Query: Improving Retrieval Performance for Verbose Queries via Axiomatic Analysis of Term Discrimination Heuristic

Relevant passages:
A formal study of information retrieval heuristics <=SEP=> Empirical studies of information retrieval methods show that good retrieval performance is closely related to the use of various retrieval heuristics, such as TF-IDF weighting. One basic research question is thus what exactly are these "necessary" heuristics that seem to cause good retrieval performance. In this paper, we present a formal study of retrieval heuristics. We formally define a set of basic desirable constraints that any reasonable retrieval function should satisfy, and check these constraints on a variety of representative retrieval functions. We find that none of these retrieval functions satisfies all the constraints unconditionally. Empirical results show that when a constraint is not satisfied, it often indicates non-optimality of the method, and when a constraint is satisfied only for a certain range of parameter values, its performance tends to be poor when the parameter is out of the range. In general, we find that the empirical performance of a retrieval formula is tightly related to how well it satisfies these constraints. Thus the proposed constraints provide a good explanation of many empirical observations and make it possible to evaluate any existing or new retrieval formula analytically.

Information Retrieval with Verbose Queries <=SEP=> Recently, the focus of many novel search applications shifted from short keyword queries to verbose natural language queries. Examples include question answering systems and dialogue systems, voice search on mobile devices and entity search engines like Facebook's Graph Search or Google's Knowledge Graph. However the performance of textbook information retrieval techniques for such verbose queries is not as good as that for their shorter counterparts. Thus, effective handling of verbose queries has become a critical factor for adoption of information retrieval techniques in this new breed of search applications. Over the past decade, the information retrieval community has deeply explored the problem of transforming natural language verbose queries using operations like reduction, weighting, expansion, reformulation and segmentation into more effective structural representations. However, thus far, there was not a coherent and organized tutorial on this topic. In this tutorial, we aim to put together various research pieces of the puzzle, provide a comprehensive and structured overview of various proposed methods, and also list various application scenarios where effective verbose query processing can make a significant difference.

Some Simple Effective Approximations to the 2-Poisson Model for Probabilistic Weighted Retrieval <=SEP=> The 2-Poisson model for term frequencies is used to suggest ways of incorporating certain variables in probabilistic models for information retrieval. The variables concerned are within-document term tkequency, document length, and within-query term frequency. Simple weighting functions are developed, and tested on the TREC test collection. Considerable performance improvements (over simple inverse collection frequency weighting) are demonstrated.

A Statistical Approach to Mechanized Encoding and Searching of Literary Information <=SEP=> Written communication of ideas is carried out on the basis of statistical probability in that a writer chooses that level of subject specificity and that combination of words which he feels will convey the most meaning. Since this process varies among individuals and since similar ideas are therefore relayed at different levels of specificity and by means of different words, the problem of literature searching by machines still presents major difficulties. A statistical approach to this problem will be outlined and the various steps of a system based on this approach will be described. Steps include the statistical analysis of a collection of documents in a field of interest, the establishment of a set of "notions" and the vocabulary by which they are expressed, the compilation of a thesaurus-type dictionary and index, the automatic encoding of documents by machine with the aid of such a dictionary, the encoding of topological notations (such as branched structures), the recording of the coded information, the establishment of a searching pattern for finding pertinent information, and the programming of appropriate machines to carry out a search.

LDA-based document models for ad-hoc retrieval <=SEP=> Search algorithms incorporating some form of topic model have a long history in information retrieval. For example, cluster-based retrieval has been studied since the 60s and has recently produced good results in the language model framework. An approach to building topic models based on a formal generative model of documents, Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. In this paper, we study how to efficiently use LDA to improve ad-hoc retrieval. We propose an LDA-based document model within the language modeling framework, and evaluate it on several TREC collections. Gibbs sampling is employed to conduct approximate inference in LDA and the computational complexity is analyzed. We show that improvements over retrieval using cluster-based models can be obtained with reasonable efficiency.

Actuator fault detection and isolation system for an hexacopter <=SEP=> The problem of detection and isolation of actuator faults in hexacopter vehicles is address in this article. The aim is to detect and isolate possible faults which can occur on the vehicle actuation system (i.e. rotors and propellers). The dynamic nonlinear model of an hexarotor vehicle is first derived to build a model-based diagnosis system. Then a Thau observer is developed to estimate the states of the hexarotor and, on the usage of this information, to generate the needed set of residuals. The developed fault detection and isolation system is finally tested in a high fidelity hexacopter simulator in which different fault situations are considered.

Interleukin-17 inhibitors. A new era in treatment of psoriasis and other skin diseases <=SEP=> Psoriasis is a chronic skin disease caused by the excessive secretion of inflammatory cytokines. Available therapeutic options include biologic drugs such as tumor necrosis factor alpha inhibitors and interleukin 12/23 (IL-12/23) inhibitors. The recent discovery of IL-17, which contributes to development of psoriasis, opened new possibilities for further treatment modalities. Currently, one anti-IL17 biological agent is approved for the treatment - a fully human monoclonal antibody that targets IL-17A (secukinumab). Further clinical trials, including a humanized IgG4 specific for IL-17 (ixekizumab) and a fully human antibody that targets the IL-17 receptor A (brodalumab).

Personalized recommendation engine using HADOOP <=SEP=> More and more E-commerce Websites provide products with different prices which made it hard for consumers to find the products and services they want. In order to overcome this data overload, personalized recommendation engines are used to suggest products and to provide consumers with relevant data to help them decide which products to purchase. Recommendation engines are highly computational and hence ideal for the Hadoop Platform. This system aims at building a book recommendation engine which uses item or user based recommendation from Mahout for recommending books. It will analyze the data and give suggestions based on what similar users did and on the past transaction history of the user.

Multimodal depression recognition with dynamic visual and audio cues <=SEP=> In this paper, we present our system design for audio visual multi-modal depression recognition. To improve the estimation accuracy of the Beck Depression Inventory (BDI) score, besides the Low Level Descriptors (LLD) features and the Local Gabor Binary Pattern-Three Orthogonal Planes (LGBP-TOP) features provided by the 2014 Audio/Visual Emotion Challenge and Workshop (AVEC2014), we extract extra features to capture key behavioural changes associated with depression. From audio we extract the speaking rate, and from video, the head pose features, the Space-Temporal Interesting Point (STIP) features, and local kinematic features via the Divergence-Curl-Shear descriptors. These features describe body movements, and spatio-temporal changes within the image sequence. We also consider global dynamic features, obtained using motion history histogram (MHH), bag of words (BOW) features and vector of local aggregated descriptors (VLAD). To capture the complementary information within the used features, we evaluate two fusion systems - the feature fusion scheme, and the model fusion scheme via local linear regression (LLR). Experiments are carried out on the training set and development set of the Depression Recognition Sub-Challenge (DSC) of AVEC2014, we obtain root mean square error (RMSE) of 7.6697, and mean absolute error (MAE) of 6.1683 on the development set, which are better or comparable with the state of the art results of the AVEC2014 challenge.

Field assessment of Serious Games for Entrepreneurship in Higher Education <=SEP=> The potential of Serious Games (SGs) in education is widely recognized, and their adoption is significant in particular in children instruction. However, the deployment rate of SGs in higher education (HE) and their proper insertion in meaningful curricula is still quite low. This paper intends to make a first step in the direction of a better characterization of the pedagogical effectiveness of SGs in HE, by providing a qualitative analysis based on our field experience using three games for entrepreneurship, that we have studied in the light of two well established pedagogical paradigms, such as the Revised Bloom’s taxonomy and the Kolb’s Learning stages. In general, we observe that SGs address several goals of the Bloom’s taxonomy, in particular at the lower levels. Moreover, the cyclical nature of the business simulations can be directly mapped to the sequential steps described by Kolb. However, our analysis also shows that SGs have still to significantly evolve in order to become an effective and efficient tool that could be successfully and reliably used in HE. In the light of our experience, we also propose a schema for a proper integration of SGs by supporting different goals in different steps of a formal education process, Our study finally suggests directions for future research in the field.

The Effect of Music on the Human Stress Response <=SEP=> BACKGROUND
Music listening has been suggested to beneficially impact health via stress-reducing effects. However, the existing literature presents itself with a limited number of investigations and with discrepancies in reported findings that may result from methodological shortcomings (e.g. small sample size, no valid stressor). It was the aim of the current study to address this gap in knowledge and overcome previous shortcomings by thoroughly examining music effects across endocrine, autonomic, cognitive, and emotional domains of the human stress response.


METHODS
Sixty healthy female volunteers (mean age = 25 years) were exposed to a standardized psychosocial stress test after having been randomly assigned to one of three different conditions prior to the stress test: 1) relaxing music ('Miserere', Allegri) (RM), 2) sound of rippling water (SW), and 3) rest without acoustic stimulation (R). Salivary cortisol and salivary alpha-amylase (sAA), heart rate (HR), respiratory sinus arrhythmia (RSA), subjective stress perception and anxiety were repeatedly assessed in all subjects. We hypothesized that listening to RM prior to the stress test, compared to SW or R would result in a decreased stress response across all measured parameters.


RESULTS
The three conditions significantly differed regarding cortisol response (p = 0.025) to the stressor, with highest concentrations in the RM and lowest in the SW condition. After the stressor, sAA (p=0.026) baseline values were reached considerably faster in the RM group than in the R group. HR and psychological measures did not significantly differ between groups.


CONCLUSION
Our findings indicate that music listening impacted the psychobiological stress system. Listening to music prior to a standardized stressor predominantly affected the autonomic nervous system (in terms of a faster recovery), and to a lesser degree the endocrine and psychological stress response. These findings may help better understanding the beneficial effects of music on the human body.

Severely suppressed bone turnover: a potential complication of alendronate therapy. <=SEP=> Alendronate, an inhibitor of bone resorption, is widely used in osteoporosis treatment. However, concerns have been raised about potential oversuppression of bone turnover during long-term use. We report on nine patients who sustained spontaneous nonspinal fractures while on alendronate therapy, six of whom displayed either delayed or absent fracture healing for 3 months to 2 yr during therapy. Histomorphometric analysis of the cancellous bone showed markedly suppressed bone formation, with reduced or absent osteoblastic surface in most patients. Osteoclastic surface was low or low-normal in eight patients, and eroded surface was decreased in four. Matrix synthesis was markedly diminished, with absence of double-tetracycline label and absent or reduced single-tetracycline label in all patients. The same trend was seen in the intracortical and endocortical surfaces. Our findings raise the possibility that severe suppression of bone turnover may develop during long-term alendronate therapy, resulting in increased susceptibility to, and delayed healing of, nonspinal fractures. Although coadministration of estrogen or glucocorticoids appears to be a predisposing factor, this apparent complication can also occur with monotherapy. Our observations emphasize the need for increased awareness and monitoring for the potential development of excessive suppression of bone turnover during long-term alendronate therapy.

On Throughput Maximization of Time Division Multiple Access With Energy Harvesting Users <=SEP=> In this paper, we consider a multiple-access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. To avoid consuming extra energy on competition for the channel, the users are supposed to share the channel via time division multiple access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for real-time information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first study the optimal transmission scheme in the case where each node has an infinite-capacity battery. By optimization theory, we show that, to maximize the average system throughput, all the users should transmit at an identical optimal power, which solely depends on the energy harvesting rate per time slot. We then study the equal-power TDMA scheme in the case where each node is equipped with a battery of finite capacity. The system is formulated as a polling system consisting of multiple energy queues and one server. By the Markov chain modeling method, we derive the performance of equal-power TDMA in this case, in terms of the energy loss ratio and average system throughput. In addition, we develop an algorithm to efficiently compute the optimal transmission power in the finite-capacity battery case. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user, and analyze its system performance. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, whereas equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

TeamBeam - Meta-Data Extraction from Scientific Literature <=SEP=> An important aspect of the work of researchers as well as librarians is to manage collections of scientific literature. Social research networks, such as Mendeley and CiteULike, provide services that support this task. Meta-data plays an important role in providing services to retrieve and organise the articles. In such settings, meta-data is rarely explicitly provided, leading to the need for automatically extracting this valuable information. The TeamBeam algorithm analyses a scientific article and extracts out structured meta-data, such as the title, journal name and abstract, as well as information about the article’s authors (e.g. names, e-mail addresses, affiliations). The input of the algorithm is a set of blocks generated from the article text. A classification algorithm, which takes the sequence of the input into account, is then applied in two consecutive phases. In the evaluation the performance of the algorithm is compared against two heuristics and three existing meta-data extraction systems. Three different data sets with varying characteristics are used to assess the quality of the extraction results. TeamBeam performs well under testing and compares favourably with existing approaches.

DONet: A Data-Driven Overlay Network For Efficient Live Media Streaming <=SEP=> This paper presents DONet, a Data-driven Overlay Network for live media streaming. The core operations in DONet are very simple: every node periodically exchanges data availability information with a set of partners, and retrieves unavailable data from one or more partners, or supplies available data to partners. We emphasize three salient features of this data-driven design: 1) easy to implement , as it does not have to construct and maintain a complex global structure; 2) efficient, as data forwarding is dynamically determined according to data availability while not restricted by specific directions; and 3) robust and resilient , as the partnerships enable adaptive and quick switching among multi-suppliers. We show through analysis that DONet is scalable with bounded delay. We also address a set of practical challenges for realizing DONet, and propose an efficient memberand partnership management algorithm, together with an intelligent scheduling algorithm that achieves real-time and continuous distribution of streaming contents. We have extensively evaluated the performance of DONet over the PlanetLab. Our experiments, involving almost all the active PlanetLab nodes, demonstrate that DONet achieves quite good streaming quality even under formidable network conditions. Moreover, its control overhead and transmission delay are both kept at low levels. An Internet-based DONet implementation, calledCoolStreaming v.0.9, was released on May 30, 2004, which has attracted over 30000 distinct users with more than 4000 simultaneously being online at some peak times. We discuss the key issues toward designingCoolStreaming in this paper, and present several interesting observations from these large-scale tests; in particular, the larger the overlay size, the better the streaming quality it can deliver.

Unbalanced Three-Phase Optimal Power Flow for Smart Grids <=SEP=> Advanced distribution management system (DMS), an evolution of supervisory control and data acquisition obtained by extending its working principles from transmission to distribution, is the brain of a smart grid. Advanced DMS assesses smart functions in the distribution system and is also responsible for assessing control functions such as reactive dispatch, voltage regulation, contingency analysis, capability maximization, or line switching. Optimal power flow (OPF)-based tools can be suitably adapted to the requirements of smart distribution network and be employed in an advanced DMS framework. In this paper, the authors present a methodology for unbalanced three-phase OPF (TOPF) for DMS in a smart grid. In the formulation of the TOPF, control variables of the optimization problem are actual active load demand and reactive power outputs of microgenerators. The TOPF is based on a quasi-Newton method and makes use of an open-source three-phase unbalanced distribution load flow. Test results are presented on the IEEE 123-bus Radial Distribution Feeder test case.

Mental balance and well-being: building bridges between Buddhism and Western psychology. <=SEP=> Clinical psychology has focused primarily on the diagnosis and treatment of mental disease, and only recently has scientific attention turned to understanding and cultivating positive mental health. The Buddhist tradition, on the other hand, has focused for over 2,500 years on cultivating exceptional states of mental well-being as well as identifying and treating psychological problems. This article attempts to draw on centuries of Buddhist experiential and theoretical inquiry as well as current Western experimental research to highlight specific themes that are particularly relevant to exploring the nature of mental health. Specifically, the authors discuss the nature of mental well-being and then present an innovative model of how to attain such well-being through the cultivation of four types of mental balance: conative, attentional, cognitive, and affective.

Characterization of Traffic Analysis based video stream source identification <=SEP=> This paper presents the concept and characterization of Traffic Analysis (TA) for identifying sources of tunneled video streaming traffic. Such identification can be used in enterprise firewalls for blocking unauthorized viewing of tunneled video. We attempt to characterize and evaluate the impacts of the primary TA-influencing factors, namely, streaming protocol, codec, and the actual video content. A test environment is built to study the influence of those factors while Packet Size Distribution is used as the classification feature during Traffic Analysis. Analysis done on data obtained from the test environment has shown that the streaming protocols provide the most dominant source identification distinction. Also, while the codecs provide some weak distinctions, the influence of video content is marginal. In addition to in-laboratory experiments, a real-world verification for corroborating those observations is also made with commercial streaming service providers. Such long-haul experiments indicate that the end-to-end network conditions between the streaming server and video client can act as an additional influencing factor for traffic analysis towards video stream source identification. Overall, the results suggest the feasibility of TA for unknown video stream source identification with sufficiently diverse video examples.

Neural Multi-task Learning in Automated Assessment <=SEP=> Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.

Intellectual capital disclosure in Malaysia <=SEP=> This study investigates intellectual capital (IC) disclosure in Malaysian public listed companies. Specifically, this research examines the relationship between IC disclosure and companies' profitability, productivity, and firm size. We measure the IC disclosure using the IC disclosure index and I C disclosure frequency. The sample includes 255 firm-year observations from 2006-2008. The results from our content analysis confirm those of prior research that the IC disclosure has been increasing over time. The study finds that human capital is the most reported intellectual capital disclosure. In addition, we also find that firm size positively contribute to the disclosure of IC. The result of this study added to the body of literature by providing evidence on the relationship between IC disclosure and firms' characteristics.

Named Entity Recognition using an HMM-based Chunk Tagger <=SEP=> This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.

Heat-induced post-mortem defect of the skull simulating an exit gunshot wound of the calvarium. <=SEP=> A severely burned body was found in a burnt-out apartment following a house fire. At the death scene, police investigators noted a defect at the back of the head of the deceased. This defect perforated the occipital bone and showed external bevelling. To clarify whether this skull wound corresponded to a gunshot exit wound and for identification purposes, a medico-legal autopsy was ordered. External examination of the body showed intense charring with skin burned away and musculature exposed, fourth degree burns of the remaining skin and heat flexures of the limbs as well as heat-related rupture of both the abdominal cavity and chest wall (Fig. 1). An oval defect of the occipital bone with cratering of the external table of the cranium was found (Fig. 2). The defect measured 2 cm in diameter, was sharp-edged and the excavation of the outer table of the cranium showed no charring in contrast to the surrounding parts of the occipital bone (Fig. 3). Apart from this finding, radiology of the body before autopsy was unremarkable. At autopsy, gross sections of the mucosa of the trachea and main bronchi were covered by a thick layer of soot with soot particles also present within the esophageus and the stomach. The brain showed a reduced volume, flattening of gyri and obliteration of sulci but no signs of trauma. Except for the defect of the occipital bone, no other signs of ante-mortem or post-mortem trauma could be detected. The level of carboxyhemoglobin in heart blood was 65% and the blood alcohol level was 285 mg/dL.

A novel secure hash algorithm for public key digital signature schemes <=SEP=> Hash functions are the most widespread among all cryptographic primitives, and are currently used in multiple cryptographic schemes and in security protocols. This paper presents a new Secure Hash Algorithm called (SHA-192). It uses a famous secure hash algorithm given by the National Institute of Standard and Technology (NIST).The basic design of SHA192 is to have the output length of 192.The SHA-192 has been designed to satisfy the different level of enhanced security and to resist the advanced SHA attacks. The security analysis of the SHA-192 is compared to the old one given by NIST and gives more security and excellent results as shown in our discussion. In this paper the digital signature algorithm which is given by NIST has been modified using the proposed algorithms SHA-192. Using proposed SHA-192 hash algorithm a new digital signature schemes is also proposed. The SHA-192 can be used in many applications such s public key cryptosystem, digital signcryption, message authentication code, random generator and in security architecture of upcoming wireless devices like software defined radio etc.

A New Method for Yielding a Database of Location Fingerprints in WLAN <=SEP=> Location fingerprinting in wireless LAN (WLAN) positioning has received much attention recently. One of the key issues of this technique is generating the database of ‘fingerprints’. The conventional method does not utilise the spatial correlation of measurements sampled at adjacent reference points (RPs), and the ‘training’ process is not an easy task. A new method based on kriging is presented in this paper. An experiment shows that the new method can not only achieve more accurate estimation, but can also greatly reduce the workload and save training time. This can make the fingerprinting technique more flexible and easier to implement. Binghao Li, Yufei Wang, Andrew Dempster, Chris Rizos are with the School of Surveying and Spatial Information System, University of New South Wales, Sydney, Australia Hyung Keun Lee is with the School of Avionics and Telecommunication, Hankuk Aviation University, Kyunggi-do, Korea

Mapping the Americanization of English in space and time <=SEP=> As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.

Glial and neuronal control of brain blood flow <=SEP=> Blood flow in the brain is regulated by neurons and astrocytes. Knowledge of how these cells control blood flow is crucial for understanding how neural computation is powered, for interpreting functional imaging scans of brains, and for developing treatments for neurological disorders. It is now recognized that neurotransmitter-mediated signalling has a key role in regulating cerebral blood flow, that much of this control is mediated by astrocytes, that oxygen modulates blood flow regulation, and that blood flow may be controlled by capillaries as well as by arterioles. These conceptual shifts in our understanding of cerebral blood flow control have important implications for the development of new therapeutic approaches.

Investigating Players' Engagement, Immersion, and Experiences in Playing Pokémon Go <=SEP=> In recent years, Augmented Reality (AR) based mobile games have become popular among players. The Pokémon Go is one of the well-known examples. Although Pokémon Go game has become a global phenomenon, there is a limited study about players' experiences in the gameplay. Especially, little is known about players' engagement and immersion in playing AR-based mobile games in which physical movements in the real world are largely required to play the game. In this study, we conducted a pilot study with eight participants to investigate players' engagement, immersion, and experiences in the Pokémon Go gameplay. We report and discuss the preliminary findings from the study, which can help game designers understand players' experiences in the gameplay so that they can design more creative AR games in the future. Furthermore, the findings are also useful for future studies with larger sample size.

Advances on Interactive Machine Learning <=SEP=> Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.

Doubly Attentive Transformer Machine Translation <=SEP=> In this paper a doubly attentive transformer machine translation model (DATNMT) is presented in which a doubly-attentive transformer decoder normally joins spatial visual features obtained via pretrained convolutional neural networks, conquering any gap between image captioning and translation. In this framework, the transformer decoder figures out how to take care of source-language words and parts of an image freely by methods for two separate attention components in an Enhanced Multi-Head Attention Layer of doubly attentive transformer, as it generates words in the target language. We find that the proposed model can effectively exploit not just the scarce multimodal machine translation data, but also large general-domain textonly machine translation corpora, or imagetext image captioning corpora. The experimental results show that the proposed doublyattentive transformer-decoder performs better than a single-decoder transformer model, and gives the state-of-the-art results in the EnglishGerman multimodal machine translation task.

High Performance Clustering Based on the Similarity Join <=SEP=> A broad class of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms perform a similarity query for each point stored in the database. This approach causes serious performance degenerations if the considered data set does not fit into main memory. Usual cache strategies such as LRU fail because the locality of KDD algorithms is typically not high enough. In this paper, we propose to replace repeated similarity queries by the similarity join, a database primitive prevalent in multimedia database systems. We present a schema to transform query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. In order to perform a comprehensive experimental evaluation of our approach, we apply the proposed transformation to the clustering algorithm DBSCAN and to the hierarchical cluster structure analysis method OPTICS. Our technique allows the application of any similarity join algorithm, which may be based on index structures or not. In our experiments, we use a similarity join algorithm based on a variant of the X-tree. The experiments yield substantial performance improvements of our technique over the original algorithms. The traditional techniques are outperformed by factors of up to 33 for the X-tree and 54 for the R*-tree.