Introducing keytagging, a novel technique for the protection of medical image-based tests This paper introduces keytagging, a novel technique to protect medical image-based tests by implementing image authentication, integrity control and location of tampered areas, private captioning with role-based access control, traceability and copyright protection. It relies on the association of tags (binary data strings) to stable, semistable or volatile features of the image, whose access keys (called keytags) depend on both the image and the tag content. Unlike watermarking, this technique can associate information to the most stable features of the image without distortion. Thus, this method preserves the clinical content of the image without the need for assessment, prevents eavesdropping and collusion attacks, and obtains a substantial capacity-robustness tradeoff with simple operations. The evaluation of this technique, involving images of different sizes from various acquisition modalities and image modifications that are typical in the medical context, demonstrates that all the aforementioned security measures can be implemented simultaneously and that the algorithm presents good scalability. In addition to this, keytags can be protected with standard Cryptographic Message Syntax and the keytagging process can be easily combined with JPEG2000 compression since both share the same wavelet transform. This reduces the delays for associating keytags and retrieving the corresponding tags to implement the aforementioned measures to only 30 and 90 ms respectively. As a result, keytags can be seamlessly integrated within DICOM, reducing delays and bandwidth when the image test is updated and shared in secure architectures where different users cooperate, e.g. physicians who interpret the test, clinicians caring for the patient and researchers.• Copyright protection, to pursue illegal copies if the image has a commercial use.Hitherto, these security measures have traditionally been implemented in the medical context by means of cryptography, mainly in medical standards such as DICOM; or watermarking, mainly in research works.Nonetheless, these techniques present several disadvantages that limit their applicability for the protection of cooperative medical architectures (see Section 2). This paper proposes a new research direction, based on the goal of performing a secure and fast association of different types of data, called tags, to certain image features. These features and their associated tags may be categorized as stable, semistable or volatile, according to their robustness, i.e. their resistance to changing their values when the image undergoes some processing, such as compression or filtering. Stable tags would be suitable for authentication, traceability and copyright protection; semistable tags would be appropriate for private captioning with RBAC, and volatile tags would be ideal for integrity control and location of tampered areas. The association of tags to suitable features would be performed by means of a novel technique, called keytagging, whose main challenges are the extraction of image features with adequate robustness and the efficient encoding of the tags as a subset of the extracted features, by means of a map. This map will be compressed and protected to produce a stable, semistable or volatile keytag, which is an access key to retrieve the content of the tag from the image.Compared to other alternatives, keytagging presents five relevant features, derived from the fact that this technique does not modify the medical image to associate information. First and most obvious, the image always preserves its clinical quality, without the need for assessment. Second, even very stable features can be used to encode keytags, which ensures high robustness and capacity. Third, no complex rules are necessary for the selection of the image features, since there is no risk of destroying its clinical value. Fourth, collusion and forgery attacks have no effect on keytagged image-based tests. Fifth, keytagging enables the deployment of secure and efficient cooperative architectures, since each user can add information related with the test by associating new keytags, with no risk of distorting or removing the previous ones. To share this update of the test, it is enough to send the new keytags to the rest of the users.The rest of this paper is organized as follows. Section 2 analyzes related works and sets the background of this research. Section 3 depicts in detail the keytagging algorithms. Section 4 evaluates the performance of this approach by using different-size images from various medical acquisition modalities (CT, MRI, US, PET-CT), and image processing techniques that are common in the medical context. Based on the results retrieved, Section 5 proposes appropriate keytagging parameters for implementing the aforementioned security measures and depicts a risk assessment of the keytagging method. Next, Section 6 approaches the 3 integration of keytags within the DICOM protocol, to enable the development of efficient architectures in which different users can update the DICOM file maintaining its security level. Finally, Section 7 outlines the main conclusions from this research and proposes future lines of work.Keytagging has relevant influences from both cryptography and watermarking. The keytagging algorithm uses cryptographic tools to protect the keytags while the idea of associating data to different features of the image was motivated by watermarking techniques, which embed data into certain parts of the image. The advance introduced by keytagging is the ability to perform this association without embedding the data into the image, which permits avoiding any image distortion. Both keytagging and watermarking face a tradeoff between the robustness of keytags/watermarks and their capacity to host large contents, and both techniques require the protection of keytags/watermarks to prevent or hinder eavesdropping and forgery.Cryptography provides two efficient and reliable tools to protect image-based tests, which may also be applied to watermarks and/or keytags. These are encryption, which may be symmetric or asymmetric; and hashing, which is found in digital signatures (DS) and hash message authentication codes (HMAC). The former can be used to implement RBAC by means of Cryptographic Message Syntax (CMS [8]), while the latter enables binary integrity control (tampered/non-tampered), authentication and traceability. Furthermore, private captioning can also be implemented by encrypting some content based on the hash of the image [9]. However, the location of tampered areas in an image would be burdensome since it would require multiple hashing of different parts of that image. The typical policy to protect an image-based test, e.g. a DICOM file, is to de-identify the private data of the image, include it with the information associated to the image, and use CMS to implement RBAC on this data, leaving the de-authenticated image unencrypted and adding a DS to the file to guarantee integrity and authentication. In addition, each entity that processes the file shall add its own DS for traceability.The main issue in cryptography-based policies like this is the difficulty of developing cooperative architectures while maintaining the security measures. Any change in the test will invalidate all the previous signatures, and even though a new DS can be calculated, the traceability from the origin will be lost. An alternative is that each user adds his/her changes to the original test, digitally signs it and delivers the signed updated test to the rest of the users. In this way, the rest of the users can store the updated test The landscape of healthcare has evolved dramatically in the last few years with the digitization of medical image-based tests and the deployment of standard protocols for their storage and transmission, such as the Digital Imaging and COmmunication in Medicine (DICOM [1] ) standard. The new e-health model [2] fosters ubiquitous and pervasive access to users of medical information: physicians who interpret the tests, clinicians caring for the patient, patients, researchers, medical teachers and students, etc. Furthermore, it enables the establishment of cooperative architectures where different authorised users may perform some editing of the image (e.g. adding annotations), update its attached information (e.g. examination-related data, patient demographics, health records) and share with others. On the one hand, this reduces the time for diagnosis and enables more accurate treatments, more thorough researches, and the provision of e-learning or commercial uses, depending on patient consent. On the other hand, the e-health model requires a high level of accessibility to medical information, which increases its potential risks. Since image-based tests include sensitive information, they are a target for eavesdropping, which may result in social and professional damage for the victim; and also for tampering and forgery, which may lead to misdiagnosis, mistreatments and wrong research outcomes. Current legal regulations such as the HIPAA [3] in the USA, the PIPEDA [4] in Canada and the LOPD [5] in Spain require the implementation of robust security policies to protect medical image-based tests. For this purpose, different security measures may be combined [6, 7] :. • Role-based access control (RBAC), so that each authorised user can read and/or edit certain contents of the image-based test according to his/her professional role, e.g. physician, researcher, teacher, etc.. • Integrity control, to detect if the image has been tampered with, which would endanger its clinical value.. • Tamper location, pinpointing the areas where the image has been manipulated, which may be helpful to validate images that are modified in permissible areas.. • Authentication, assessing if the image received corresponds to the image originally acquired, to an image derived from the original or to an unrelated image.. • Private captioning, associating private information with the image, only retrievable by authorised users of the authenticated image.. • Traceability control for user accountability, by associating marks from each entity that processes the image.. with security, and add new updates from the last test version by following the same procedure. Nonetheless, as the number of users and updates of the medical image-based test grow, this approach becomes quite impractical in terms of delays, bandwidth and storage. Keytagging, a method based on associating tags of variable robustness to the image ROI, has proved to be an interesting alternative for the protection of medical image-based tests. In fact, this technique has a series of advantages over watermarking and cryptography alone. It preserves the image clinical content, prevents image forgery and collusion attacks, permits associating data to very stable features of the image and obtains a substantial capacity-robustness tradeoff by means of simple operations. In addition, the keytags (keys to access the tags) are tightly bound to the image, since no tag bit can be derived from them alone or by using an unrelated image. Tested with 512 × 512 and 1024 × 1024 pixel images, the robustness of stable tags to modifications that are typical in the medical context is total (N HD = 0) up to 512 bits and still very high (only three modifications produce N HD > 0) for 1024 bits. Semistable tags obtain good robustness to JPEG2000 CR 16:1 image compression and contrast and brightness change (N HD = 0% for tag length of 4096 bits), and bad, as intended, to local operations that distort the details of the image (average N HD > 15% for tag length ≥ 512 bits). Volatile tags achieve very little robustness to any modification of the image, even when associating very short tags (N HD > 45% for length ≥ 64 bits). Regarding runtime costs, associating a set of keytags and retrieving the corresponding tags for the simultaneous implementation of complementary security measures takes 55, 115 ms for 512 × 512 px 2 images; 140, 200 ms for 1024 × 1024 px 2 images and 510, 570 for 2048 × 2048 px 2 images. To guarantee the scalability of this approach, it has also been demonstrated that this method can be combined with JPEG2000 compression, maintaining its robustness while reducing the keytag association-tag retrieval delays to only 30, 90 ms for any image size.. As future work, we will evaluate improved keytag encoding methods in order to reduce the overhead, which is the main drawback of keytagging with respect to watermarking and cryptography. In addition to this, we intend to avoid the need for BCH coding in stable and semistable tags and to raise the capacity of semistable tags. To achieve this, we will research into the combined use of both most significant and sign bits of high magnitude coefficients as stable features, and also on the use of new transforms that may perform better than wavelets.. To sum up, keytagging achieves the transparent, secure and efficient association of stable, semistable and volatile tags to medical images. This enables the simultaneous implementation of private captioning with role-based access control, integrity control and location of tampered areas, authentication, traceability and copyright protection. Finally, it has been explained how this method can be seamlessly integrated within DICOM to facilitate the deployment of efficient medical architectures where different authorised users can update DICOM files with new information and share it without sacrificing any security measure.

O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O B O


Linear Bayes Policy for Learning in Contextual-Bandits Machine and Statistical Learning techniques are used in almost all online advertisement systems. The problem of discovering which content is more demanded (e.g. receive more clicks) can be modeled as a multi-armed bandit problem. Contextual bandits (i.e. bandits with covariates, side information or associative reinforcement learning) associate, to each specific content, several features that define the "context" in which it appears (e.g. user, web page, time, region). This problem can be studied in the stochastic/statistical setting by means of the conditional probability paradigm using the Bayes' theorem. However, for very large contextual information and/or realtime constraints, the exact calculation of the Bayes' rule is computationally infeasible. In this article, we present a method that is able to handle large contextual information for learning in contextual-bandits problems. This method was tested in the Challenge on Yahoo! dataset at ICML2012's Workshop "new Challenges for Exploration & Exploitation 3", obtaining the second place. Its basic exploration policy is deterministic in the sense that for the same input data (as a time-series) the same results are obtained. We address the deterministic exploration vs. exploitation issue, explaining the way in which the proposed method deterministically finds an effective dynamic trade-off based solely in the input-data, in contrast to other methods that use a random number generator. In statistical decision theory, conditional probability through the Bayes' theorem provides an optimal decision rule. However, in sequential decision problems under stochastic conditions, when informed decisions must be computed sequentially with larger previously unknown side information, or the time to take decisions is critical, the exact calculation of the Bayes' optimal rule is computationally intractable or -even worse-non-computable. Nowadays, this is an increasingly common picture and so empirical approximations to Bayesian methods (Robbins, 1964) are finding great applicability in the Machine and Statistical Learning fields.. One of the most general and theoretically sound approaches in Machine Learning is the Inductive Inference Theory, where the learner tries to predict future events from the history (sequence) of past events (see Solomonoff, 1964 and Vovk et al., 2005) , that is, "learning from observations". However, in general, there is no way to predict a sequence completely. Thus, one can divide a sequence in two parts: all that can be predicted, and all that cannot. Moreover, even for the predictable part some kind of search or trial-error process is often required. This effort is what is called exploration and deciding when one should explore or just use the current knowledge to make an educated guess (i.e. exploit current knowledge) is the so called exploration vs. exploitation problem (see Holland, 1975 , March, 1991 or Kaelbling et al., 1996 . The balance, ratio or proportion between these two opposed alternatives is called the exploration / exploitation trade-off and is performed following an arbitrary (ad hoc) algorithm referred to as the exploration policy.. In this article, we present a (empirical) Bayes-like method for learning in contextual-bandits problems. This method is able to take effective advantage of large contextual information in an efficient manner. In addition, one key-feature is the autonomous exploration / exploitation tradeoff that it achieves deterministically, based solely in the input-data, in contrast to other methods that use a random number generator. We have presented a feasible computationally efficient empirical Bayes-like method for learning in contextual-bandit problems. It is able to take effective advantage of large contextual information in an efficient manner. The method obtained the second place in the Challenge on Yahoo! dataset at ICML 2012 Workshop "new Challenges for Exploration & Exploitation 3", among more than 30 teams from several universities.. As shown in Section 3, the method has shown to possess several features that are often a hard requirement for real applications:. 1. It presents good stability in the long-run, which can be affected by overflows or arithmetic precision when running on big-data. 2. It keeps a good exploration / exploitation trade-off during all the running time, something that is truly difficult to tune manually. 3. It maintains a good learning trade-off between a long history of estimates and new data, that is, the plasticity level. 4. Its computation is quite simple, fast and efficient.. As a final remark, it is very important to mention that the algorithm scales linear in the number of binary features and scales linear in the number of arms. These are definitely the mayor advantages (together with its predictive performance) of the presented approach to contextual bandits with large binary features context. The presented method obtained the second place in the Challenge on Yahoo! dataset at ICML 2012 Workshop new Challenges for Exploration & Exploitation 3. Here the details of the results of that benchmark are presented. Here we present the details of the results obtained by the proposed algorithm on the "R6B -Yahoo! Front Page Today Module User Click Log Dataset, version 2.0 (300 MB)" Figure 1 shows the different performances obtained for the naïve II, naïve III and Linear Bayes approaches on the first 9200000 rows of the dataset. These results show that the Bayes' method outperforms a simple contextual bandit and that a simple contextual bandit outperforms a simple non-contextual bandit as well. All the three curves present the same shape (c.f. Figure 1) . This may indicate a strong pattern in user-click behavior that is independent of the contextual information. However, by the same reason, it can be argued that, since this pattern exists in the behavior of all the three experiments, the difference in performance should be strongly attributed to the effective use of contextual information by the Bayes based method. Furthermore, Figure 2 shows the difference in derivative CTR vs. processed lines between the Naïve II, III and Linear Bayes method over the first 9200000 rows. As it can bee seen, the difference is small and focused on certain periods of time. Tables 3 and 4 show the result of the evaluation of the proposed algorithm by the evaluation server application used by the organizing committee for the challenge. Table 3 shows the leaderboard for the top 10 entries of the first phase (training on the first 9200000). The entry of the proposed method is indicated in bold, by the name JAMH, Universidad Complutense de Madrid (UCM), with a score of 891.9 (5th place of 38 entries). Table 4 shows the leaderboard for the top 9 entries of the final-stage (evaluation on the complete dataset). The entry of the proposed method is indicated by the name JAMH, Universidad Complutense de Madrid, with a score of 887.6 (2nd place! with a very little gap w.r.t. the first place). This result indicates not only that the algorithm performs very well but that it also generalizes very well. Furthermore, since the algorithm obtained the fifth-place in the training phase and improved up to the second place in the final-stage (on the complete dataset), it may indicate that some desired features of the algorithm are working well. For instance, the stability in the long-run, which can be affected by overflows or arithmetic precision when running on big-data; keep a good exploration / exploitation trade-off during all the running time, something that is truly difficult to tune manually; and maintain a good learning trade-off between a long history of estimates and new data, i.e. , the plasticity level.. Furthermore, all the attempts that we have made (by now) to combine this algorithm with other complimentary exploration / exploitation procedures, such as -greedy or soft-max, have failed to beat the selection rule (24).

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Chi-square-based scoring function for categorization of MEDLINE citations Objectives: Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE® citations containing genetic relevant topic.Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH® descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain.Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision.We evaluated the method by comparing it to three machine learning algorithms (support vector machines, decision trees, naïve Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine learning algorithms.We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA 3 literature-based discovery support system as a preprocessor for gene symbol disambiguation process. The proliferation of the biomedical literature makes it difficult even for experts to absorb all the relevant knowledge in their specific field of interest. Effective heuristics for identifying articles containing relevant topics would be beneficial both for the individual researchers and for curation of biomedical databases. Manual literature curation is a resource and time-consuming process that is prone to inconsistencies [1] . Therefore, an automated system which could correctly determine the relevant topic of the citation retrieved from a bibliographic database, is needed. This is referred to as text or document categorization (DC), which is a process of assigning a text document to one or more categories based on its content or topic [2] .. A large body of studies has been published addressing the problem of DC in biomedical domain. Most frequently used approaches include support vector machines (SVM), decision trees (DT), and naïve Bayes (NB). Humphrey et al. [3] presented the technique of automatic indexing of documents by discipline with broad descriptors that express the general nature and orientation of the document. Donaldson et al. [4] used an SVM algorithm to locate PubMed® citations containing information on protein-protein interaction before they were curated into the Biomolecular Interaction Network Database. Dobrokhotov et al. [5] applied a combination of natural language processing and probabilistic classification to re-rank documents returned by PubMed according to their relevance to Swiss-Prot database curation. Bernhardt et al. [6] developed an automated method for identifying prominent subdomains in medicine that relies on Journal Descriptor Indexing, an automated method for topical categorization of biomedical text. Miotto et al. [7] tested the performance of DT and artificial neural networks to identify PubMed abstracts that contain allergen cross-reactivity information. Chen et al. [8] combined an SVM and a phrase-based clustering algorithm to categorize papers about Caenorhabditis elegans. McDonald et al. [9] exploited the maximum entropy classification principle to calculate the likelihood of MEDLINE® abstracts containing quotations of genomic variation data suitable for annotation in mutation databases.. Recently, Wang et al. [10] used an NB classifier to speed up the abstract selection process of the Immune Epitope Database reference curation. The most popular platforms to evaluate DC algorithms in biomedical domain are Text Retrieval Conference (TREC) [11] and Critical Assessment of Information Extraction systems in Biology (BioCreAtIvE) [12] . Classical statistical methods and machine learning algorithms have been shown highly useful for DC in the area of biomedical informatics. However, they usually require long computational times and tedious manual preparation of training datasets. Here, we fill this gap by proposing a novel and simple DC algorithm as well as preparation of a training corpus for saving manual annotation. We have proposed a simple chi-square-based scoring method to categorize MEDLINE citations according to its genetic relevance. Results of experimental validation showed that the proposed method is as good as popular machine learning algorithms, although the differences are not statistically significant. Our algorithm could be easily reimplemented as a module in a general information extraction system and may thus be a powerful tool for the broader research community. The algorithm is currently implemented in the BITOLA literature-based discovery support system [42] as a preprocessor for gene symbol disambiguation process.. The presented approach provides very good performance, but further slight modification may allow even better performance. An important aspect that could be addressed in the future is the better selection of representative corpora. The selection of the corpora is a crucial step in the methodologies of corpora linguistics, since it defines the quality of the training dataset. Ideally for our method, the genetic domain corpus should contain information relevant only to genetics. An interesting future research direction is the extension of the proposed methodology for handling simultaneously several genetically specific knowledge sources, which would better reflect the genetic domain. There are several such sources, including Online. Mendelian Inheritance in Man (OMIM), Gene Ontology (GO) and Gene Reference. Into Function (GeneRIF) databases. Another technique that may increase performance is linking MeSH terms to Unified Medical Language System (UMLS) concepts and semantic types. As future work the setting of the initial weights to characteristic words should also be studied. In this paper, we developed and experimentally validated a semi-supervised DC approach and demonstrated a simple chi-square-based scoring function algorithm. The method has been shown to discover key MeSH descriptors in MEDLINE, which differentiate between genetic and nongenetic domains. The fundamental conclusions of this study can be summarized as follows: (i) in our particular settings the chisquare-based scoring function algorithm proved to be effective for MEDLINE citation categorization task; (ii) the chi-square-based algorithm is as accurate as evaluated machine learning algorithms, namely SVM, DT, and NB, although the differences are not statistically significant; (iii) chi-square-based algorithm is easy to implement into existent text mining systems.. Our chi-square-based algorithm is a simple statistical procedure, based on widely used and well-known chi-square statistics. In spite of the fact that our results did not yield statistical significant differences between the chi-square algorithm and the benchmark machine learning algorithms, they are still promising. Furthermore, the chi-square algorithm does not perform 'transformations' on the original data, possibly affecting reliability of the categorization process. Our approach does not use the full text titles or abstracts, so it is much more efficient. With the exception of the threshold parameter, no other parameters needed to be tuned in the proposed approach. Last but not least, our method does not need a manually tagged training corpus, which is timeconsuming, expensive, and error-prone to construct.. The benchmarking experiments indicate that the categorization results are competitive with state-of-the-art machine learning algorithms like SVM, DT, and NB. According to our results, SVM was the second best algorithm, followed by DT. The high dimensional nature of text data has been shown to be the main reason for bad performance of many classification methods [36] . The SVM method is generally the best choice for high dimensional feature representation, such as those for free text, but requires a lot of parameters to adjust and is very time consuming and labor intensive.. In addition, the major disadvantage of DT is instability. Small changes in the data often result in a very different tree and big changes in prediction performance. The reason for that is the hierarchical process of tree induction, where errors made in the splits close to the root are carried down the entire tree. However, DT has been overlooked in the domain of text categorization and big advantage of DT over the other classifiers is that they could be visualized as a set of rules explaining the categorization process. NB was the worst performing algorithm, which confirms previous research that NB is a popular but not the best algorithm for DC [37] .. However, based on the absence of statistically significant differences between the evaluated approaches we could not be certain that one of the machine learning classifiers used would not have performed better, and MeSH descriptors may not be optimal for the task of identifying genetic citations. However, the results generated with our method are a promising starting point of this task. Following Occam, we doubt that more intricate classifiers should necessarily be preferred over simple approaches. As a heuristic, Occam's razor principle tells us that the simpler model is generally to be preferred over a more sophisticated model [38] . This is particularly relevant in practical online applications where large amounts of unstructured text data have to be processed quickly and accurately.. Our algorithm requires that the input documents to be categorized have assigned MeSH descriptors. That is a limitation for documents without MeSH descriptors.. However, our algorithm could be used as a module in a general information extraction or retrieval system by using text words instead of controlled vocabulary indexing terms. Indeed, the immediate goal of this study was to provide a way to categorize biomedical literature according to genetic and nongenetic MEDLINE citations, but the process to categorize text in another domain is fairly straightforward. The researcher must first create a frequency A wide array of statistical and machine learning techniques has been applied to DC.. Most of them are based on having some initial set of class-labeled documents, which is used to train an automatic categorization model. There has been much prior work in applying methods to analyzing the biomedical literature to extract biomedical data, but relatively little prior work has addressed the task of screening the entire literature for particular types of citations. To the best of our knowledge no one has tried to generalize the MeSH descriptors in a manner described in this article. Only a few researchers have tested their DC systems using the entire set of MeSH terms. Rubin et al. [39] developed a curation filter, an automated method to identify citations in MEDLINE that contain pharmacogenetics data pertaining to gene-drug relationships.. They reported F-measures ranging from 0.20 to 0.88 for different experimental settings. DC machinery has also been used in maintaining systematic reviews in the domain of internal medicine [40] and of the efficacy of drug therapy [41] .. There are also some disadvantages of our chi-square-based categorization approach.. Each MEDLINE citation has an average of twelve MeSH terms assigned. The standard deviation estimated on a collection of 734 randomly selected MEDLINE citations is very high (SD = 5.15), suggesting a high scattering of the number of MeSH terms assigned to each citation. Therefore, the citations with more MeSH terms assigned are more prone to extremely positive or extremely negative scores, which means they are categorized either into genetic or nongenetic domain with greater confidence (although the proposed heuristic scoring could not be directly interpreted as a measure of statistical confidence). Many MeSH terms are also too specific to be used as valid representatives of broad topic domains. In addition, the MeSH terms are related hierarchically, and frequently both the parent and the child are assigned to the same citation; this results in artificially increased or decreased chi-square decision score. The starting point of our algorithm is a frequency profile For example, the main characteristic terms for genetic domain are 'Molecular Sequence Data', 'Amino Acid Sequence', and 'Base Sequence'.. Our evaluation corpus contained 1,000 citations. Annotators achieved a κ score for inter-annotator agreement of 0.78. The citations that were most difficult to categorize were those concerning general biochemistry, proteomics, and metabolomics.. Consensus voting was then used to achieve complete agreement between judges. The annotated corpus is publicly available at our homepage [35] and can serve as a benchmark for other applications. The percentage of genetic citations in this corpus was 18.6% (186 citations about genetics and 814 citations about nongenetic topics).. There were 88 citations (8.8%) that contained no MeSH terms and 223 citations without an abstract field (22.3%). The title field was present in all the citations in the evaluation set. We built a join set of citations in which each citation has a title, To estimate the quality of the training data and to evaluate the assumption that the citations from the 'gene2pubmed' file were indicative of genetic domain, we joined the citations from the manually annotated evaluation corpus to those derived from the 'gene2pubmed' file. If a citation in the evaluation corpus was present in the 'gene2pubmed' file, we labeled it as genetic citation; otherwise we labeled it as nongenetic citation. The accuracy of 0.77 with 0.98 recall and 0.78 precision was obtained. The F-measure was 0.87. The methodology we present here was strongly motivated and is used in support of our previous work on literature-based discovery. BITOLA, a biomedical discovery support system, was designed to discover potentially new relationships between diseases and genes [13, 14] . Gene symbols are short acronyms that often create ambiguities if used outside the context of gene names [15] . For example, in the sentence [16] 'The inverse association between MR and VEGFR-2 expression in carcinoma suggest a potential tumor-suppressive function for MR' we need to decide if 'MR' stands for 'mineralocorticoid receptor' gene or 'magnetic resonance' imaging.. Our method is designed to categorize citations very broadly according to discipline in order to help disambiguate gene symbols in the millions of documents in MEDLINE.. It therefore has the potential to filter out nonrelevant (i.e., nongenetics) citations early on. The problem can be formally described as a task of assigning a MEDLINE citation to the genetic or nongenetic domain, based on its content. More specifically, our task was to categorize MEDLINE citations into genetic and nongenetic domain first and then to detect gene symbols only in the genetic domain citations. Here we address only the problem of categorization of MEDLINE citations. We refer to the genetic domain as a subset of MEDLINE citations in which occurrences of gene symbols are more probable than in any other subset of citations.. Therefore, a DC algorithm that has a high categorization speed as well as high classification accuracy is required. Our objective was to investigate the benefit of using the MeSH® controlled vocabulary as a representation level of the MEDLINE citation for DC. We developed and evaluated a document ranking technique based on chi-square test for independence. The chi-square test for DC was introduced first by Oakes et al. [17] and Alexandrov et al. [18] . Preliminary results of our work were presented at AMIA 2008 Annual Symposium [19] . In this work, we highly extend the conference paper with a much more rigorous statistical validation methodology on a larger data set. Our proposed approach is simple to implement and can be easily integrated into the existing framework of the BITOLA system. It is able to process the full MEDLINE distribution in a few hours. Pre-processing of the corpora MEDLINE is the main and largest literature database in the biomedical related fields.. As of this writing, MEDLINE contains about 17 million research abstracts dated back to the 1950s. MEDLINE citations are manually annotated using MeSH vocabulary by trained indexers from the National Library of Medicine (NLM). MeSH is a controlled vocabulary thesaurus consisting of medical terms at various levels of specificity.

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Self-localization method for mobile robot using acoustic beacons In this paper, we have proposed a low-cost self-localization method which uses 4 elements of microphones, wheel rotation and sound sources as beacons, whose absolute location and frequency bands are known. The proposed method consists of following 4 steps. The proposed method (i) execute self-localization using wheel-based odometry, (ii) estimate direction-of-arrival (DOA) of the sound sources using sounds recorded by the elements of the microphone array, (iii) predict the DOA of the sound sources from estimated location and pose, and (iv) conduct self-localization by integrating all of the information. To evaluate the proposed method, experiments were conducted. The proposed method was compared to the conventional methods, which were wheel-based odometry and self-localization using only DOA. In the experiments, we have supposed the house-cleaning robot and its trajectory. As results, without any obstacles or walls, the mean of the estimation errors by wheel-based odometry were 670 mm and 0.08 rad, and those of self-localization using only DOA were 2870 m and 0.07 rad in the worst case. In contrast with these methods, proposed method results in 69 mm, 0.02 rad as the worst estimation error of self location and pose. From the result with occlusion of a sound source, the mean of the localization error increased 60 mm, as the proposed method detects the incorrect DOA and prevents it from estimation. From the result with reflective wave from wall, there was a place where the localization error was large. The cause of this error was considered as directivity of sound source. These results indicate that the proposed method is feasible under indoor environment. In this paper, we have proposed the low-cost selflocalization method which uses 4 elements of microphones, wheel rotation and known sound sources as beacons. The proposed method consists of following 4 steps. The proposed method (i) execute wheel-based odometry, (ii) estimate DOA of the sound sources using sounds recorded by the elements of the microphone array, (iii) predict the DOA of the sound sources from estimated location and pose, and (iv) conduct self-localization by integrating all of the information. To evaluate the proposed method, experiments were conducted. The proposed method was compared with the conventional methods, which were wheel-based odometry and selflocalization using only DOA. Three types of experiments were conducted to evaluate the proposed method with the occlusion or reflection of the sound. In experiment, the robot run on the trajectory which was supposed Mobile robots are widely used indoor for building security, cleaning, automatic guided vehicle system and so on. For autonomous robots, self-localization is one of the essential function to achieve tasks autonomously. While there are needs for inexpensive robots without precise self-localization, they are generally expensive because of the excessively accurate sensing. To make indoor autonomous robots inexpensive, it is essential to develop the self-localization method which does not require expensive sensor or processor and have just enough accuracy. Conventional self-localization methods are divided mainly into two approaches: the methods which use internal information and the methods which use external information of the robot. Internal information of the robot is measured by internal sensors such as rotary encoders *Correspondence: mizutani@iit.tsukuba.ac.jp 3 Faculty of Engineering, Information and Systems, University of Tsukuba, Ibaraki, JP Full list of author information is available at the end of the article or accelerometers equipped on the robot [1] . The selflocalization using these internal sensors requires relatively low-calculation cost, as the self-localization using these sensors are merely the accumulation of the information, such as wheel rotation obtained from rotary encoders or accelerations measured by accelerometer. Moreover, the internal sensors and the processor used for self-localization are generally inexpensive. However, once the error, such as bias of the measurement or slip of the robot, was accumulated, it cannot be detected and the estimation error piles up. Sometimes the error would be fatal as the robot cannot reach to the destination or crashes into a facility. While internal sensors are low-cost, external information are usually measured by laser range finder [2] , camera [3] , ultrasonic range sensor [4] and microphones [5] . With the external information from laser range finder or camera, simultaneous localization and mapping (SLAM) algorithm provides robust self-localization results as it integrates the external information of the robot [6] . However, these sensors are generally expensive and there are much more information to be processed than the internal sensors so that it is hard to be implemented on a low-cost processors, such as microcontrollers. Even if the sensor itself was inexpensive, the extraction of the feature from the obtained data such as image is costly calculation or otherwise the localization need to be achieved by monte-carlo method which requires much memory and calculation cost than simple Kalman filtering [7] . Because of these reasons, most of the self-localization methods with external information require high investment for sensors and computers. However, suitable self-localization method which does not require expensive sensor or processor and does not accumulate the error over time have not been realized.. From the reasons of the sensor cost, there are some researches focused on the sound signals as beacons to self-locate the mobile robot. Previously, many researches have been done for the use of sound signals for robots and several techniques such as the self-localization, sound sources separation and autonomous speech recognition are reported [8] [9] [10] [11] [12] [13] . Also there are self-localization methods using microphones installed around the room [14] [15] [16] [17] . However, most of them use a huge amount of microphones for self-localization or separation of sound signals to improve accuracy or to suppress the effect of reflective sounds. While these techniques use many microphones, it is difficult to achieve self-localization with only a few number of microphones [5] . We have proposed a self-localization method using only a few number of microphones and conducted simulation to examine it [18, 19] . This is based on the techniques using microphone array consists of small element number [20] [21] [22] .. In this paper, we propose a low-cost self-localization method which uses 4 elements of microphones, wheel rotation and known sound sources as beacons. Comparison of conventional low-cost self-localization methods and proposed method is shown in Fig. 1 . Wheel-based odometry is one of the most popular self-localization methods, as it is easy to be implemented. However, it is known that the errors of measurements are accumulated and the total estimation error increase over time. In contrast, acoustic localization method does not accumulate errors of measurements, while the estimation results of this method sometimes diverge. The proposed method is the combination of these two. The features of the proposed method are followings:. • The proposed method uses only low cost sensors: a few microphones and rotary encoders.. • The proposed method combines 2 low-cost methods, which have complementary properties, to improve accuracy of each other. • The extraction of the landmark can be easily conducted (e.g. using band-pass filter) relative to the camera image or laser scanned data.. • Extended Kalman filter is used so that it is able to deal with errors of measurements and able to be implemented on a powerless computers.. As this method uses only sound signals and it is not a SLAM problem, the proposed method have the characteristics that the sensors are inexpensive and the calculation cost are relatively low. To evaluate the proposed method, experiment were conducted. The proposed method was compared with the conventional methods, which were wheel-based odometry and self-localization using only DOA.

B O O B I O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O


Parallel-machine scheduling with simple linear deterioration to minimize total completion time We consider the parallel-machine scheduling problem in which the processing time of a job is a simple linear increasing function of its starting time. The objective is to minimize the total completion time. We give a fully polynomial-time approximation scheme (FPTAS) for the case with m identical machines, where m is fixed. This study solves an open problem that has been posed in the literature for ten years. For most scheduling problems it is assumed that the job processing times are fixed parameters [14] .. However, such restrictive assumptions represent an oversimplified view of reality. Job processing times are not necessarily fixed because jobs may deteriorate while waiting to be processed. Examples can be found in financial management, steel production, resource allocation and national defence, etc., where any delay in processing a job may result in deterioration in accomplishing the job. For a list of applications, the reader is referred to [2, 10, 11, 13] . Such problems are generally known as the deteriorating job scheduling problem.. Work on the deteriorating job scheduling problem was initiated by Brown and Yechiali [2] , and Gupta and Gupta [6] . They focused on the single-machine makespan problem under the linear deteriorating condition. Since then, scheduling problems with time-dependent processing times have received increasing attention. An extensive survey of different models and problems was provided by Alidaee and Womer [1] . Cheng, Ding and Lin [5] recently presented an updated survey of the results on scheduling problems with time-dependent processing times.. The problem under consideration can be formally described as follows: There are n independent jobs J = {J 1 , J 2 , · · · , J n }, which are simultaneously available at time t 0 > 0, to be processed nonpreemptively on m identical parallel machines. We assume, as in Mosheiov [12, 13] , and Chen [3, 4] , that the actual processing time of job J j is p j = α j s j , where s j and α j are the starting time and the growth (or deterioration) rate of J j , respectively. The assumption "t 0 > 0" is made here to avoid the trivial case of t 0 = 0 (when t 0 = 0, the completion time of each job will be 0). Let C [i] denote the completion time of the ith job in a schedule. It is easy to see that. ) for every i ≥ 1 in a schedule. Since t 0 is a common factor in all the completion times, it will not affect the optimal schedule under any performance criterion. Without loss of generality, we assume that t 0 and all α j for every j are integral. Our goal is to minimize the total completion time, i.e., n j=1 C j . Using the notation of Chen [3] , we denote the problem as the PTCT (i.e., parallel-machine total completion time) problem. This paper studied the parallel-machine scheduling problem in which the processing time of a job is a simple linear function of its starting time to minimize the total completion time. We gave a fully polynomial-time approximation scheme for the case with m machines, where m is fixed. Future research may focus on scheduling problems with jobs of more general deterioration types. It will also be interesting to investigate problems with other scheduling objectives.

B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


The influence of prototype fidelity and aesthetics of design in usability tests: Effects on user behaviour, subjective evaluation and emotion An empirical study examined the impact of prototype fidelity on user behaviour, subjective user evaluation and emotion. The independent factors of prototype fidelity (paper prototype, computer prototype, fully operational appliance) and aesthetics of design (high vs. moderate) were varied in a betweensubjects design. The 60 participants of the experiment were asked to complete two typical tasks of mobile phone usage: sending a text message and suppressing a phone number. Both performance data and a number of subjective measures were recorded. The results suggested that task completion time may be overestimated when a computer prototype is being used. Furthermore, users appeared to compensate for deficiencies in aesthetic design by overrating the aesthetic qualities of reduced fidelity prototypes. Finally, user emotions were more positively affected by the operation of the more attractive mobile phone than by the less appealing one. The central question of this article concerned the utility of prototypes that are of lesser fidelity than the reference system during usability tests. The main results showed that task completion time may be overestimated when a computer-based simulation is used. Furthermore, the effects of fidelity levels on attractiveness ratings appeared to be stronger for less appealing products than for attractive ones. It also emerged that objective performance parameters collected during the usability test and subjective usability ratings were not associated. Finally, the results showed no evidence for fidelity level affecting emotions or subjective user evaluation.. The results showed that task completion time was higher for the computer-based simulation than when a fully operational product was being used. This effect was observed for both mobile phones, though they differed with regard to the strength with which this effect occurred. The increased task completion time under the computer condition was partly caused by prototype-specific errors being made by users that resulted from differences in the interactivity of prototype (cf. model of Virzi et al., 1996) . With the computer prototype, ineffective clicks were made by participants because they erroneously extended the interactivity of the device from the computer screen (direct manipulation was possible) to the display of the simulated mobile phone (direct manipulation was not possible). However, it was only the representation of the mobile phone's controls on the touch screen that was interactive. Although the touch screen permits a more natural interaction of the user with the mobile phone than a conventional screen (for which the user needs to use a mouse), this advantage may be accompanied, as observed in the present case, by unanticipated side effects in the form of negative transfer.. For the attractiveness rating of the appliances, an interesting interaction between prototype fidelity and design aesthetics was observed. While there was no difference in ratings across different fidelity levels for the highly aesthetic mobile phone, the moderately aesthetic phone was rated lower on attractiveness for the original appliance than for the reduced fidelity prototypes. The fact that the two reduced fidelity prototypes had similar ratings like the original appliance for the highly aesthetic design is in itself a somewhat surprising result. This suggests that some compensatory activity on the part of the user took place since neither the paper prototype nor the computer-based prototype was aesthetically refined (e.g., lacking colour and shape of the reference appliance). Users may have mentally anticipated of what the real appliance might look like and employed this mental picture as a basis for their rating. For the moderately aesthetic phone design, users may have engaged in a similar process in that they extrapolated the appearance of the computer and paper prototypes to the real appliance (indeed, there were no significant differences between the two computer-based prototypes and the two paper prototypes across phone types). Since the computer-based and paper prototypes were judged to be more attractive than the real appliance, it can be speculated that under the reduced fidelity conditions users created a mental model of the real appliance representing a much more attractive design than the real appliance actually enjoyed. This may suggest a kind of ''deficiency compensation'' effect. As this interaction between prototype fidelity and design aesthetics was not predicted, it needs to be treated with some caution but, if confirmed in subsequent studies, it would have implications for the use of reduced fidelity prototypes for the purpose of attractiveness judgements.. The results showed no association between objective performance parameters and subjective usability evaluation. While there was a clear preference of users for the more aesthetic appliance because of higher attractiveness ratings and higher perceived usability, this was not paralleled by better objective usability of that appliance. This suggests that perceived usability may be more strongly associated with attractiveness ratings than objectively measured usability parameters. This result is in support of the findings of Tractinsky (1997) , who proposed that the beauty of design would positively affect perceived usability. While in Tractinsky's study no user-product interaction took place (with the usability rating of users being based on the mere look of the product), the present study provided similar evidence even for the case when user-product interaction occurred. If this finding was to be found consistently, it would imply that the beauty of a product was such an important aspect that it would also need to be considered by designers and engineering psychologist when designing for usability. Table 3 Mean ratings of emotions at t 0 (prior to usability test) and t 1 (after usability test) as a function of appliance usability on a 9-point Likert scale; significant differences as a function of design aesthetics are indicated by stars (*p < .05, ***p < .001).

O O O B I O B O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O B O O O O O O O O O O


A method for increasing expressivity of Gene Ontology annotations using a compositional approach Background: The Gene Ontology project integrates data about the function of gene products across a diverse range of organisms, allowing the transfer of knowledge from model organisms to humans, and enabling computational analyses for interpretation of high-throughput experimental and clinical data. The core data structure is the annotation, an association between a gene product and a term from one of the three ontologies comprising the GO. Historically, it has not been possible to provide additional information about the context of a GO term, such as the target gene or the location of a molecular function. This has limited the specificity of knowledge that can be expressed by GO annotations.The GO Consortium has introduced annotation extensions that enable manually curated GO annotations to capture additional contextual details. Extensions represent effector-target relationships such as localization dependencies, substrates of protein modifiers and regulation targets of signaling pathways and transcription factors as well as spatial and temporal aspects of processes such as cell or tissue type or developmental stage. We describe the content and structure of annotation extensions, provide examples, and summarize the current usage of annotation extensions.Conclusions: The additional contextual information captured by annotation extensions improves the utility of functional annotation by representing dependencies between annotations to terms in the different ontologies of GO, external ontologies, or an organism's gene products. These enhanced annotations can also support sophisticated queries and reasoning, and will provide curated, directional links between many gene products to support pathway and network reconstruction. GO annotation extensions have been introduced to enhance the depth and utility of annotation data by capturing specific contextual information regarding a gene product's function or location. Curators can now create, on-the-fly, complex GO annotations that describe dependencies and consequences of a gene product's function or location more completely than was previously possible. Data curated using annotation extensions provides a repository for experimentally verified regulation targets for a wide range of gene products, including transcription factors and microRNAs, information that is currently not captured by other standardized annotation approaches. A large corpus of annotations now make use of annotation extensions, and this number is growing rapidly as groups make use of powerful curation tools such as UniProt-GOA's Protein2GO [30] . Extensive annotation enhancement makes GO data more informative for a biologist's understanding of a gene or process of interest, and provides additional value to the data which can be used by GO analysis tool providers to enhance the interpretation of high-throughput datasets, such as those created by next generation sequencing, transcriptomic and proteomic studies. Comprehensive representation of the roles of gene products, individually and in combination, is essential to the understanding and modeling of biological systems. In addition to a gene product's intrinsic activity, aspects of the context in which it acts, such as the gene products it acts upon, subcellular location of the activity, distribution in cell or tissue types, or temporal restrictions to a cell cycle phase or developmental stage, must be described in order to obtain a full description of its biological role.. The Gene Ontology (GO) is a bioinformatics resource that uses structured controlled vocabularies (ontologies) to describe the molecular functions or activities of a gene product, the biological processes in which a gene product is involved and the cellular components in which a gene product is located. Associations or 'annotations' can be made between ontology terms and specific genes or gene products using a variety of manual or algorithmic methods that rely upon experimental evidence or sequence similarity, for example, to support the assertion [1] [2] [3] [4] .. While the ontological rigor of the GO vocabularies has been enriched over the years through the use of expressive formalisms that permit logical reasoning and interaction with external ontologies [5] , the annotations themselves have, until now, remained simple declarative statements. Each GO annotation is essentially a pair, combining a single gene product with a single GO term, plus supporting metadata such as the evidence for the association [6] . Furthermore, any gene product can be associated with many GO terms, and likewise any GO term could be used to annotate any number of gene products, the annotations thus coded remain independent. The simplicity of this core GO annotation model has facilitated the population of large annotation datasets, but this simplicity has, as well, been unable to capture the interconnections between multiple annotations to multiple genes, resulting in limitations on the granularity and connectivity of information that could be captured. Figure 1 illustrates this by showing a subset of Molecular Function and Cellular Component annotations to several gene products, including microsomal glutathione transferase 1 [7] . While the annotations can describe which activities the gene products can perform, and in which components they are located, there is no way of combining this information to convey which activities are performed in which locations.

O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Apps at Hand: Personalized Live Homescreen Based on Mobile App Usage Prediction To facilitate the discovery of mobile apps in personal devices, we present the personalized live homescreen system. The system mines the usage patterns of mobile apps, generates personalized predictions, and then makes apps available at users' hands whenever they want them. Evaluations have verified the promising effectiveness of our system. key words: mobile app, personalized pattern, usage prediction, mobile app discovery, personalized live homescreen With the huge expansion of mobile devices in recent years, the development of mobile apps has undergone a tremendous growth. Such a tendency results in the continuous installations and frequent updates of apps on personal mobile devices. This eventually gives rise to the essential app discovery problem for mobile users. While the screen spaces of a device are rapidly consumed with so many apps installed, it becomes much more difficult for users to find the app they want among screens of apps. Although popular mobile operating systems provide multiple homescreens for users to customize shortcuts of apps for quick accessing, there are still lots of apps lying quietly in the background. Users often have to scroll through screens of apps just for discovering and accessing one. Such a problem significantly hurts the experiences of users, but it has attracted little attention from the research community.. To address such a challenge, we present the Personalized Live Homescreen (PLH) system that implements a novel "live homescreen" in a "personalized" way. The main efforts and contributions of this paper are as follows:. 1. We originally propose the concept of live homescreen to tackle the app discovery problem of mobile devices. The live homescreen differs from the traditional homescreen by automatically predicting and displaying the apps that are of interest to a user, instead of always showing a static set of apps fixed by users. We design, implement and evaluate the PLH system, which identifies the potential of providing personalized intelligent interface by leveraging the app usage patterns. On the analysis of app usage for mobile users, the research community has conducted valuable investigations [1] , [4] - [6] . However, they mainly focus on the analysis of large scale of devices, users or apps, in contrast of the fine-grained mining of personal usage patterns, addressed in this paper.. Applications leveraging the usage of mobile apps have already been presented [7] - [9] . However, they mainly concentrate on those apps that have not be installed by users, instead of promoting the user experiences with apps that have been installed. Additionally, they did not give attention to providing intelligent user interfaces.

O O O B I I O O B I B I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I B I B I O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O