile crowdsensing (MCS) enables a platform to recruit users to collectively perform sensing tasks from requesters. In order to maximize the completion qualities of tasks, an incentive mechanism should be well designed for the platform to incentivize high-quality users’ participation. The existing works largely adopt the Stackelberg game to model the strategic interactions in the incentive mechanism. However, there are practical issues that are less investigated in the context of the Stackelberg-based incentive mechanism. First, the platform has no knowledge about users’ sensing qualities beforehand due to their private information. Second, the platform needs users’ continuous participation in the long run, which results in fairness requirements. Third, it is also crucial to protect users’ privacy due to the potential privacy leakage concerns (e.g., sensing qualities) after completing tasks. In this article, we jointly address these issues and propose the three-stage Stackelberg-based incentive mechanism for the platform to recruit participants. In detail, we leverage combinatorial volatile multiarmed bandits (CVMABs) to elicit unknown users’ sensing qualities. We use the drift-plus-penalty (DPP) technique in Lyapunov optimization to handle the fairness requirements. We blur the quality feedback with tunable Laplacian noise such that the incentive mechanism protects locally differential privacy (LDP). Finally, we carry out experiments to evaluate our incentive mechanism. The numerical results show that our incentive mechanism achieves <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sublinear</i> regret performance to learn unknown quality with fairness and privacy guarantee.

An Intelligent Secure Monitoring Phase in Blockchain Framework for Large Transaction

Blockchain is the key concept for security purposes for digital applications. But, in some cases, the effectiveness of the malicious behavior has degraded the security function of the blockchain. So, to enrich the blockchain process prediction and to neglect the malicious event from the data broadcasting medium is very important. So, the current research article intends to develop an efficient monitoring strategy based on incorporating deep features. Hence, the designed paradigm is termed as Lion-based Convolutional Neural Model (LbCNM) with serpent encryption. Before performing the encryption process, the novel LbCNM parameters have been activated to monitor the data process channel in the blockchain environment. Here, the malicious behaviors were estimated by incorporating the known and unknown user behavior in the Lion fitness model. During the execution, the fitness formulation of Lion is acted in the classification layer of the convolutional model. Once the present malicious characteristics have been detected, it is neglected from the data broadcasting channel. Hereafter, the transactional data has been encrypted and stored in the specific cloud. The planned strategy is verified in the python platform. The successful performance of the LbCNM with serpent has been analyzed with some key parameters like confidential rate, accuracy, data overhead, and processing time.

Stochastic one-way carsharing systems with dynamic relocation incentives through preference learning

The development of carsharing services is expected to achieve greater resource efficiency and provide a sustainable solution for future mobility systems. However, operators inevitably face the imbalance between demand and supply in one-way carsharing systems (CSSs). Also, it is challenging for them to make quick and efficient operational decisions when both travel time and trip requests are uncertain. This study leverages historical and online data and proposes a learning-based methodology to quickly make real-time decisions for CSSs, including vehicle assignment, relocation, and user incentive decisions. Compared to the literature in approximate dynamic programming (ADP), which mostly focuses on uncertainty in trip requests, we propose an offline–online ADP approach to consider the temporal and spatial uncertainty in both trip requests and travel time. To tackle our high-dimensional problem, we integrate an online look-ahead policy into the offline value function approximation (VFA) policy to produce a computational tractable high-quality dynamic fleet management policy. Furthermore, a user-based relocation strategy is investigated to rebalance the fleet distribution to meet the demand better. Specifically, we aim to solve the optimal incentives the operator could offer to users to relocate cars, while user preferences towards relocation incentives are generally unknown in practice. We further enhance our anticipatory policy by developing an online module via Bayesian learning that learns the preference model on the fly using users’ revealed preference data collected online. The numerical experiments in Singapore demonstrate our offline–online ADP approach improves the solution quality and significantly compared to offline VFA policy. The results also confirm the importance of incorporating uncertainty in travel time. The benefits of the user-based relocation scheme using the Bayesian learning method using online data to enhance anticipatory decisions and learn unknown user preferences are also illustrated. It quickly and efficiently learns the user preferences, which further reduces the relocation cost and increases the profit.

A novel trust recommendation model in online social networks using soft computing methods

SummarySocial network (OSN) is an emerging platform through which people can connect with their friends, relatives, and other like‐minded people. On the other hand, users' personal information might be misused because of other users' biased and malicious behavior. Establishing a trusted environment in social networks is one of the current research problems. Some of the research papers proposed to trust computational methods, but still, there is a lack of methods to handle biased recommendations and loss of trust accuracy towards the target user. In this article, to address these open issues, “a novel trust recommendation model in online social networks using soft computing methods (TRMSC)” is proposed for the Twitter social networks. Here direct and indirect trust is computed for known and unknown users, respectively. The direct trust of a user is computed using clustering methods based on his social activities (posts, retweets received, mentions received, listed count, and follower count) with other users. In the computation of indirect trust, the impact of biased recommendations is suppressed using the Dempster Shafer theory(DST) method, and loss of trust is minimized using trust transitive matrices. The performance of the proposed method is analyzed theoretically and experimentally. Time and space complexities are measured using asymptotic notations. In experimental results, TRMSC is evaluated for different network sizes and for target users at different distances (2 to 4‐hops), where it could perform better than existing methods.

Cooperative Activation and Caching Strategy for Low-Latency and Energy-Efficient Small-Cell Networks

Traffic load can be largely alleviated by content caching in densely deployed small-cell base stations (SBSs), which however require significant energy expenses for operation. In this letter, we investigate the cooperative activation and caching problem for dense small-cell networks with unknown user demands. A key issue is to make activation and caching decisions, which is proved to be NP-hard even if user demands are perfectly known. While existing works on joint optimization of activation and caching make decisions based on estimated user demands, we consider to learn activation and caching strategy in an online manner. We take into account the cooperation among SBSs. In particular, an active SBS that serves a user should share the observed rewards to the SBSs that are nearer to the user. In this way, an inactive SBS can keep learning the information of its surrounding users, and will become active again when its surrounding users have sufficient requests. The simulation results demonstrate the superior performance of the proposed strategy in terms of delay and energy consumption.

COME-UP: Computation Offloading in Mobile Edge Computing with LSTM Based User Direction Prediction

In mobile edge computing (MEC), mobile devices limited to computation and memory resources offload compute-intensive tasks to nearby edge servers. User movement causes frequent handovers in 5G urban networks. The resultant delays in task execution due to unknown user position and base station lead to increased energy consumption and resource wastage. The current MEC offloading solutions separate computation offloading from user mobility. For task offloading, techniques that predict the user’s future location do not consider user direction. We propose a framework termed COME-UP Computation Offloading in mobile edge computing with Long-short term memory (LSTM) based user direction prediction. The nature of the mobility data is nonlinear and leads to a time series prediction problem. The LSTM considers the previous mobility features, such as location, velocity, and direction, as input to a feed-forward mechanism to train the learning model and predict the next location. The proposed architecture also uses a fitness function to calculate priority weights for selecting an optimum edge server for task offloading based on latency, energy, and server load. The simulation results show that the latency and energy consumption of COME-UP are lower than the baseline techniques, while the edge server utilization is enhanced.

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Combining review-based collaborative filtering and matrix factorization: A solution to rating's sparsity problem

An important factor affecting the performance of collaborative filtering for recommendation systems is the sparsity of the rating matrix caused by insufficient rating data. Improving the recommendation model and introducing side information are two main research approaches to address the problem. We combine these two approaches and propose the Review-Based Matrix Factorization method in this paper. The method consists of two phases. The first phase is review-based collaborative filtering, where an item-topic rating matrix is constructed by the feature-level opinion mining of online review text. This rating matrix is used to derive item similarities, which can be used to infer unknown users' ratings of the items. The second phase consists of rating imputation, where we first fill some of the empty elements of the user-item rating matrix, then conduct matrix factorization to learn the latent user and item factors to generate recommendations. Experiments on two actual datasets show that our method improves the accuracy of recommendation compared with similar algorithms.

Exploratory study of the underutilization of CTSA module services.

The Clinical and Translational Science Award (CTSA) program aims to enhance the quality, efficiency, and impact of translation from discovery to interventions that improve human health. CTSA program hubs at medical research institutions across the United States develop and test innovative tools, methods, and processes, offering core resources and training for the clinical and translational research (CTR) workforce. Hubs have developed services across different domains, such as informatics and pilot studies, to provide ad hoc expertise and staffing for local research teams. Although these services can provide efficient, cost-effective ways to cover skills gaps and implement rigorous studies, three CTSAs of varying size found the majority of investigators were single domain service users, likely missing opportunities to further enhance their work. Through interviews with CTSA service users and a survey of CTSA service managers, this exploratory study aims to identify barriers to using services from multiple modules and solutions to overcome those barriers. Barriers include challenges in finding information about services, unclear or unknown user needs, and users' lack of funding to engage in services. More issues were identified for the largest CTSA. Although this study represents a small subset of CTSA hubs, we anticipate that our findings and proposed solutions will be relevant to the broader CTSA community. This study provides foundational information can use in their own efforts to increase service utilization and methods that can be used for more comprehensive studies that focus on explaining the relationship between CTSA features and rates of single versus cross-module service use.

The Model May Fit You: User-Generalized Cross-Modal Retrieval

In real-world applications, a cross-model retrieval model trained on multimodal instances without considering differences in data distributions among users, termed as user domain shift, usually cannot generalize well to unknown user domains. In this paper, we define a new task of user-generalized cross-modal retrieval, and propose a novel Meta-Learning Multimodal User Generalization (MLMUG) method to solve it. MLMUG simulates the user domain shift with meta-optimization, which aims to embed multimodal data effectively and generalize the cross-modal retrieval model to any unknown user domains. We design a cross-modal embedding network with a learnable meta covariant attention module to encode transferable knowledge among different user domains. A user-adaptive metaoptimization scheme is proposed to adaptively aggregate gradients and meta-gradients for fast and stable meta-optimization.We build two benchmarks for user-generalized cross-modal retrieval evaluation. Experiments on the proposed benchmarks validate the generalization of our method compared with several stateof-the-art methods.

User Recruitment Algorithm for Maximizing Quality under Limited Budget in Mobile Crowdsensing

In the mobile crowdsensing task assignment, under the premise that the data platform does not know the user’s perceived quality or cost value, how to establish a suitable user recruitment mechanism is the critical issue that this article needs to solve. It is necessary to learn the user’s perceived quality in the execution p. It also needs to try its best to ensure the efficiency and profit maximization of the mobile group intelligence perception platform. Therefore, this paper proposes a mobile crowdsensing user recruitment algorithm based on Combinatorial Multiarmed Bandit (CMAB) to solve the recruitment problem with known and unknown user costs. Firstly, the user recruitment process is modeled as a combined multiarm bandit model. Each rocker arm represents the selection of different users, and the income obtained represents the user’s perceived quality. Secondly, it proposes the upper confidence bound (UCB) algorithm, which updates the user’s perceptual quality according to the completion of the task. This algorithm sorts the users’ perceived quality values from high to low, then selects the most significant ratio of perceived quality to recruitment costs under the budget condition, assigns tasks, and updates their perceived quality. Finally, this paper introduces the regret value to measure the efficiency of the user recruitment algorithm and conducts many experimental simulations based on real data sets to verify the feasibility and effectiveness of the algorithm. The experimental results show that the recruitment algorithm with known user cost is close to the optimal algorithm, and the recruitment algorithm with unknown user cost is more than 75% of the optimal algorithm result, and the gap tends to decrease as the budget cost increases, compared with other comparisons. The algorithm is 25% higher, which proves that the proposed algorithm has good learning ability and can independently select high‐quality users to realize task assignments.

Deanonymization of users based on correlation analysis

The article discusses the issues of deanonymization of network users who hide their data in order to evade responsibility when committing malicious acts. Within the framework of this work, a user deanonymization algorithm has been developed based on traffic correlation analysis. The algorithm is based on comparing the statistical characteristics of traffic generated by an unknown user with the characteristics of traffic corresponding to known users. An illustrative example of the implementation of the algorithm, which confirms its effectiveness, is given.

Cache Placement Optimization in Mobile Edge Computing Networks With Unaware Environment—An Extended Multi-Armed Bandit Approach

Caching high-frequency reuse contents at the edge servers in the mobile edge computing (MEC) network omits the part of backhaul transmission and further releases the pressure of data traffic. However, how to efficiently decide the caching contents for edge servers is still an open problem, which refers to the cache capacity of edge servers, the popularity of each content, and the wireless channel quality during transmission. In this paper, we discuss the influence of unknown user density and popularity of content on the cache placement solution at the edge server. Specifically, towards the implementation of the cache placement solution in the practical network, there are two problems needing to be solved. First, the estimation of unknown users’ preference needs a huge amount of records of users’ previous requests. Second, the overlapping serving regions among edge servers cause the wrong estimation of users’ preference, which hinders the individual decision of caching placement. To address the first issue, we propose a learning-based solution to adaptively optimize the cache placement policy without any previous knowledge of the user density and the popularity of the contents. We develop the extended multi-armed bandit (Extended MAB), which combines the generalized global bandit (GGB) and Standard Multi-armed bandit (MAB), to iteratively estimate both a global parameter, i.e., the user density, and individual parameters, i.e., the popularity of each content. For the second problem, a multi-agent Extended MAB based solution is presented to avoid the mis-estimation of parameters and achieve the decentralized cache placement policy. The proposed solution determines the primary time slot and secondary time slot for each edge server. The edge servers estimate expected satisfied user number of caching a content with the overlap information and determine the cache placement solution. The proposed strategies are proven to achieve the bounded regret according to the mathematical analysis. Extensive simulations verify the optimality of the proposed strategies when comparing with baselines.

Satisfying user preferences in community-based local energy markets — Auction-based clearing approaches

The advancing energy transition is leading to a constantly increasing number of prosumers and active participants in the energy system. Local energy markets are considered a promising approach to coordinate those active participants efficiently, increase acceptance among the population, and decrease grid expansion costs. However, energy models and markets do not consider heterogeneous user preferences which are considered crucial for the progressing energy transition. Therefore, this paper proposes new auction-based local energy market models that consider user preferences and users’ willingness to pay a premium for heterogeneous energy qualities. In order to account for the unknown and stochastic user behavior in such a new market setting, we simulate and compare existing and newly developed auction-based clearing algorithms with an unbiased Monte Carlo method and evaluate whether they fulfill pre-defined key characteristics. Based on the results, we conclude and identify a clearing algorithm that verifiably satisfies user preferences, considers willingness to pay a premium, increases local coverage of electricity, maintains individual rationality, and computational tractability. The presented clearing algorithms enable new market designs that can help to increase acceptance and accelerate the expansion of renewable energies. All market models and simulations are publicly available in the open-source repository lemlab. Future research will validate the presented results in a field trial in Germany with 20 households.

Making friends on Facebook: Common group membership as a central decision rule

The present study examines the role of group identification and common group membership as a decision rule when accepting friendship requests on Facebook from unknown users. To look for motives regarding social capital increase, a methodically triangulated approach was chosen, combining qualitative methods (focus groups) and quantitative data in form of a description of the sample group (questionnaire). Because social desirability biases were expected during the data collection, a qualitative experiment was designed beforehand, giving the opportunity to ask people why they have done something and not how they would react under certain circumstances. A confederate created a fake profile (including average information, incognito pictures and some expectable noise on the profile page) into two online groups on Facebook. These groups are intended for students of a university in Eastern Germany (n = 119) and our confederate sent out friendship requests to 102 of those members, from which 84 were accepted. Subsequent focus group discussions and a quantitative questionnaire showed that common ground, especially common group membership, is a central decision rule for accepting friendship requests from unknown people. The matter of context is discussed when dealing with online phenomena like building social relationships and, of course, the ethical implications for the online experiment and the unavoidably deceitful process.

Spam message detection using Danger theory and Krill herd optimization

Due to proliferation of online posts and rise in the active social media users, fraudulent activities related with spam messages have taken a spike drift. Spam is an activity by which hackers use electronic messaging system to unsolicited messages in mass content to unknown users. It can be also taken as one of the major attraction of attackers in the form of short message service (SMS) messages. Spam messages can be categorized in different categories such as business opportunity spam, trending topic spam, banking services spam etc. These problems can be tackled by confirming to the actions taken by users towards these messages. There is an urgent explicit need of practical medium in order to assist the users against these spam messages. This paper proposes a novel SMS spam filtering model based on Danger theory of Artificial Immune System (AIS). Several feature extraction and selection techniques have been applied for optimizing the algorithm and claiming an admissible accuracy. This paper uses a biologically inspired algorithm named Krill herd Optimization (KHO) for the task of feature selection and various optimization functions like Quing function, Sumsquare function, Levy function etc. are applied for enhancing its performance. The Dendritic Cell Algorithm (DCA) is also incorporated with KHA as an added advantage towards achieving efficiency. Comparative results between Dendritic Cell Algorithm (DCA) with KHA and other spam filtering models have been shown in comparison with several state-of-the-art machine learning classifiers. The algorithms have been experimented by using varied optimization functions illustrated using visualization tools and results have been validated in the paper. The obtained results demonstrate an admissible accuracy of 96% that is calculated using different information retrieval metrics using recall, F-measure and precision.

Study of challenges faced by Enterprises using Security Information and Event Management (SIEM)

The field of information security plays an important role in education, IT, health domain, etc. Much research has been carried out in order to secure data in hardware, on the cloud, and during transmission over the network. A secure data transmission and securing the stored data is still taken as one of the concerned areas. Cloud-based SIEM is used nowadays, which is the art and science to secure the information of the organization. SIEM is Security Information and Event Management, which means securing the organization containing network devices and devices holding critical and sensitive information. In this paper, a survey is carried out to determine the gap in current security providers and areas that need attention. We take logs as input and send them to SIEM for analysis. Whether a SIEM is capable enough to determine the unknown threats and user behavior to identify insider threats. Also, terms such as EPS, False positive Rate, Mean Time to Resolution are used as compassion and aim to keep False positive rate and mean time resolution value low and EPS no restriction.