Search

In the hotel booking market, high click-through rates are essential for online travel agencies (OTAs) to earn commissions. Given the dominance of mobile devices in web traffic, analyzing the mobile click-through decision-making process plays a vital role in search engine optimization. This study proposes a sequential framework that leverages Bayesian inference to model individual users' click-through behaviors using user digital footprints, which encompass sequences of search, browse, compare, and click-through actions. This framework extracts three categories of information based on the degrees of dynamism in the hotel search process, ranging from less dynamic to highly dynamic levels: static hotel attributes, information cues in the search results, and temporal characteristics of user behaviors. Extensive experiments on a global OTA mobile clickstream dataset with over 600,000 observations reveal the substantial superiority of the proposed framework over the baseline models like probit regression and Naive Bayes. Notably, temporal characteristics emerge as the most important category. Drawing on our model, we delve into the interpretability of these three information categories. Additionally, we compare their varying impacts across different devices. Beyond these findings, this study offers valuable managerial implications for mobile OTA search engine marketing and optimization. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated whether websites regarding diabetic retinopathy are readable for patients, and adequately designed to be found by search engines. Methods: The term "diabetic retinopathy" was queried in the Google search engine. Patient-oriented websites from the first 10 pages were categorized by search result page number and website organization type. Metrics of search engine optimization (SEO) and readability were then calculated. Results: Among the 71 sites meeting inclusion criteria, informational and organizational sites were best optimized for search engines, and informational sites were the most visited. Better optimization as measured by authority score was correlated with lower Flesch Kincaid Grade Level (r = 0.267, P = 0.024). There was a significant increase in Flesch Kincaid Grade Level with successive search result pages (r = 0.275, P = 0.020). Only 2 sites met the 6th grade reading level AMA recommendation by Flesch Kincaid Grade Level; the average reading level was 10.5. There was no significant difference in readability between website categories. Conclusion: While the readability of diabetic retinopathy patient information was poor, better readability was correlated to better SEO metrics. While we cannot assess causality, we recommend websites improve their readability, which may increase uptake of their resources. [ABSTRACT FROM AUTHOR]

This article introduces Mi-Go, a tool aimed at evaluating the performance and adaptability of general-purpose speech recognition machine learning models across diverse real-world scenarios. The tool leverages YouTube as a rich and continuously updated data source, accounting for multiple languages, accents, dialects, speaking styles, and audio quality levels. To demonstrate the effectiveness of the tool, an experiment was conducted, by using Mi-Go to evaluate state-of-the-art automatic speech recognition machine learning models. The evaluation involved a total of 141 randomly selected YouTube videos. The results underscore the utility of YouTube as a valuable data source for evaluation of speech recognition models, ensuring their robustness, accuracy, and adaptability to diverse languages and acoustic conditions. Additionally, by contrasting the machine-generated transcriptions against human-made subtitles, the Mi-Go tool can help pinpoint potential misuse of YouTube subtitles, like search engine optimization. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO) is an important tool that can predict the number of website visits, and good SEO strategies can help increase the visibility of educational resources. In the current article, the relevance between Search engine optimization (SEO) characteristics and the number of Moodle users in four Central Asian countries was examined. The analysis of learning management systems covered 38 Moodle websites. Analysis was conducted using semi-automated tools. Results suggested that although there is no difference in general SEO scores of Moodle installations, page structures and link structures in websites of different countries were dissimilar. Also, server configuration practices were different. In addition, there was a positive correspondence between the number of visits to a website and the number of external factors; the duration of users' visits was correlated with page structure. As for the normalized number of visits, the analysis revealed that in Kazakh universities, students use the Moodle system more frequently in a day than in other countries, while visits to Tajik universities' Moodle platforms were the lowest; the difference was 53 users per day. [ABSTRACT FROM AUTHOR]

This paper presents the first systematic investigation into the search engine optimization practices of major contract cheating websites in the United States. From a business perspective, visibility in organic search engine results is considered one of the top client recruitment tools. The current understanding of student recruitment strategies by these companies remains largely unexplored in both academic literature and popular press. Replicating the business research practices used in the search engine optimization industry, comprehensive search engine ranking and traffic data was obtained for the 38 largest contract cheating websites in the US. The overall objective was to illuminate the strategies that these companies take to get their services at the top of the search results of as many students as possible – not just the relatively small proportion of students actively cheating. The results show that these companies dominate the search results for not just students searching to cheat, but also for naïve search efforts, when students are simply doing genuine research or classwork. These nefarious companies use highly sophisticated search engine manipulation strategies to bait naïve student searchers onto their sites, thus enabling the potential to switch them to cheaters. Higher education institutions, armed with the specific details provided in this study, can use the strategies outlined in the discussion to directly and negatively impact on the success of these contract cheating services. [ABSTRACT FROM AUTHOR]

Digital channels have become the modern priority channels for promoting educational services. Integration into the international educational space, changes in the priority of communication channels, and digital transformation have led to the need to rethink existing approaches to the digital promotion of educational services of Kazakhstani universities. Particular interest in modernizing the promotion of educational services is caused by the fact that representatives of the new generation, Generation Z, are now becoming consumers of educational services. The basis of this study was the analysis of digital promotion channels as the main source of information and value, the needs of generation Z, who are now becoming the main consumers of the services of universities, including those in Kazakhstan. The purpose of the study is to determine the priorities for the digital promotion of educational services, to build a model for the digital promotion of university educational services, taking into account the needs of key consumers—generation Z, digital channels, and educational programs of the university. Hypothesis: priorities for digital promotion of educational services of universities should be differentiated depending on the popularity and technical parameters of the sites. The web resources similarweb.com, pagespeed.web.dev, wordstat.yandex.ru, sitechecker.pro were used as analysis tools. A correlation analysis of queries on university websites and the core values of generation Z was carried out. The closeness of the pairwise correlation between the total number of visits and website performance, search engine optimization, recommendations, and special features was determined. Based on the research, a model for promoting the university's educational services has been structured, highlighting key components: educational programs, digital technologies, and the values of generation Z. [ABSTRACT FROM AUTHOR]

The surge in online review platforms has reshaped how patients evaluate and choose surgical practitioners, underscoring a new dimension in healthcare transparency. This analysis dissects the multifaceted role of patients’ online reviews within the surgical practice, addressing their implications for surgeons and patient outcomes. Online reviews empower patients to voice their experiences, enabling prospective patients to make informed choices while fostering a sense of accountability among healthcare providers. Positive feedback can bolster a surgeon’s reputation and encourage practice growth, while constructive criticism provides valuable insights for service enhancements. However, the reliability of such reviews remains contentious due to their subjective nature, potential biases, and issues like sample size variability and anonymity. How surgeons can constructively engage with patient feedback online, navigating challenges like maintaining patient confidentiality and handling negative reviews with professionalism are all vital prerequisites of a modern-day surgical practice. Rating websites and social media platforms that host these reviews allows patients to freely express themselves but if the surgeon encounters something defamatory or abusive they can report to these platforms or take legal action. [ABSTRACT FROM AUTHOR]

Given the high rates of vaccine hesitancy, web-based medical misinformation about vaccination is a serious issue. We sought to understand the nature of Google searches leading to medical misinformation about vaccination, and guided by fuzzy-trace theory, the characteristics of misinformation pages related to comprehension, inference-making, and medical decision-making. We collected data from web pages presenting vaccination information. We assessed whether web pages presented medical misinformation, had an overarching gist, used narrative, and employed emotional appeals. We used Search Engine Optimization tools to determine the number of backlinks from other web pages, monthly Google traffic, and Google Keywords. We used Coh-Metrix to measure readability and Gist Inference Scores (GIS). For medical misinformation web pages, Google traffic and backlinks were heavily skewed with means of 138.8 visitors/month and 805 backlinks per page. Medical misinformation pages were significantly more likely than other vaccine pages to have backlinks from other pages, and significantly less likely to receive at least one visitor from Google searches per month. The top Google searches leading to medical misinformation were "the truth about vaccinations," "dangers of vaccination," and "pro con vaccines." Most frequently, pages challenged vaccine safety, with 32.7% having an overarching gist, 7.7% presenting narratives, and 17.3% making emotional appeals. Emotional appeals were significantly more common with medical misinformation than other high-traffic vaccination pages. Misinformation pages had a mean readability grade level of 11.5, and a mean GIS of – 0.234. Low GIS scores are a likely barrier to understanding gist, and are the "Achilles' heel" of misinformation pages. [ABSTRACT FROM AUTHOR]

We present an overview of the work of the Search Studies research group, focusing on commercial search engines from a user perspective. This encompasses studying what users of these search engines get to see on the result pages, how users interact with search engines, and the effect both have on knowledge acquisition in society. Our research combines search engine data analysis, by collecting and analysing data from commercial search engines (data science), with understanding information-seeking behaviour through conducting user studies in different settings (information science), ranging from large, representative online surveys to behavioural studies in the lab employing, amongst others, eye-tracking. [ABSTRACT FROM AUTHOR]

Apple changed the communications landscape with its 2007 introduction of the iPhone. In little more than a decade, most Americans have become smartphone owners. With more than 200,000 applications (apps) for education available in the Apple App Store, we can infer that many smartphone owners use their devices to learn. Several surveys of medical students reveal that apps do indeed enhance clinical knowledge and provide comparable training with textbooks. We launched our first iOS app, the CTisus iQuiz, in 2010 in response to the growing number of portable devices, and with an intention to grow alongside technology. We now have 17 apps available for free on the App Store, and present tips here on how to successfully develop apps for radiology education. Furthermore, we detail the steps necessary to advertise a new app and use analytics to assess how the app is performing. Generating apps for radiology can play an important role in continuing education. As the medical and education fields are integrating increasingly with technology, it is essential that we use technology to reach and interact with its users. Creating iOS applications and using analytics to understand how they meet the goals of the developer can expand the reach of the medical and education communities and render information more accessible on mobile devices on a large scale. [ABSTRACT FROM AUTHOR]

This paper presents the significant problem of so called shelf-warmers in the environment of electronic commerce. In the first part of the paper, the factors that have led to an increasing stock of shelf-warmers in online stores are discussed. These factors are divided into two groups: those that occur mainly in an online store environment and those not specifically associated with an online environment. One group of factors are related to the large number of items offered in an online store enabled by various models of inventory management (e.g. drop-shipping, 3PL, 4PL), the lack of a need to maintain costly space for the exposition of goods, the heterogeneity of online shoppers and the large number of returns from customers. The other group of factors concern incorrect forecasts of demand, inappropriate inventory control, choice of product assortment, pricing policies and the end of a product's life cycle. The problem of appropriately identifying shelf-warmers is then discussed and an index measuring the tendency of a good to be a shelf-warmer is proposed. Based on this, different methods for dealing with shelf-warmers are outlined in the form of the use of recommendation systems, search engine optimization and social media. The paper then presents an experiment conducted in a real business environment to assess the effectiveness of some of the aforementioned methods. This experiment resulted in the sale of approximately 33% of 160 selected shelf-warmers and over 50% from one group (number 8) where all the suggestions were implemented, within a period of 10 months. The results obtained show that the methods and recommendations proposed may help online stores to deal with the problem of shelf-warmers. [ABSTRACT FROM AUTHOR]

This study investigates the impact of search query intention when evaluating and managing search engine advertising. Specifically, we study whether the performance of a search engine advertising campaign depends on the informational, transactional, and navigational search intentions and also consider the appearance of an organic result alongside a search engine advertisement on the same search engine results page. Both, search intention and organic presence significantly affect some performance indicators of search engine advertising. Advertising ranking as well as click and conversion metrics are influenced by search intention and organic presence. Advertisers may consequently assign advertising budgets according to the dominant search intention in line with their advertising objectives. With the help of search engine optimization, advertisers can also influence the organic presence on the search engine results pages. In summary, theory and practice need to include search intention and organic presence in search engine advertising management. [ABSTRACT FROM AUTHOR]

Search Engine Optimization (SEO) is the process of managing web content in a manner that elevates page rankings in search engines. Among other sectors, academic world is one of the number-one categories for search based on the percentage of web traffic generated through search engine referrals. However, SEO includes a number of factors grouped into two as 'on page' and 'off page.' To obtain maximum benefit from SEO, relevant factors/criteria should be considered using multi-criteria decision making (MCDM) methods. The focus of this paper is to consider SEO criteria evaluation as a MCDM problem in which the criteria are in different priority levels and the criteria values take the form of hesitant fuzzy linguistic term sets to facilitate the elicitation of information in hesitate situations. A three-step solution approach is developed: (i) determination of 21 SEO criteria, such as page loading time, page size and meta-keyword (ii) prioritizing the criteria using hesitant fuzzy analytic hierarchy process, and (iii) ranking 70 Turkish websites of the industrial engineering departments using Technique for Order Preference by Similarity to Ideal Solution. The results show that trust flow and XML sitemap are the determinant criteria among others. Using the proposed method, web designers can approach SEO from weighted criteria perspective. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO) has been considered one of the most important techniques in internet marketing. This study establishes a decision model of search engine ranking for administrators to improve the performances of websites that satisfy users' needs. To probe into the interrelationship and influential weights among criteria of SEO and evaluate the gaps of performance to achieve the aspiration level in real world, this research utilizes hybrid modified multiple criteria decision-making models, including decision-making trial and evaluation laboratory (DEMATEL), DEMATEL-based analytic network process (called DANP), and VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The empirical findings discover that the criteria of SEO possessed a self-effect relationship based on DEMATEL technique. According to the influential network relation map (INRM), external website optimization is the top priority dimension that needs to be improved when implementing SEO. Among the six criteria for evaluation, meta tags is the most significant criterion influencing search engine ranking, followed by keywords and website design. The evaluation of search engine ranking reveals that the website with lowest gap would be the optimal example for administrators of websites to make high ranking website during the time that this study is executed. [ABSTRACT FROM AUTHOR]

This paper focuses on the historical and current day impact that search engine optimization (SEO) tools have in the context of increasing website traffic and, inevitably, online sales. It takes a special focus on the book publishing industry from the perspective of a small-scale publishing company (Company A). The paper accomplishes three things: to develop a working knowledge of SEOs and their impact on website sales and traffic, to highlight the differing usages of SEO in competitors to Company A, and to arrive at a well-informed implementation of SEO strategy for Company A. Our findings conclude that, although there are many, 3 factors are large determinants of a successful SEO campaign. These factors are: title tags, metatag descriptions, and keyword usage. As data and research are conveyed, this paper concludes with how Company A has implemented SEO into their website. [ABSTRACT FROM AUTHOR]

In this paper, the problem of economic load dispatch (ELD) is tackled using a recently introduced local search-based method called β-hill climbing optimizer. In a power system, the ELD problem is tackled by arranging a set of generation units’ outputs in a specific order to minimize the cost of the operating fuel and to match the power system load demand. This goal is achieved by satisfying all the power balance equality constraints and power output inequality constraints. β-hill climbing algorithm is a new local search algorithm which uses an intelligent stochastic operator called β-operator to escape the trap of local optima. The proposed method is evaluated using five real-world ELD benchmarks which vary in terms of complexity and size. The sensitivity analysis to study the effect of the proposed method parameters is conducted based on eight different convergence cases. The evaluation results of the proposed method are compared with 45 state-of-the-art methods using the same tested ELD benchmarks. Interestingly, the proposed method is able to produce a very closed-to-optimum result for almost all the tested ELD systems and the best result for one of them. [ABSTRACT FROM AUTHOR]

Information-centric networking (ICN) is gradually becoming the alternative approach to the traditional Internet architecture through enlightening information (content) distribution on the Internet with content names. The growing rate of Internet traffic has adapted the content-centric architectures to better serve the user requirement of accessing data. Routing disseminates preliminary topology and strategy information as well as long-term changes in them and computes the routing table to coordinate the forwarding procedure. A good routing scheme is essential to reduce delay and to enhance the performance of the network with an efficient searching of the desired content item. In this paper, we present a novel scheme for ICN which evaluates the path for searching the desired content item with an efficient lookup process and we call as Intelligent Routing (IR). Our implement IR scheme utilizes the concept of ternary digital search for making an efficient lookup and evaluates the satisfaction score for making a forwarding decision. The simulation results recommend that IR scheme can reliably accomplish the better performance across other hashing and routing approaches proposed in this field. [ABSTRACT FROM AUTHOR]

Monte Carlo tree search (MCTS) has recently been drawing great interest in the domain of planning and learning under uncertainty. One of the fundamental challenges is the trade-off between exploration and exploitation. To address this problem, we propose to balance between exploration and exploitation via posterior sampling in the contexts of Markov decision process (MDP) and partially observable Markov decision process (POMDP). Specifically, we treat the cumulative reward returned by taking an action from a search node in the MCTS search tree as a random variable following an unknown distribution. We parametrize this distribution by introducing necessary hidden parameters, and infer the posterior distribution of the hidden parameters in a Bayesian way. We further expand a node in the search tree by using Thompson sampling to select an action based on its posterior probability of being optimal. Following this idea, we develop Dirichlet-NormalGamma based Monte Carlo tree search (DNG-MCTS) and Dirichlet-Dirichlet-NormalGamma based partially observable Monte Carlo planning (D2NG-POMCP) algorithms respectively for Monte Carlo planning in MDPs and POMDPs. Experimental results show that the proposed algorithms outperform the state-of-the-art with better values on several benchmark problems. [ABSTRACT FROM AUTHOR]

Copyright of Journal für Ästhetische Chirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Today's wireless communication systems are highly susceptible to malicious jamming attacks and interference signals. These attacks are sent via communication link at the same frequency as the intended signal. The sent information is thus corrupted and the receiver is unable to correctly decode the sent data stream. This paper thereby seeks to come up with an anti-jamming system that is able to respond intelligently to such malicious attacks, block them and enable the correct decoding of a message. The proposed system objectives are to ensure that data throughput is at its highest, energy consumption at the lowest level and the bit error rate is maintained at a very low level during and after any jamming attack. The system is also able to identify jammer state from transmit power and respond accordingly. [ABSTRACT FROM AUTHOR]

This study seeks to evaluate the basic Priority 1 web accessibility of all college and university websites in the US (n = 3141). Utilizing web scraping and automated content analysis, the study establishes that even in the case of high-priority, simple-to-address accessibility requirements, colleges and universities generally fail to make their sites accessible. Results should be used to determine reasonable and simple steps for moving toward accessible design in institutional websites, which is necessary to ensure that institutional resources can be open and useable by all. [ABSTRACT FROM AUTHOR]

Copyright of Journal für Ästhetische Chirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Assembly sequence planning (ASP) plays an important role in intelligent manufacturing. As ASP is a non-deterministic polynomial (NP) hard problem, it is scarcely possible for a brute force approach to find out the optimal solution. Therefore, increasing meta-heuristic algorithms are introduced to solve the ASP problem. However, due to the discreteness and strong constraints of ASP problem, most meta-heuristics are unsuitable or inefficient to optimize it. Harmony search (HS) algorithm is one of the most suitable meta-heuristics for solving the problem. This paper proposes a dynamic parameter controlled harmony search (DPCHS) for solving ASP problems including a transformation of the assembly sequences by the largest position value (LPV) rule, initializing harmony memory with opposition-based learning (OBL) and designing dynamic parameters to control evolution. The key improvement to former work lies in the introduction of a dynamic pitch adjusting rate and bandwidth, which are adapting their value during the evolution. The performances of the DPCHS and the fixed harmony search algorithm are compared thoroughly in the case studies. Meantime, the efficiency of this algorithm in solving ASP problems is tested using two cases, and the results of other popular algorithms are compared. Furthermore, the DPCHS has been successfully applied to an industrial ASP problem. [ABSTRACT FROM AUTHOR]

With recent advances in networking technology, emerging networks continue to play an increasing role in the lives of most users. The Internet search and retrieval system is so powerful that it helps us to share information and perspectives from across the world. However, the threat of censorship exists on some centralized search engines, since all of their information is currently controlled by these sites administrators. The restriction and control of information are pervasive enough within governments and organizations to censor or intrude on even the most free and uncontrolled communication media. For this reason, the Peer-to-Peer (P2P) search and retrieval system is designed to resist censorship over the network. Nevertheless, its decentralized nature makes it very difficult to infer information that cannot be measured directly, such as the proportion of subverted and selfish nodes. Moreover, the situation is even more challenging when the network becomes extremely large. Hence, I propose a dynamic adaptive algorithm that can: 1) tackle the censorship and security issues; 2) determine the proportion of subverted and selfish nodes; 3) defend against malicious and selective forwarding attacks by appropriately adjusting the number of requests to ensure high match probability; 4) guarantee robustness and scalability even with different random networks and varied network sizes. In several experiments, I demonstrate that my algorithm can effectively and accurately estimate these metrics and manage the system, even when the network has a large proportion of malicious nodes, a large proportion of selfish nodes, or a mere partial view of network membership. [ABSTRACT FROM AUTHOR]

We present an efficient and easy-to-use methodology to predict-at design time-the availability of systems that support local recovery. Our analysis techniques work at the architectural level, where the software designer simply inputs the software modules' decomposition annotated with failure and repair rates. From this decomposition, we automatically generate an analytical model (a continuous-time Markov chain), from which an availability measure is then computed, in a completely automated way. A crucial step is the use of intermediate models in the input/output interactive Markov chain formalism, which makes our techniques efficient, mathematically rigorous, and easy to adapt. In particular, we use aggressive minimization techniques to keep the size of the generated state spaces small. We have applied our methodology on a realistic case study, namely the MPlayer open-source software. We have investigated four different decomposition alternatives and compared our analytical results with the measured availability on a running MPlayer. We found that our predicted results closely match the measured ones . [ABSTRACT FROM AUTHOR]

To improve the quality of software systems, one of the widely used techniques is refactoring defined as the process of improving the design of an existing system by changing its internal structure without altering the external behavior. The majority of existing refactoring work focuses mainly on the source code level. The suggestion of refactorings at the model level is more challenging due to the difficulty to evaluate: (a) the impact of the suggested refactorings applied to a diagram on other related diagrams to improve the overall system quality, (b) their feasibility, and (c) interdiagram consistency. We propose, in this paper, a novel framework that enables software designers to apply refactoring at the model level. To this end, we used a multi-objective evolutionary algorithm to find a trade-off between improving the quality of class and activity diagrams. The proposed multi-objective approach provides a multi-view for software designers to evaluate the impact of suggested refactorings applied to class diagrams on related activity diagrams in order to evaluate the overall quality, and check their feasibility and behavior preservation. The statistical evaluation performed on models extracted from four open-source systems confirms the efficiency of our approach. [ABSTRACT FROM AUTHOR]

Mining the latent intents behind search queries is critical for contemporary search engines. Therefore, there has been lots of effort on studying how to infer the intents of search queries via search engine query log. However, the task of query log-based intent inference is not trivial, since it involves cross-disciplinary knowledge of data modeling and data mining. In this paper, we tackle the problem of query intent inference by integrating multiple information sources in a seamless manner. We first propose a comprehensive data model called Search Query Log Structure (SQLS) that represents the relation between search queries via the User dimension, the URL dimension, the Session dimension and the Term dimension. In order to explore the effective ways of using such multidimensional information modeled by SQLS, we survey and compare three frameworks, namely Result-Oriented Framework, Laplacian-Oriented Framework and Topic-Oriented Framework, to infer the intents of search queries. Experimental results show that the three frameworks significantly outperform the state-of-the-art approach and meet the diverse requirements arising from different application scenarios. [ABSTRACT FROM AUTHOR]

The ROADEF/EURO challenge is a contest jointly organized by the French Operational Research and Decision Aid society (ROADEF) and the European Operational Research society (EURO). The contest appears on a regular basis since 1999 and always concerns an industrial optimization problem proposed by an industrial partner. Google proposed a subject for the ROADEF/EURO challenge 2012 (), presenting a complex and large-scale machine reassignment problem, where a set of processes assigned to a set of machines have to be reassigned (or moved) while balancing machine usage improvement and moving costs, under resource (more precisely CPU, RAM, disk) and operational constraints. The 2012 challenge edition has been an unprecedented success with 82 registered teams, 48 teams that actually sent a program for qualification, 30 qualified teams and 27 teams that sent a program for the final evaluation. This paper aims at introducing the Annals of Operations Research special issue by presenting the ROADEF/EURO challenge 2012 subject, as well as the methods of the finalist teams and their results. [ABSTRACT FROM AUTHOR]

This paper presents an external parallelization of Constraint Programming (CP) search tree mixing both static and dynamic partitioning. The principle of the parallelization is to partition the CP search tree into a set of sub-trees, then assign each sub-tree to one computing core in order to perform a local search using a sequential CP solver. In this context, static partitioning consists of decomposing the CP variables domains in order to split the CP search tree into a set of disjoint sub-trees to assign them to the cores. This strategy performs well without adding an extra cost to the parallel search, but the problem is the load imbalance between computing cores. On the other hand, dynamic partitioning is based on preservation of the search state to generate, dynamically or on demand, the sub-trees that are assigned to the cores. This strategy offers good load balancing between the different computing cores, but computing overcosts appear due to the initialisation of the search when a sub-tree is migrated from one core to another. In this paper, we propose a new partitioning strategy that mixes the static and dynamic partitioning and enjoys the benefits of each strategy. This mixed partitioning is designed to run on shared and distributed memory architectures. The performances obtained are illustrated by solving the CP problems modelled using the FlatZinc format and solved using the Google OR-Tools solver on top of the parallel Bobpp framework. [ABSTRACT FROM AUTHOR]

Unlike the IPO research, the SEO research has not examined the relation between insider behavior and R&D changes around offering dates. We tread new ground by examining this relation for SEOs. Like the IPO research, we find a positive association between changes in R&D and changes in insider ownership proportions. However, the comparison of SEOs with IPOs fails to generate complete agreement. For example, our insider results for SEOs are driven as much by five percent owners as by directors and officers, while IPO results are driven by directors and officers. Furthermore, while the IPO research and our SEO study both find evidence for the earnings manipulation hypothesis, our SEO results also offer support for the R&D signaling hypothesis. This latter support is due strictly to the five percent ownership group. Additionally, our insider results for SEOs indicate that endogeneity is just as pronounced during non-bubble periods as bubble periods. For IPOs, the evidence indicated endogeneity was a bubble period phenomenon. The main factor in explaining differences between SEOs and IPOs involves the five percent group consisting of large institutional owners who can amass large holdings and, along with directors and officers, control decision-making around SEOs. This decision-making cannot only reduce R&D to inflate earnings when insiders are lowering their ownership but can also increase R&D when insiders are prone to increase their ownership based on positive sentiments. For this positive situation, firms are more likely to be increasing capital expenditures with the five percent owners pushing this agenda. [ABSTRACT FROM AUTHOR]

Copyright of Journal für Ästhetische Chirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, the 2D Haar wavelet transform is the proposed analysis technique for HTTP traffic data. Web attacks are detected by two threshold operations applied to the wavelet coefficients of the 2D transform: one based on their median and the other on the best approximation method. The two proposed algorithms are validated through an extensive number of simulations, including comparisons with well-established techniques, confirming the effectiveness of the designed sensor. [ABSTRACT FROM AUTHOR]

A major challenge organizations face when hosting or moving their data to the Cloud is how to support complex queries over outsourced data while preserving their confidentiality. In principle, encryption-based systems can support querying encrypted data, but their high complexity has severely limited their practical use. In this paper, we propose an efficient yet secure secret sharing-based approach for outsourcing relational data to honest-but-curious data servers. The problem with using secret sharing in a data outsourcing scenario is how to efficiently search within randomly generated shares. We present multiple partitioning methods that enable clients to efficiently search among shared secrets while preventing inference attacks on the part of data servers, even if they can observe shares and queries. Also, we prove that with some of our partitioning methods the probability of finding a correspondence between a set of shares and their original values is almost equal to that of a random guess. We discuss query processing for different types of queries including equality, range, aggregation, projection, join, and update queries. Our extensive experimentation confirms the practicality and efficiency of our approach in terms of query execution time, storage, and communication overheads. [ABSTRACT FROM AUTHOR]

Copyright of Journal für Ästhetische Chirurgie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Non-negative matrix factorization (NMF) mainly focuses on the hidden pattern discovery behind a series of vectors for two-way data. Here, we propose a tensor decomposition model Tri-ONTD to analyze three-way data. The model aims to discover the common characteristics of a series of matrices and at the same time identify the peculiarity of each matrix, thus enabling the discovery of the cluster structure in the data. In particular, the Tri-ONTD model performs adaptive dimension reduction for tensors as it integrates the subspace identification (i.e., the low-dimensional representation with a common basis for a set of matrices) and the clustering process into a single process. The Tri-ONTD model can also be regarded as an extension of the Tri-factor NMF model. We present the detailed optimization algorithm and also provide the convergence proof. Experimental results on real-world datasets demonstrate the effectiveness of our proposed method in author clustering, image clustering, and image reconstruction. In addition, the results of our proposed model have sparse and localized structures. [ABSTRACT FROM AUTHOR]

A good or bad product quality rating can make or break an organization. However, the notion of 'quality' is often defined by an independent rating company that does not make the formula for determining the rank of a product publicly available. In order to invest wisely in product development, organizations are starting to use intelligent approaches for determining how funding for product development should be allocated. A critical step in this process is to 'reverse-engineer' a rating company's proprietary model as closely as possible. In this work, we provide a machine learning approach for this task, which optimizes a certain rank statistic that encodes preference information specific to quality rating data. We present experiments on data from a major quality rating company, and provide new methods for evaluating the solution. In addition, we provide an approach to use the reverse-engineered model to achieve a top ranked product in a cost-effective way. [ABSTRACT FROM AUTHOR]

Presenting and browsing image search results play key roles in helping users to find desired images from search results. Most existing commercial image search engines present them, dependent on a ranked list. However, such a scheme suffers from at least two drawbacks: inconvenience for consumers to get an overview of the whole result, and high computation cost to find desired images from the list. In this paper, we introduce a novel search result summarization approach and exploit this approach to further propose an interactive browsing scheme. The main contribution of this paper includes: (1) a dynamic absorbing random walk to find diversified representatives for image search result summarization; (2) a local scaled visual similarity evaluation scheme between two images through inspecting the relation between each image and other images; and (3) an interactive browsing scheme, based on a tree structure for organizing the images obtained from the summarization approach, to enable users to intuitively and conveniently browse the image search results. Quantitative experimental results and user study demonstrate the effectiveness of the proposed summarization and browsing approaches. [ABSTRACT FROM AUTHOR]

useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics related to the user's information need, or contain nuggets of information with little redundancy. Evaluation measures have been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation to investigate the practical implications of this for optimization and evaluation of retrieval systems. [ABSTRACT FROM AUTHOR]

Recently direct optimization of information retrieval (IR) measures has become a new trend in learning to rank. In this paper, we propose a general framework for direct optimization of IR measures, which enjoys several theoretical advantages. The general framework, which can be used to optimize most IR measures, addresses the task by approximating the IR measures and optimizing the approximated surrogate functions. Theoretical analysis shows that a high approximation accuracy can be achieved by the framework. We take average precision (AP) and normalized discounted cumulated gains (NDCG) as examples to demonstrate how to realize the proposed framework. Experiments on benchmark datasets show that the algorithms deduced from our framework are very effective when compared to existing methods. The empirical results also agree well with the theoretical results obtained in the paper. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to the solution of the well-studied conservation area network design problem (CANP), which is closely related to the classical set cover problem (SCP). The goal is to find the smallest amount of land that (when placed under conservation) will contain and protect a specified representation level of biodiversity resources. A new tabu search methodology is applied to an extension of the “basic” CANP which explicitly considers additional spatial requirements for improved conservation planning. The underlying search engine, modular adaptive self-learning tabu search (MASTS), incorporates state-of-the-art techniques including adaptive tabu search, dynamic neighborhood selection, and rule-based objectives. The ability to utilize intransitive orderings within a rule-based objective gives the search flexibility, improving solution quality while saving computation. This paper demonstrates how rule-based objectives can be used to design near optimal conservation area networks in which the individual conservation areas are well connected. The results represent a considerable improvement over classical techniques that do not consider spatial features. This paper provides an initial description of ConsNet, a comprehensive software package for systematic conservation planning. [ABSTRACT FROM AUTHOR]

Different terminals, with their unique combinations of liner services, yard layouts and equipment configurations, may find that different yard planning strategies work better for their scenarios. While an optimum yard plan can be found for each yard planning strategy, it is interesting to know which strategy gives the best plan. In designing an IT-based search engine to discover the best yard planning strategy and/or scenario, having a generic specification and solver is important, so that the whole solution space could be represented and searched. We design a generic problem specification with parameterised scenarios and yard planning strategies, and formulate a generic mathematical model that solves for the optimum weekly yard plan template for that given problem. A good run time of this generic model is extremely important as the model will be executed hundreds of times in the search engine. Experiments are conducted with the model. An interesting discovery is that re-modelling a set of integer variables into multiple binary variables improve the run time tremendously, and in some cases, outperform the relaxed original model. We also find that the strategy which allows sharing of yard space between services yield better utilization for yard space and rail mounted gantry handling capacity. [ABSTRACT FROM AUTHOR]

Most ranking algorithms are based on the optimization of some loss functions, such as the pairwise loss. However, these loss functions are often different from the criteria that are adopted to measure the quality of the web page ranking results. To overcome this problem, we propose an algorithm which aims at directly optimizing popular measures such as the Normalized Discounted Cumulative Gain and the Average Precision. The basic idea is to minimize a smooth approximation of these measures with gradient descent. Crucial to this kind of approach is the choice of the smoothing factor. We provide various theoretical analysis on that choice and propose an annealing algorithm to iteratively minimize a less and less smoothed approximation of the measure of interest. Results on the Letor benchmark datasets show that the proposed algorithm achieves state-of-the-art performances. [ABSTRACT FROM AUTHOR]

When solving real-world optimization problems, evolutionary algorithms often require a large number of fitness evaluations in order to converge to the global optima. Attempts have been made to find techniques to reduce the number of fitness function evaluations. We propose a novel framework in the context of multi-objective optimization where fitness evaluations are distributed by creating a limited number of adaptive spheres spanning the search space. These spheres move towards the global Pareto front as components of a swarm optimization system. We call this process localization. The contribution of the paper is a general framework for distributed evolutionary multi-objective optimization, in which the individuals in each sphere can be controlled by any existing evolutionary multi-objective optimization algorithm in the literature. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO) pertains to the activity of optimizing individual websites and webpages to get higher page rank in the search results. Websites are ordinarily optimized through back links, while individual webpages are optimized through specific keywords. In this paper, we propose a framework based on set of guidelines for keyword research analysis and back links generation. The proposed framework suggests that webpage content should be based on keywords and the site traffic should be monitored through referrals. We argue that if a website has some prevalent and pertinent keywords in its content and title along with a reasonable amount of back links that help watch the website traffic, then we can get better rank for the website in the search results. The framework also emphasizes that proper keyword selection and link building should be taken into account when developers and designers work on a software development project. The results of the study are reported here. [ABSTRACT FROM AUTHOR]

The article examines the importance of search engine optimization strategies to repair the online image of clients who have been besieged with unfavorable press. It reveals that the strategy improves the visibility of the users' research, and they have considered using external services to manage their online reputation. It also notes that maintaining a prominent online presence can help researchers to network with colleagues, share resources, raise money and communicate their work.

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