As an efficient classical machine learning classifier, the Softmax regression uses cross-entropy as the loss function. Therefore, it has high accuracy in classification. However, when there is inconsistency between the distribution of training samples and test samples, the performance of traditional Softmax regression models will degrade. A transfer discriminant Softmax regression model called Transfer Discriminant Softmax Regression with Weighted MMD (TDS-WMMD) is proposed in this paper. With this method, the Weighted Maximum Mean Divergence (WMMD) is introduced into the objective function to reduce the marginal distribution and conditional distribution between domains both locally and globally, realizing the cross domain transfer of knowledge. In addition, to further improve the classification performance of the model, Linear Discriminant Analysis (LDA) is added to the label iteration refinement process to improve the class separability of the designed method by keeping the same kind of samples together and the different kinds of samples repeling each other. Finally, after conducting classification experiments on several commonly used public transfer learning datasets, the results verify that the designed method can enhance the knowledge transfer ability of the Softmax regression model, and deliver higher classification performance compared with other current transfer learning classifiers.

To protect user privacy and establish self-information control rights, service providers must notify users of their privacy policies and obtain their consent in advance. The frameworks that impose these requirements are mandatory. Although originally designed to protect user privacy, obtaining user consent in advance has become a mere formality. These problems are induced by the gap between service providers' privacy policies, which prioritize the observance of laws and guidelines, and user expectations which are to easily understand how their data will be handled. To reduce this gap, we construct a tool supporting users in reading privacy policies in Japanese. We designed the tool to present users with separate unique expressions containing relevant information to improve the display format of the privacy policy and render it more comprehensive for Japanese users. To accurately extract the unique expressions from privacy policies, we created training data for machine learning for the constructed tool. The constructed tool provides a summary of privacy policies for users to help them understand the policies of interest. Subsequently, we assess the effectiveness of the constructed tool in experiments and follow-up questionnaires. Our findings reveal that the constructed tool enhances the users' subjective understanding of the services they read about and their awareness of the related risks. We expect that the developed tool will help users better understand the privacy policy content and and make educated decisions based on their understanding of how service providers intend to use their personal data.

In 2019, a new selection method, named fitness-distance balance (FDB), was proposed. FDB has been proved to have a significant effect on improving the search capability for evolutionary algorithms. But it still suffers from poor flexibility when encountering various optimization problems. To address this issue, we propose a functional weights-enhanced FDB (FW). These functional weights change the original weights in FDB from fixed values to randomly generated ones by a distribution function, thereby enabling the algorithm to select more suitable individuals during the search. As a case study, FW is incorporated into the spherical search algorithm. Experimental results based on various IEEE CEC2017 benchmark functions demonstrate the effectiveness of FW.

Tourist satisfaction plays a very important role in the development of local community tourism. For the development of tourist destinations in local communities, it is important to measure, maintain, and improve tourist destination royalties over the medium to long term. It has been proven that improving tourist satisfaction is a major factor in improving tourist destination royalties. Therefore, to improve tourist satisfaction in local communities, we identified multiple clusters of sightseeing spots and determined that the satisfaction of tourists can be increased based on these clusters of sightseeing spots. Our discovery flow can be summarized as follows. First, we extracted tourism keywords from guidebooks on sightseeing spots. We then constructed a complex network of tourists and sightseeing spots based on the data collected from experiments conducted in Kyoto. Next, we added the corresponding tourism keywords to each sightseeing spot. Finally, by analyzing network motifs, we successfully discovered multiple clusters of sightseeing spots that could be used to improve tourist satisfaction.

In this paper, we propose homomorphic encryption based device owner equality verification (HE-DOEV), a new method to verify whether the owners of two devices are the same. The proposed method is expected to be used for credential sharing among devices owned by the same user. Credential sharing is essential to improve the usability of devices with hardware-assisted trusted environments, such as a secure element (SE) and a trusted execution environment (TEE), for securely storing credentials such as private keys. In the HE-DOEV method, we assume that the owner of every device is associated with a public key infrastructure (PKI) certificate issued by an identity provider (IdP), where a PKI certificate is used to authenticate the owner of a device. In the HE-DOEV method, device owner equality is collaboratively verified by user devices and IdPs that issue PKI certificates to them. The HE-DOEV method verifies device owner equality under the condition where multiple IdPs can issue PKI certificates to user devices. In addition, it can verify the equality of device owners without disclosing to others any privacy-related information such as personally identifiable information and long-lived identifiers managed by an entity. The disclosure of privacy-related information is eliminated by using homomorphic encryption. We evaluated the processing performance of a server needed for an IdP in the HE-DOEV method. The evaluation showed that the HE-DOEV method can provide a DOEV service for 100 million users by using a small-scale system in terms of the number of servers.

A travel route recommendation service that recommends a sequence of points of interest for tourists traveling in an unfamiliar city is a very useful tool in the field of location-based social networks. Although there are many web services and mobile applications that can help tourists to plan their trips by providing information about sightseeing attractions, travel route recommendation services are still not widely applied. One reason could be that most of the previous studies that addressed this task were based on the orienteering problem model, which mainly focuses on the estimation of a user-location relation (for example, a user preference). This assumes that a user receives a reward by visiting a point of interest and the travel route is recommended by maximizing the total rewards from visiting those locations. However, a location-location relation, which we introduce as a transition pattern in this paper, implies useful information such as visiting order and can help to improve the quality of travel route recommendations. To this end, we propose a travel route recommendation method by combining location and transition knowledge, which assigns rewards for both locations and transitions.

This letter studies secure transmission design with finite alphabet input for cooperative jamming network under individual power constraint. By adopting the zero-force scheme, where the jamming signal is fully laid in the null space of the relay-destination channel, the problem of enhancing the achievable secrecy rate is decomposed into two independent subproblems: relay weights design and power control. We reveal that the problem of relay weights design is identical to the problem of minimizing the maximal equivalent source-eavesdropper channel gain, which can be transformed into a semi-definite programming (SDP) problem and thus is tackled using interior point method. Besides, the problem of power control is solved with the fundamental relation between mutual information and minimum mean square error (MMSE). Numerical results show that the proposed scheme achieves significant performance gains compared to the conventional Gaussian design.

Asymptotically good sequences of linear ramp secret sharing schemes have been intensively studied by Cramer et al. in terms of sequences of pairs of nested algebraic geometric codes [4]-[8], [10]. In those works the focus is on full privacy and full reconstruction. In this paper we analyze additional parameters describing the asymptotic behavior of partial information leakage and possibly also partial reconstruction giving a more complete picture of the access structure for sequences of linear ramp secret sharing schemes. Our study involves a detailed treatment of the (relative) generalized Hamming weights of the considered codes.

In this letter, we investigate the computation offloading problem in cloud based multi-robot systems, in which user weights, communication interference and cloud resource limitation are jointly considered. To minimize the system cost, two offloading selection and resource allocation algorithms are proposed. Numerical results show that the proposed algorithms both can greatly reduce the overall system cost, and the greedy selection based algorithm even achieves near-optimal performance.

This paper proposes a Preventive Start-time Optimization with no penalty (PSO-NP). PSO-NP determines a suitable set of Open Shortest Path First (OSPF) link weights at the network operation start time that can handle any link failure scenario preventively while considering both failure and non failure scenarios. Preventive Start-time Optimization (PSO) was designed to minimize the worst case congestion ratio (maximum link utilization over all the links in the network) in case of link failure. PSO considers all failure patterns to determine a link weight set that counters the worst case failure. Unfortunately, when there is no link failure, that link weight set leads to a higher congestion ratio than that of the conventional start-time optimization scheme. This penalty is perpetual and thus a burden especially in networks with few failures. In this work, we suppress that penalty while reducing the worst congestion ratio by considering both failure and non failure scenarios. Our proposed scheme, PSO-NP, is simple and effective in that regard. We expand PSO-NP into a Generalized Preventive Start-time Optimization (GPSO) to find a link weight set that balances both the penalty under no failure and the congestion ratio under the worst case failure. Simulation results show that PSO-NP achieves substantial congestion reduction for any failure case while suppressing the penalty in case of no failure in the network. In addition, GPSO as framework is effective in determining a suitable link weight set that considers the trade off between the penalty under non failure and the worst case congestion ratio reduction.

Cyclic codes are a subclass of linear codes and have applications in consumer electronics, data storage systems, and communication systems as they have efficient encoding and decoding algorithms. In this letter, a class of four-weight binary cyclic codes are presented. Their weight distributions of these cyclic codes are also settled.

Link duplication is widely used in Internet protocol networks to tackle the network congestion increase caused by link failure. Network congestion represents the highest link utilization over all the links in the network. Due to capital expenditure constraints, not every link can be duplicated to reduce congestion after a link fails. Giving priority to some selected links makes sense. Meanwhile, traffic routes are determined by link weights that are configured in advance. Therefore, choosing an appropriate set of link weights reduces the number of links that actually need to be duplicated in order to keep a manageable congestion under failure. A manageable congestion is a congestion under which Service Level Agreements can be met. The conventional scheme fixes link weights before determining links to duplicate. In this scheme, the fixed link weights are optimized to minimize the worst network congestion. The worst network congestion is the highest network congestion over all the single non-duplicated link failures. As the selection of links for protection depends on the fixed link weights, some suitable protection patterns, which are not considered with other possible link weights, might be skipped leading to overprotection. The paper proposes a scheme that considers multiple protection scenarios before optimizing link weights in order to reduce the overall number of protected links. Simulation results show that the proposed scheme uses fewer link protections compared to the conventional scheme.

The energy consumption of the Internet has a huge impact on the world economy and it is likely to increase every year. In present backbone networks, pairs of nodes are connected by “bundles” of multiple physical cables that form one logical link and energy saving can be achieved by shutting down unused network resources. The hose model can support traffic demand variations among node pairs in different time periods because it accommodates multiple traffic matrices unlike the pipe model which supports only one traffic matrix. This paper proposes an OSPF (Open Shortest Path First) link weight optimization scheme to reduce the network resources used for the hose model considering single link failures. The proposed scheme employs a heuristic algorithm based on simulated annealing to determine a suitable set of link weights to reduce the worst-case total network resources used, and considering any single link failure preemptively. It efficiently selects the worst-case performance link-failure topology and searches for a link weight set that reduces the worst-case total network resources used. Numerical results show that the proposed scheme is more effective in the reduction of worst-case total network resources used than the conventional schemes, Start-time Optimization and minimum hop routing.

Nowadays, error control codes have become an essential technique to improve the reliability of various digital systems. A new type error control codes called m-spotty byte error control codes are applied to computer memory systems. These codes are essential to make the memory systems reliable. Here, we introduce the m-spotty Rosenbloom-Tsfasman weights and m-spotty Rosenbloom-Tsfasman weight enumerator of linear codes over Fq[u]/(uk) with uk=0. We also derive a MacWilliams type identity for m-spotty Rosenbloom-Tsfasman weight enumerator.

The Open Mobile Alliance (OMA) Order of Rights Object Evaluation algorithm causes the loss of rights on contents under certain circumstances. By identifying the cases that cause this loss we suggest an algebraic characterization, as well as an ordering of OMA licenses. These allow us to redesign the algorithm so as to minimize the losses, in a way suitable for the low computational powers of mobile devices. In addition we provide a formal proof that the proposed algorithm fulfills its intent. The proof is conducted using the OTS/CafeOBJ method for verifying invariant properties.

Digital Rights Management (DRM) ensures that the usage of digital media adheres to the intentions of the copyright holder and prevents the unauthorized modification or distribution of media. Due to the widespread adoption of digital content use, DRM has received a fair amount of attention and has seen implementation in many commercial models. Although many DRM schemes have been introduced in the literature, they still suffer from some security issues and may not guarantee the quality of performance. In this paper, we propose a trust-distributed DRM model to provide improvements for realistic DRM environments to bring more functionality to users. We use the features of the smart cards to provide an option of anonymity for the consumer while continuing to protect the rights of the copyright holder and the financial interests of the media industry. We also classify the security criteria of DRM systems and show that our proposed smart card based DRM scheme satisfies all of these criteria.

In the work we introduce novel approach to remote sensing from space for the estimation of sea wave heights with a spaceborne high precision two-frequency radar altimeter with nadir synthesis antenna aperture. Experiments show considerable reduction of the decorrelation factor of the correlation coefficient and so significant enhancement of the sensitivity of the altimeter for the estimation for the sea wave status.

Due to an increase in multimedia content and the acceleration of digital convergence, demand for next-generation IPTV service is rapidly growing. IPTV service seamlessly provides both real-time broadcasting and content sharing services on diverse terminals through complex networks. In this paper, a secure and scalable content sharing framework is proposed for next-generation IPTV service. The proposed framework has an advantage over conventional content protection techniques in producing scalable content with transcodable, adjustable, and perceptual security features. Moreover, it ensures end-to-end security over the entire service range based on a single security mechanism. The suitability of the proposed approach is demonstrated experimentally using a practical service scenario with real-world environments. The experiments show that the proposed approach can provide several different levels of content security, from a perceptual level to an almost unintelligible level, while keeping the additional time overhead low. Consequently, it is expected that use of this security technology alone can have a practical contribution in creating new business opportunities for IPTV services.

Effect of an addition of a cooled step-up transformer to a flux locked loop (FLL) circuit was studied to reduce indirect rf interference to HTS-dc-SQUID. First, we demonstrated that a noise level of an HTS-dc-SQUID system using the FLL circuit with single room-temperature transformer could be easily degraded by radiation of rf electromagnetic wave to cables in the FLL circuit. It is thought that the rf radiation induced rf current in the circuit, and was transmitted to the SQUID to modulate the bias current, resulting in the increase of the noise level. To avoid the degradation due to such indirect rf interference, the cooled set-up transformer was added to the FLL circuit since it was expected that the additional transformer would work as a "step-down" transformer against the induced rf current. It was shown that the noise level of a HTS-SQUID system (SQUITEM system) operated in an electromagnetically unshielded environment could be improved to the same level as that measured in a magnetically shielded room by the additional cooled transformer and appropriate impedance matching.

Recently several weighted clustering algorithms have been proposed, however, to the best of our knowledge; there is none that propagates weights to other nodes without weight message for leader election, normalizes node parameters and considers neighboring node parameters to calculate node weights. In this paper, we propose an Energy Efficient and Stable Weight Based Clustering (EE-SWBC) algorithm that elects cluster heads without sending any additional weight message. It propagates node parameters to its neighbors through neighbor discovery message (HELLO Message) and stores these parameters in neighborhood list. Each node normalizes parameters and efficiently calculates its own weight and the weights of neighboring nodes from that neighborhood table using Grey Decision Method (GDM). GDM finds the ideal solution (best node parameters in neighborhood list) and calculates node weights in comparison to the ideal solution. The node(s) with maximum weight (parameters closer to the ideal solution) are elected as cluster heads. In result, EE-SWBC fairly selects potential nodes with parameters closer to ideal solution with less overhead. Different performance metrics of EE-SWBC and Distributed Weighted Clustering Algorithm (DWCA) are compared through simulations. The simulation results show that EE-SWBC maintains fewer average numbers of stable clusters with minimum overhead, less energy consumption and fewer changes in cluster structure within network compared to DWCA.