query: Multi-task Domain Adaptation for Sequence Tagging

positive: Speech recognition with deep recurrent neural networks Recurrent neural networks (RNNs) are a powerful model for sequential data. End-to-end training methods such as Connectionist Temporal Classification make it possible to train RNNs for sequence labelling problems where the input-output alignment is unknown. The combination of these methods with the Long Short-term Memory RNN architecture has proved particularly fruitful, delivering state-of-the-art results in cursive handwriting recognition. However RNN performance in speech recognition has so far been disappointing, with better results returned by deep feedforward networks. This paper investigates deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long range context that empowers RNNs. When trained end-to-end with suitable regularisation, we find that deep Long Short-term Memory RNNs achieve a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to our knowledge is the best recorded score.

positive: Marginalized Denoising Autoencoders for Domain Adaptation Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters — in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.

positive: Multi-Task Learning for Mental Health using Social Media Text We introduce initial groundwork for estimating suicide risk and mental health in a deep learning framework. By modeling multiple conditions, the system learns to make predictions about suicide risk and mental health at a low false positive rate. Conditions are modeled as tasks in a multitask learning (MTL) framework, with gender prediction as an additional auxiliary task. We demonstrate the effectiveness of multi-task learning by comparison to a well-tuned single-task baseline with the same number of parameters. Our best MTL model predicts potential suicide attempt, as well as the presence of atypical mental health, with AUC > 0.8. We also find additional large improvements using multi-task learning on mental health tasks with limited training data.

positive: Co-Training for Domain Adaptation Domain adaptation algorithms seek to generalize a model trained in a source domain to a new target domain. In many practical cases, the source and target distributions can differ substantially, and in some cases crucial target features may not have support in the source domain. In this paper we introduce an algorithm that bridges the gap between source and target domains by slowly adding to the training set both the target features and instances in which the current algorithm is the most confident. Our algorithm is a variant of co-training [7], and we name it CODA (Co-training for domain adaptation). Unlike the original co-training work, we do not assume a particular feature split. Instead, for each iteration of cotraining, we formulate a single optimization problem which simultaneously learns a target predictor, a split of the feature space into views, and a subset of source and target features to include in the predictor. CODA significantly out-performs the state-of-the-art on the 12-domain benchmark data set of Blitzer et al. [4]. Indeed, over a wide range (65 of 84 comparisons) of target supervision CODA achieves the best performance.

positive: Thumbs Up or Thumbs Down? Semantic Orientation Applied to Unsupervised Classification of Reviews This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs. A phrase has a positive semantic orientation when it has good associations (e.g., “subtle nuances”) and a negative semantic orientation when it has bad associations (e.g., “very cavalier”). In this paper, the semantic orientation of a phrase is calculated as the mutual information between the given phrase and the word “excellent” minus the mutual information between the given phrase and the word “poor”. A review is classified as recommended if the average semantic orientation of its phrases is positive. The algorithm achieves an average accuracy of 74% when evaluated on 410 reviews from Epinions, sampled from four different domains (reviews of automobiles, banks, movies, and travel destinations). The accuracy ranges from 84% for automobile reviews to 66% for movie reviews.

negative: A Three-Phase Current-Fed DC/DC Converter With Active Clamp for Low-DC Renewable Energy Sources This paper focuses on a new three-phase high power current-fed DC/DC converter with an active clamp. A three-phase DC/DC converter with high efficiency and voltage boosting capability is designed for use in the interface between a low-voltage fuel-cell source and a high-voltage DC bus for inverters. Zero-voltage switching in all active switches is achieved through using a common active clamp branch, and zero current switching in the rectifier diodes is achieved through discontinuous current conduction in the secondary side. Further, the converter is capable of increased power transfer due to its three-phase power configuration, and it reduces the RMS current per phase, thus reducing conduction losses. Moreover, a delta-delta connection on the three-phase transformer provides parallel current paths and reduces conduction losses in the transformer windings. An efficiency of above 93% is achieved through both improvements in the switching and through reducing conduction losses. A high voltage ratio is achieved by combining inherent voltage boost characteristics of the current-fed converter and the transformer turns ratio. The proposed converter and three-phase PWM strategy is analyzed, simulated, and implemented in hardware. Experimental results are obtained on a 500-W prototype unit, with all of the design verified and analyzed.

negative: A Planar Magic-T Using Substrate Integrated Circuits Concept In this letter, a slotline to substrate integrated waveguide transition is proposed for the development of substrate integrated circuits. The insertion loss of the back-to-back transition is less than 1 dB from 8.7 to 9.0 GHz. With this transition, a planar magic-T is studied and designed. Measured results indicate a very good performance of the fabricated magic-T is observed within the experimental frequency range of 8.4-9.4 GHz. The amplitude and phase imbalances are less than 0.2 dB and 1.5deg, respectively.

negative: Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration Vessel enhancement and segmentation aim at (binary) per-pixel segmentation considering certain local features as probabilistic vessel indicators. We propose a new methodology to combine any local probability map with local directional vessel information. The resulting global vessel segmentation is represented as a set of discrete streamlines populating the vascular structures and providing additional connectivity and geometric shape information. The streamlines are computed by numerical integration of the directional vector field that is obtained from the eigenanalysis of the local Hessian indicating the local vessel direction. The streamline representation allows for sophisticated post-processing techniques using the additional information to refine the segmentation result with respect to the requirements of the particular application such as image registration. We propose different post-processing techniques for hierarchical segmentation, centerline extraction, and catheter removal to be used for X-ray angiograms. We further demonstrate how the global approach is able to significantly improve the segmentation compared to conventional local Hessian-based approaches.

negative: Control of VSC connected to the grid through LCL-filter to achieve balanced currents Grid-connected voltage source converters (VSCs) are the heart of many applications with power quality concerns due to their reactive power controllability. However, the major drawback is their sensitivity to grid disturbances. Moreover, when VSCs are used in DG applications, voltage unbalance may be intolerant. The current protection may trip due to current unbalance or due to overcurrent. In this paper, a vector current controller for VSC connected to the grid through LCL-filter is developed with the main focus on producing symmetrical and balanced currents in case of unbalanced voltage dips. Implementing this controller helps the VSC system not to trip during voltage dips.

negative: Marketing malpractice: the cause and the cure. Ted Levitt used to tell his Harvard Business School students, "People don't want a quarter-inch drill--they want a quarter-inch hole." But 35 years later, marketers are still thinking in terms of products and ever-finer demographic segments. The structure of a market, as seen from customers' point of view, is very simple. When people need to get a job done, they hire a product or service to do it for them. The marketer's task is to understand what jobs periodically arise in customers' lives for which they might hire products the company could make. One job, the "I-need-to-send-this-from-here-to-there-with-perfect-certainty-as-fast-as-possible"job, has existed practically forever. Federal Express designed a service to do precisely that--and do it wonderfully again and again. The FedEx brand began popping into people's minds whenever they needed to get that job done. Most of today's great brands--Crest, Starbucks, Kleenex, eBay, and Kodak, to name a few-started out as just this kind of purpose brand. When a purpose brand is extended to products that target different jobs, it becomes an endorser brand. But, over time, the power of an endorser brand will surely erode unless the company creates a new purpose brand for each new job, even as it leverages the endorser brand as an overall marker of quality. Different jobs demand different purpose brands. New growth markets are created when an innovating company designs a product and then positions its brand on a job for which no optimal product yet exists. In fact, companies that historically have segmented and measured markets by product categories generally find that when they instead segment by job, their market is much larger (and their current share much smaller) than they had thought. This is great news for smart companies hungry for growth.

negative: Cubrick : A Scalable Distributed MOLAP Database for Fast Analytics This paper describes the architecture and design of Cubrick, a distributed multidimensional in-memory database that enables real-time data analysis of large dynamic datasets. Cubrick has a strictly multidimensional data model composed of dimensions, dimensional hierarchies and metrics, supporting sub-second MOLAP operations such as slice and dice, roll-up and drill-down over terabytes of data. All data stored in Cubrick is chunked in every dimension and stored within containers called bricks in an unordered and sparse fashion, providing high data ingestion ratios and indexed access through every dimension. In this paper, we describe details about Cubrick’s internal data structures, distributed model, query execution engine and a few details about the current implementation. Finally, we present some experimental results found in a first Cubrick deployment inside Facebook.

negative: A comparative study of HTM and other neural network models for online sequence learning with streaming data Online sequence learning from streaming data is one of the most challenging topics in machine learning. Neural network models represent promising candidates for sequence learning due to their ability to learn and recognize complex temporal patterns. In this paper, we present a comparative study of Hierarchical Temporal Memory (HTM), a neurally-inspired model, and other feedforward and recurrent artificial neural network models on both artificial and real-world sequence prediction algorithms. HTM and long-short term memory (LSTM) give the best prediction accuracy. HTM additionally demonstrates many other features that are desirable for real-world sequence learning, such as fast adaptation to changes in the data stream, robustness to sensor noise and fault tolerance. These features make HTM an ideal candidate for online sequence learning problems.

negative: Multirate Multimodal Video Captioning Automatically describing videos with natural language is a crucial challenge of video understanding. Compared to images, videos have specific spatial-temporal structure and various modality information. In this paper, we propose a Multirate Multimodal Approach for video captioning. Considering that the speed of motion in videos varies constantly, we utilize a Multirate GRU to capture temporal structure of videos. It encodes video frames with different intervals and has a strong ability to deal with motion speed variance. As videos contain different modality cues, we design a particular multimodal fusion method. By incorporating visual, motion, and topic information together, we construct a well-designed video representation. Then the video representation is fed into a RNN-based language model for generating natural language descriptions. We evaluate our approach for video captioning on "Microsoft Research - Video to Text" (MSR-VTT), a large-scale video benchmark for video understanding. And our approach gets great performance on the 2nd MSR Video to Language Challenge.

negative: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations In this paper, we describe the acquisition and contents of a large-scale Chinese face database: the CAS-PEAL face database. The goals of creating the CAS-PEAL face database include the following: 1) providing the worldwide researchers of face recognition with different sources of variations, particularly pose, expression, accessories, and lighting (PEAL), and exhaustive ground-truth information in one uniform database; 2) advancing the state-of-the-art face recognition technologies aiming at practical applications by using off-the-shelf imaging equipment and by designing normal face variations in the database; and 3) providing a large-scale face database of Mongolian. Currently, the CAS-PEAL face database contains 99 594 images of 1040 individuals (595 males and 445 females). A total of nine cameras are mounted horizontally on an arc arm to simultaneously capture images across different poses. Each subject is asked to look straight ahead, up, and down to obtain 27 images in three shots. Five facial expressions, six accessories, and 15 lighting changes are also included in the database. A selected subset of the database (CAS-PEAL-R1, containing 30 863 images of the 1040 subjects) is available to other researchers now. We discuss the evaluation protocol based on the CAS-PEAL-R1 database and present the performance of four algorithms as a baseline to do the following: 1) elementarily assess the difficulty of the database for face recognition algorithms; 2) preference evaluation results for researchers using the database; and 3) identify the strengths and weaknesses of the commonly used algorithms.

negative: Animal-assisted therapy for persons with aphasia: A pilot study. This study explored the effects and effectiveness of animal-assisted therapy (AAT) for persons with aphasia. Three men with aphasia from left-hemisphere strokes participated in this study. The men received one semester of traditional therapy followed by one semester of AAT. While both therapies were effective, in that each participant met his goals, no significant differences existed between test results following traditional speech-language therapy versus AAT. Results of a client-satisfaction questionnaire, however, indicated that each of the participants was more motivated, enjoyed the therapy sessions more, and felt that the atmosphere of the sessions was lighter and less stressed during AAT compared with traditional therapy.

negative: NoSQL Databases and Data Modeling Techniques for a Document-oriented NoSQL Database NoSQL databases are an important component of Big Data for storing and retrieving large volumes of data. Traditional Relational Database Management Systems (RDBMS) use the ACID theorem for data consistency, whereas NoSQL Databases use a non-transactional approach called BASE. RDBMS scale vertically and NoSQL Databases can scale both horizontally (sharding) and vertically. Four types of NoSQL databases are Document-oriented, Key-Value Pairs, Column-oriented and Graph. Data modeling for Document-oriented databases is similar to data modeling for traditional RDBMS during the conceptual and logical modeling phases. However, for a physical data model, entities can be combined (denormalized) by using embedding. What was once called a foreign key in a traditional RDBMS is now called a reference in a Documentoriented NoSQL database.

negative: Institutionally Distributed Deep Learning Networks Deep learning has become a promising approach for automated medical diagnoses. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In such cases, sharing a deep learning model is a more attractive alternative. The best method of performing such a task is unclear, however. In this study, we simulate the dissemination of learning deep learning network models across four institutions using various heuristics and compare the results with a deep learning model trained on centrally hosted patient data. The heuristics investigated include ensembling single institution models, single weight transfer, and cyclical weight transfer. We evaluated these approaches for image classification in three independent image collections (retinal fundus photos, mammography, and ImageNet). We find that cyclical weight transfer resulted in a performance (testing accuracy = 77.3%) that was closest to that of centrally hosted patient data (testing accuracy = 78.7%). We also found that there is an improvement in the performance of cyclical weight transfer heuristic with high frequency of weight transfer.

negative: A Chebyshev-Response Step-Impedance Phase-Inverter Rat-Race Coupler Directly on Lossy Silicon Substrate and Its Gilbert Mixer Application This paper focuses on the analysis and the design methodology of the step-impedance phase-inverter rat-race coupler on a silicon-based process. The issues of impedance limitation and bandwidth are discussed in detail. Our proposed concept utilizes a high silicon dielectric constant, phase-inverter structure, step-impedance technique, and Chebyshev response to make the rat-race coupler more compact (~ 64% reduction) and highly balanced over a wide operating bandwidth. Moreover, the inter-digital coplanar stripline used in the step-impedance section effectively reduces the characteristic impedance of the transmission line for large size shrinkage and insertion-loss reduction. The demonstrated step-impedance rat-race coupler directly on silicon substrate has 6- ~ 7-dB insertion loss from 5 to 15 GHz and small variations in amplitude/phase balance. Compared with our previous work, the proposed rat-race coupler achieves a 3-dB improvement in the insertion loss. Thus, a 0.13-μm CMOS Gilbert down-converter with a miniature phase-inverter rat-race coupler at the RF path for wideband single-to-differential signal conversion achieves a noise figure of 16 dB.

negative: The Visual Hull Concept for Silhouette-Based Image Understanding Many algorithms for both identifying and reconstructing a 3-D object are based on the 2-D silhouettes of the object. In general, identifying a nonconvex object using a silhouettebased approach implies neglecting some features of its surface as identification clues. The same features cannot be reconstructed by volume intersection techniques using multiple silhouettes of the object. This paper addresses the problem of finding which parts of a nonconvex object are relevant for silhouette-based image understanding. For this purpose, the geometric concept of visual hull of a 3-D object is introduced. The visual hull of a 3-D object S is the closest approximation of S that can be obtained with the volume intersection approach. An equivalent statement, relative to object identification, is that the visual hull of S is the maximal object silhouette-equivalent to S, i.e., which can be substituted for S without affecting any silhouette. Only the parts of the surface of S that also lie on the surface of the visual hull can be reconstructed or identified using silhouette-based algorithms. The visual hull of an object depends not only on the object itself but also on the region allowed to the viewpoint. Two main viewing regions can be considered, resulting in the external and internal visual hull. In the former case the viewing region is related to the convex hull of S, in the latter it is bounded by S itself. The internal visual hull also admits an interpretation not related to silhouettes: the features of the surface of S that is not coincident with the surface of the internal visual hull cannot be observed from any viewpoint lying outside the convex hull. After a general discussion of the visual hull and its properties, algorithms for computing the internal and external visual hulls of 2-D objects and 3-D planar face objects are presented and their complexity analyzed. In general, the visual hull of a 3-D planar face object turns out to be bounded by planar and curved patches. A precise statement of the concept of visual hull appears to be novel, as is the problem of its computation.

negative: Synthetic aperture radar at millimeter wavelength for UAV surveillance applications The airborne monitoring of scenes using unmanned aircrafts is becoming increasingly important. Several types of airborne sensors - in the optical, infrared or millimeter wave spectrum - are available for the different platforms. Beside the all-weather suitability of the sensors, the deployment scenarios, often also demand for the ability to look through dust clouds, smoke, and fog. The only sensor, which is capable to cope with such environmental restrictions and is able to deliver high-resolution images, is the synthetic aperture radar (SAR). In this paper we focus on miniaturized SAR systems which were developed and optimized for utilization in a UA V (unmanned aerial vehicle) with a low loading capacity. This not only requires a compact and light radar sensor but the processing also has to cope with the unstable flight conditions of a small aircraft. Therefore, a high-precision inertial measurement unit (IMU) and motion compensating SAR-algorithms are needed. Thanks to the utilization of a high transmit frequency of either 35 GHz or 94 GHz, the sensors are suitable for the detection of small-scale objects. A very high resolution of 15 cm × 15 cm can be achieved when using modern FMCW (frequency modulated continuous wave) generation with a high bandwidth (up to 1 GHz) in combination with small antennas.

negative: Big data, smart cities and city planning I define big data with respect to its size but pay particular attention to the fact that the data I am referring to is urban data, that is, data for cities that are invariably tagged to space and time. I argue that this sort of data are largely being streamed from sensors, and this represents a sea change in the kinds of data that we have about what happens where and when in cities. I describe how the growth of big data is shifting the emphasis from longer term strategic planning to short-term thinking about how cities function and can be managed, although with the possibility that over much longer periods of time, this kind of big data will become a source for information about every time horizon. By way of conclusion, I illustrate the need for new theory and analysis with respect to 6 months of smart travel card data of individual trips on Greater London's public transport systems.

negative: Bank Customer Credit Scoring by Using Fuzzy Expert System Granting banking facility is one of the most important parts of the financial supplies for each bank. So this activity becomes more valuable economically and always has a degree of risk. These days several various developed Artificial Intelligent systems like Neural Network, Decision Tree, Logistic Regression Analysis, Linear Discriminant Analysis and etc, are used in the field of granting facilities that each of this system owns its advantages and disadvantages. But still studying and working are needed to improve the accuracy and performance of them. In this article among other AI methods, fuzzy expert system is selected. This system is based on data and also extracts rules by using data. Therefore the dependency to experts is omitted and interpretability of rules is obtained. Validity of these rules could be confirmed or rejected by banking affair experts. For investigating the performance of proposed system, this system and some other methods were performed on various datasets. Results show that the proposed algorithm obtained better performance among the others.

negative: Maglev: A Fast and Reliable Software Network Load Balancer Maglev is Google’s network load balancer. It is a large distributed software system that runs on commodity Linux servers. Unlike traditional hardware network load balancers, it does not require a specialized physical rack deployment, and its capacity can be easily adjusted by adding or removing servers. Network routers distribute packets evenly to the Maglev machines via Equal Cost Multipath (ECMP); each Maglev machine then matches the packets to their corresponding services and spreads them evenly to the service endpoints. To accommodate high and ever-increasing traffic, Maglev is specifically optimized for packet processing performance. A single Maglev machine is able to saturate a 10Gbps link with small packets. Maglev is also equipped with consistent hashing and connection tracking features, to minimize the negative impact of unexpected faults and failures on connection-oriented protocols. Maglev has been serving Google’s traffic since 2008. It has sustained the rapid global growth of Google services, and it also provides network load balancing for Google Cloud Platform.

negative: Augmented Reality Tracking in Natural Environments Tracking, or camera pose determination, is the main technical challenge in creating augmented realities. Constraining the degree to which the environment may be altered to support tracking heightens the challenge. This paper describes several years of work at the USC Computer Graphics and Immersive Technologies (CGIT) laboratory to develop self-contained, minimally intrusive tracking systems for use in both indoor and outdoor settings. These hybrid-technology tracking systems combine vision and inertial sensing with research in fiducial design, feature detection, motion estimation, recursive filters, and pragmatic engineering to satisfy realistic application requirements.

negative: Word Epoch Disambiguation: Finding How Words Change Over Time In this paper we introduce the novel task of “word epoch disambiguation,” defined as the problem of identifying changes in word usage over time. Through experiments run using word usage examples collected from three major periods of time (1800, 1900, 2000), we show that the task is feasible, and significant differences can be observed between occurrences of words in different periods of time.

negative: How to model fake news Over the past three years it has become evident that fake news is a danger to democracy. However, until now there has been no clear understanding of how to define fake news, much less how to model it. This paper addresses both these issues. A definition of fake news is given, and two approaches for the modelling of fake news and its impact in elections and referendums are introduced. The first approach, based on the idea of a representative voter, is shown to be suitable to obtain a qualitative understanding of phenomena associated with fake news at a macroscopic level. The second approach, based on the idea of an election microstructure, describes the collective behaviour of the electorate by modelling the preferences of individual voters. It is shown through a simulation study that the mere knowledge that pieces of fake news may be in circulation goes a long way towards mitigating the impact of fake news.

negative:
Penoscrotal plication as a uniform approach to reconstruction of penile curvature. OBJECTIVE
To present our 4-year experience of using a minimally invasive technique, penoscrotal plication (PSP), as a uniform treatment for men with debilitating penile curvature resulting from Peyronie's disease.

PATIENTS AND METHODS
In 48 men (median age 58.7 years) with penile curvature the penis was reconstructed by imbricating the tunica albuginea opposite the curvature with multiple nonabsorbable sutures. All patients, regardless of the degree or direction of curvature, were approached through a small penoscrotal incision made without degloving the penis. Detailed measurements of penile shaft angle and stretched penile length were recorded and analysed before and after reconstruction, and the numbers of sutures required for correction were documented.

RESULTS
Nearly all patients had dorsal and/or lateral deformities that were easily corrected via a ventral penoscrotal incision. The median (range) degree of correction was 28 (18-55) degrees and number of sutures used was 6 (4-17). Stretched penile length measurements before and after plication showed no significant difference. A single PSP procedure was successful in 45/48 (93%) patients; two were dissatisfied with the correction, one having repeat plication and the other a penile prosthesis; one other required a suture release for pain.

CONCLUSIONS
PSP is safe and effective and should be considered even for cases with severe or biplanar curvature.

negative:
Non-sexually related acute genital ulcers in 13 pubertal girls: a clinical and microbiological study. OBJECTIVE
To describe the clinical and microbiological features of acute genital ulcers (AGU), which have been reported in virgin adolescents, predominantly in girls.

DESIGN
Descriptive study. We collected data on the clinical features, sexual history, blood cell count, biochemistry, microbiological workup, and 1-year follow-up.

SETTING
Departments of dermatology of 3 university hospitals in Paris. Patients Thirteen immunocompetent female patients with a first flare of non-sexually transmitted AGU.

MAIN OUTCOME MEASURES
Clinical and microbiological data, using a standardized form.

RESULTS
Mean age was 16.6 years (range, 11-19 years). Eleven patients denied previous sexual contact. A fever or flulike symptoms preceded AGU in 10 of the 13 patients (77%), with a mean delay of 3.8 days before the AGU onset (range, 0-10 days). The genital ulcers were bilateral in 10 patients. The final diagnosis was Epstein-Barr virus primary infection in 4 patients (31%) and Behçet disease in 1 patient (8%). No other infectious agents were detected in this series.

CONCLUSIONS
We recommend serologic testing for Epstein-Barr virus with IgM antibodies to viral capsid antigens in non-sexually related AGU in immunocompetent patients. Further microbiological studies are required to identify other causative agents.

negative: Tangible Query Interfaces: Physically Constrained Tokens for Manipulating Database Queries We present a new approach for using physically constrained tokens to express, manipulate, and visualize parameterized database queries. This method extends tangible interfaces to enable interaction with large aggregates of information. We describe two interface prototypes that use physical tokens to represent database parameters. These tokens are manipulated upon physical constraints, which map compositions of tokens onto interpretations including database queries, views, and Boolean operations. We propose a framework for “ token + constraint” interfaces, and compare one of our prototypes with a comparable graphical interface in a preliminary user study. † Current affiliation: Visualization Dept., Zuse Institute Berlin (ZIB), ullmer@zib.de †† Current affiliation: Computer Science Dept., Tufts University, jacob@cs.tufts.edu

negative: OpenIoT: An open service framework for the Internet of Things The Internet of Things (IoT) has been a hot topic for the future of computing and communication. It will not only have a broad impact on our everyday life in the near future, but also create a new ecosystem involving a wide array of players such as device developers, service providers, software developers, network operators, and service users. In this paper, we present an open service framework for the Internet of Things, facilitating entrance into the IoT-related mass market, and establishing a global IoT ecosystem with the worldwide use of IoT devices and softwares. We expect that the open IoT service framework we proposed will play an important role in the widespread adoption of the Internet of Things in our everyday life, enhancing our quality of life with a large number of innovative applications and services, but also offering endless opportunities to all of the stakeholders in the world of information and communication technologies.

negative: DocTag2Vec: An Embedding Based Multi-label Learning Approach for Document Tagging Tagging news articles or blog posts with relevant tags from a collection of predefined ones is coined as document tagging in this work. Accurate tagging of articles can benefit several downstream applications such as recommendation and search. In this work, we propose a novel yet simple approach called DocTag2Vec to accomplish this task. We substantially extend Word2Vec and Doc2Vec—two popular models for learning distributed representation of words and documents. In DocTag2Vec, we simultaneously learn the representation of words, documents, and tags in a joint vector space during training, and employ the simple k-nearest neighbor search to predict tags for unseen documents. In contrast to previous multi-label learning methods, DocTag2Vec directly deals with raw text instead of provided feature vector, and in addition, enjoys advantages like the learning of tag representation, and the ability of handling newly created tags. To demonstrate the effectiveness of our approach, we conduct experiments on several datasets and show promising results against state-of-the-art methods.

negative: Replicability Analysis for Natural Language Processing: Testing Significance with Multiple Datasets With the ever growing amount of textual data from a large variety of languages, domains, and genres, it has become standard to evaluate NLP algorithms on multiple datasets in order to ensure a consistent performance across heterogeneous setups. However, such multiple comparisons pose significant challenges to traditional statistical analysis methods in NLP and can lead to erroneous conclusions. In this paper we propose a Replicability Analysis framework for a statistically sound analysis of multiple comparisons between algorithms for NLP tasks. We discuss the theoretical advantages of this framework over the current, statistically unjustified, practice in the NLP literature, and demonstrate its empirical value across four applications: multi-domain dependency parsing, multilingual POS tagging, cross-domain sentiment classification and word similarity prediction.

negative: Stanford's Graph-based Neural Dependency Parser at the CoNLL 2017 Shared Task This paper describes the neural dependency parser submitted by Stanford to the CoNLL 2017 Shared Task on parsing Universal Dependencies. Our system uses relatively simple LSTM networks to produce part of speech tags and labeled dependency parses from segmented and tokenized sequences of words. In order to address the rare word problem that abounds in languages with complex morphology, we include a character-based word representation that uses an LSTM to produce embeddings from sequences of characters. Our system was ranked first according to all five relevant metrics for the system: UPOS tagging (93.09%), XPOS tagging (82.27%), unlabeled attachment score (81.30%), labeled attachment score (76.30%), and content word labeled attachment score (72.57%).

negative: Interactive Block Games for Assessing Children's Cognitive Skills: Design and Preliminary Evaluation Background: This paper presents design and results from preliminary evaluation of Tangible Geometric Games (TAG-Games) for cognitive assessment in young children. The TAG-Games technology employs a set of sensor-integrated cube blocks, called SIG-Blocks, and graphical user interfaces for test administration and real-time performance monitoring. TAG-Games were administered to children from 4 to 8 years of age for evaluating preliminary efficacy of this new technology-based approach. Methods: Five different sets of SIG-Blocks comprised of geometric shapes, segmented human faces, segmented animal faces, emoticons, and colors, were used for three types of TAG-Games, including Assembly, Shape Matching, and Sequence Memory. Computational task difficulty measures were defined for each game and used to generate items with varying difficulty. For preliminary evaluation, TAG-Games were tested on 40 children. To explore the clinical utility of the information assessed by TAG-Games, three subtests of the age-appropriate Wechsler tests (i.e., Block Design, Matrix Reasoning, and Picture Concept) were also administered. Results: Internal consistency of TAG-Games was evaluated by the split-half reliability test. Weak to moderate correlations between Assembly and Block Design, Shape Matching and Matrix Reasoning, and Sequence Memory and Picture Concept were found. The computational measure of task complexity for each TAG-Game showed a significant correlation with participants' performance. In addition, age-correlations on TAG-Game scores were found, implying its potential use for assessing children's cognitive skills autonomously.

negative: Predicting Depth, Surface Normals and Semantic Labels with a Common Multi-scale Convolutional Architecture In this paper we address three different computer vision tasks using a single basic architecture: depth prediction, surface normal estimation, and semantic labeling. We use a multiscale convolutional network that is able to adapt easily to each task using only small modifications, regressing from the input image to the output map directly. Our method progressively refines predictions using a sequence of scales, and captures many image details without any superpixels or low-level segmentation. We achieve state-of-the-art performance on benchmarks for all three tasks.

negative:
Software-specific part-of-speech tagging: an experimental study on stack overflow Part-of-speech (POS) tagging performance degrades on out-of-domain data due to the lack of domain knowledge. Software engineering knowledge, embodied in textual documentations, bug reports and online forum discussions, is expressed in natural language, but is full of domain terms, software entities and software-specific informal languages. Such software texts call for software-specific POS tagging. In the software engineering community, there have been several attempts leveraging POS tagging technique to help solve software engineering tasks. However, little work is done for POS tagging on software natural language texts.
 In this paper, we build a software-specific POS tagger, called S-POS, for processing the textual discussions on Stack Overflow. We target at Stack Overflow because it has become an important developer-generated knowledge repository for software engineering. We define a POS tagset that is suitable for describing software engineering knowledge, select corpus, develop a custom tokenizer, annotate data, design features for supervised model training, and demonstrate that the tagging accuracy of S-POS outperforms that of the Stanford POS Tagger when tagging software texts. Our work presents a feasible roadmap to build software-specific POS tagger for the socio-professional contents on Stack Overflow, and reveals challenges and opportunities for advanced software-specific information extraction.

negative: Tag suggestion and localization in user-generated videos based on social knowledge Nowadays, almost any web site that provides means for sharing user-generated multimedia content, like Flickr, Facebook, YouTube and Vimeo, has tagging functionalities to let users annotate the material that they want to share. The tags are then used to retrieve the uploaded content, and to ease browsing and exploration of these collections, e.g. using tag clouds. However, while tagging a single image is straightforward, and sites like Flickr and Facebook allow also to tag easily portions of the uploaded photos, tagging a video sequence is more cumbersome, so that users just tend to tag the overall content of a video. Moreover, the tagging process is completely manual, and often users tend to spend as few time as possible to annotate the material, resulting in a sparse annotation of the visual content. A semi-automatic process, that helps the users to tag a video sequence would improve the quality of annotations and thus the overall user experience. While research on image tagging has received a considerable attention in the latest years, there are still very few works that address the problem of automatically assigning tags to videos, locating them temporally within the video sequence. In this paper we present a system for video tag suggestion and temporal localization based on collective knowledge and visual similarity of frames. The algorithm suggests new tags that can be associated to a given keyframe exploiting the tags associated to videos and images uploaded to social sites like YouTube and Flickr and visual features.


query: Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends

positive: Social network analysis: a powerful strategy, also for the information sciences Social network analysis (SNA) is not a formal theory in sociology but rather a strategy for investigating social structures. As it is an idea that can be applied in many fields, we study, in particular, its influence in the information sciences. Information scientists study publication, citation and co-citation networks, collaboration structures and other forms of social interaction networks. Moreover, the Internet represents a social network of an unprecedented scale. We are convinced that in all these studies social network analysis can successfully be applied. SNA is further related to recent theories concerning the free market economy, geography and transport networks. The growth of SNA is documented and a co-author network of SNA is drawn. Centrality measures of this network are calculated. * To whom all correspondence should be addressed.

positive: Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy We are developing corpus-based techniques for identifying semantic relations at an intermediate level of description (more specific than those used in case frames, but more general than those used in traditional knowledge representation systems). In this paper we describe a classification algorithm for identifying relationships between two-word noun compounds. We find that a very simple approach using a machine learning algorithm and a domain-specific lexical hierarchy successfully generalizes from training instances, performing better on previously unseen words than a baseline consisting of training on the words themselves.

positive: Studying Online Social Networks What is Social Network Analysis? The Social Network Approach Units of Analysis Relations Ties Multiplexity Composition Beyond the Tie Social Networks Two Views: Ego-Centered and Whole Networks Network Characteristics Partitioning Networks Groups STUDYING ONLINE SOCIAL NETWORKS, by Laura Garton... http://jcmc.indiana.edu/vol3/issue1/garton.html 1 of 32 9/21/11 11:24 AM Positional Analysis Networks of Networks Placing CMC in Context Collecting Network Data for CMC Studies Selecting a Sample Collecting Data How are Network Data Analyzed? Ego-Centered Analysis Whole Network Analysis Conclusions References About the Authors

positive: MAPLSC: A novel multi-class classifier for medical diagnosis Analysis of clinical records contributes to the Traditional Chinese Medicine (TCM) experience expansion and techniques promotion. More than two diagnostic classes (diagnostic syndromes) in the clinical records raise a popular data mining problem: multi-value classification. In this paper, we propose a novel multi-class classifier, named Multiple Asymmetric Partial Least Squares Classifier (MAPLSC). MAPLSC attempts to be robust facing imbalanced data distribution in the multi-value classification. Elaborated comparisons with other seven state-of-the-art methods on two TCM clinical datasets and four public microarray datasets demonstrate MAPLSC's remarkable improvements.

negative: Template-Based Information Extraction without the Templates Standard algorithms for template-based information extraction (IE) require predefined template schemas, and often labeled data, to learn to extract their slot fillers (e.g., an embassy is the Target of a Bombing template). This paper describes an approach to template-based IE that removes this requirement and performs extraction without knowing the template structure in advance. Our algorithm instead learns the template structure automatically from raw text, inducing template schemas as sets of linked events (e.g., bombings include detonate, set off, and destroy events) associated with semantic roles. We also solve the standard IE task, using the induced syntactic patterns to extract role fillers from specific documents. We evaluate on the MUC-4 terrorism dataset and show that we induce template structure very similar to handcreated gold structure, and we extract role fillers with an F1 score of .40, approaching the performance of algorithms that require full knowledge of the templates.

negative: Emerging Trends in Smart Home Security, Privacy, and Digital Forensics Technology integration is becoming an impetus to everyday lives. This new interconnected world can be found from our most private spaces to the public ones. Smart homes, which is the use of Internet of Things (IoT) within a home, has become the utmost concern in the security and privacy domain. In this paper, we review the current literature from both research and practice and offer five research trends for security and privacy in smart homes: potential for remote security breaches, risks for smart home devices, privacy violations, infrastructure vulnerabilities, and digital forensics challenges. We integrated these five trends in a conceptual model showcasing their roles in the rapidly changing IoT landscape. Combining the trends and smart forensics, we elucidate the bimodal lenses in security and privacy: preventive and investigative. These discussions offer research directions and practitioners’ implications for further development of a safe and secured smart home.

negative: Automated Text Summarization In SUMMARIST SUMMARIST is an attempt to create a robust automated text summarization system, based on the ‘equation’: summarization = topic identification + interpretation + generation. Each of these stages contains several independent modules, many of them trained on large corpora of text. We describe the system’s architecture and provide details of some of its modules.

negative: Object Recognition using 3D SIFT in Complex CT Volumes The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.

negative: COA: finding novel patents through text analysis In recent years, the number of patents filed by the business enterprises in the technology industry are growing rapidly, thus providing unprecedented opportunities for knowledge discovery in patent data. One important task in this regard is to employ data mining techniques to rank patents in terms of their potential to earn money through licensing. Availability of such ranking can substantially reduce enterprise IP (Intellectual Property) management costs. Unfortunately, the existing software systems in the IP domain do not address this task directly. Through our research, we build a patent ranking software, named COA (Claim Originality Analysis) that rates a patent based on its value by measuring the recency and the impact of the important phrases that appear in the "claims" section of a patent. Experiments show that COA produces meaningful ranking when comparing it with other indirect patent evaluation metrics--citation count, patent status, and attorney's rating. In reallife settings, this tool was used by beta-testers in the IBM IP department. Lawyers found it very useful in patent rating, specifically, in highlighting potentially valuable patents in a patent cluster. In this article, we describe the ranking techniques and system architecture of COA. We also present the results that validate its effectiveness.

negative: Honey: A Biologic Wound Dressing. Honey has been used as a wound dressing for thousands of years, but only in more recent times has a scientific explanation become available for its effectiveness. It is now realized that honey is a biologic wound dressing with multiple bioactivities that work in concert to expedite the healing process. The physical properties of honey also expedite the healing process: its acidity increases the release of oxygen from hemoglobin thereby making the wound environment less favorable for the activity of destructive proteases, and the high osmolarity of honey draws fluid out of the wound bed to create an outflow of lymph as occurs with negative pressure wound therapy. Honey has a broad-spectrum antibacterial activity, but there is much variation in potency between different honeys. There are 2 types of antibacterial activity. In most honeys the activity is due to hydrogen peroxide, but much of this is inactivated by the enzyme catalase that is present in blood, serum, and wound tissues. In manuka honey, the activity is due to methylglyoxal which is not inactivated. The manuka honey used in wound-care products can withstand dilution with substantial amounts of wound exudate and still maintain enough activity to inhibit the growth of bacteria. There is good evidence for honey also having bioactivities that stimulate the immune response (thus promoting the growth of tissues for wound repair), suppress inflammation, and bring about rapid autolytic debridement. There is clinical evidence for these actions, and research is providing scientific explanations for them.

negative: Fast Component-Based QR Code Detection in Arbitrarily Acquired Images Quick Response (QR) codes are a type of 2D barcode that is becoming very popular, with several application possibilities. Since they can encode alphanumeric characters, a rich set of information can be made available through encoded URL addresses. In particular, QR codes could be used to aid visually impaired and blind people to access web based voice information systems and services, and autonomous robots to acquire context-relevant information. However, in order to be decoded, QR codes need to be properly framed, something that robots, visually impaired and blind people will not be able to do easily without guidance. Therefore, any application that aims assisting robots or visually impaired people must have the capability to detect QR codes and guide them to properly frame the code. A fast component-based two-stage approach for detecting QR codes in arbitrarily acquired images is proposed in this work. In the first stage, regular components present at three corners of the code are detected, and in the second stage geometrical restrictions among detected components are verified to confirm the presence of a code. Experimental results show a high detection rate, superior to 90 %, at a fast speed compatible with real-time applications.

negative: Detection and Analysis of 2016 US Presidential Election Related Rumors on Twitter The 2016 U.S. presidential election has witnessed the major role of Twitter in the year’s most important political event. Candidates used this social media platform extensively for online campaigns. Meanwhile, social media has been filled with rumors, which might have had huge impacts on voters’ decisions. In this paper, we present a thorough analysis of rumor tweets from the followers of two presidential candidates: Hillary Clinton and Donald Trump. To overcome the difficulty of labeling a large amount of tweets as training data, we detect rumor tweets by matching them with verified rumor articles. We analyze over 8 million tweets collected from the followers of the two candidates. Our results provide answers to several primary concerns about rumors in this election, including: which side of the followers posted the most rumors, who posted these rumors, what rumors they posted, and when they posted these rumors. The insights of this paper can help us understand the online rumor behaviors in American politics.

negative: The max-min hill-climbing Bayesian network structure learning algorithm We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and search-and-score techniques in a principled and effective way. It first reconstructs the skeleton of a Bayesian network and then performs a Bayesian-scoring greedy hill-climbing search to orient the edges. In our extensive empirical evaluation MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms, namely the PC, Sparse Candidate, Three Phase Dependency Analysis, Optimal Reinsertion, Greedy Equivalence Search, and Greedy Search. These are the first empirical results simultaneously comparing most of the major Bayesian network algorithms against each other. MMHC offers certain theoretical advantages, specifically over the Sparse Candidate algorithm, corroborated by our experiments. MMHC and detailed results of our study are publicly available at http://www.dsl-lab.org/supplements/mmhc_paper/mmhc_index.html.

negative: Static hand gesture recognition based on HOG characters and support vector machines Gesture recognition technology has important significance in the field of human-computer interaction (HCI), the gesture recognition technology which is based on visual is sensitive to the impact of the experimental environment lighting, and so, the recognition result will produce a greater change; it makes this technology one of the most challenging topics. HOG feature which is successfully applied to pedestrian detection is operating on the local grid unit of image, so it can maintain a good invariance on geometric and optical deformation. In this paper, we extracted the gradient direction histogram (HOG) features of gestures, then, a Support Vector Machines is used to train these feature vectors, at testing time, a decision is taken using the previously learned SVMs, and compared the same gesture recognition rate in different light conditions. Experimental results show that the HOG feature extraction and multivariate SVM classification methods has a high recognition rate, and the system has a better robustness for the illumination.

negative: A Surface-Growing Approach to Multi-View Stereo Reconstruction We present a new approach to reconstruct the shape of a 3D object or scene from a set of calibrated images. The central idea of our method is to combine the topological flexibility of a point-based geometry representation with the robust reconstruction properties of scene-aligned planar primitives. This can be achieved by approximating the shape with a set of surface elements (surfels) in the form of planar disks which are independently fitted such that their footprint in the input images matches. Instead of using an artificial energy functional to promote the smoothness of the recovered surface during fitting, we use the smoothness assumption only to initialize planar primitives and to check the feasibility of the fitting result. After an initial disk has been found, the recovered region is iteratively expanded by growing further disks in tangent direction. The expansion stops when a disk rotates by more than a given threshold during the fitting step. A global sampling strategy guarantees that eventually the whole surface is covered. Our technique does not depend on a shape prior or silhouette information for the initialization and it can automatically and simultaneously recover the geometry, topology, and visibility information which makes it superior to other state-of-the-art techniques. We demonstrate with several high-quality reconstruction examples that our algorithm performs highly robustly and is tolerant to a wide range of image capture modalities.

negative: Neural evidence for the interplay between language, gesture, and action: A review Co-speech gestures embody a form of manual action that is tightly coupled to the language system. As such, the co-occurrence of speech and co-speech gestures is an excellent example of the interplay between language and action. There are, however, other ways in which language and action can be thought of as closely related. In this paper we will give an overview of studies in cognitive neuroscience that examine the neural underpinnings of links between language and action. Topics include neurocognitive studies of motor representations of speech sounds, action-related language, sign language and co-speech gestures. It will be concluded that there is strong evidence on the interaction between speech and gestures in the brain. This interaction however shares general properties with other domains in which there is interplay between language and action.

negative: Rubinstein Taybi Syndrome in an Indian Child due to EP300 Gene Mutation. To the Editor : A 10-y-old girl was diagnosed with Rubinstein Taybi Syndrome (RTS) based on distinctive facial features (low hanging columella, high palate, grimacing smile, talon cusps), broad, angulated thumbs and great toes, short stature and moderate intellectual disability (Fig. 1). Her younger sister, who died at 11 mo of age due to acute myeloid leukemia, did not have clinical features of RTS. Complete sequencing of CREBBP and EP300 genes was done for the proband as previously described [1, 2]. No pathogenic mutation was noted in CREBBP gene. She was found to be heterozygous for c.1282C > T or p.P428S mutation in exon five of EP300 gene (Fig. 1). The mutation is predicted to affect the donor splicing [3]. The patient’s mother was heterozygous for the same mutation. Prenatal diagnosis was offered to the couple and the fetus did not carry this mutation, and was confirmed to be unaffected postnatally. This mutation was not identified in 100 normal unrelated individuals. Deletion/duplication analysis of CREBBP and EP300 gene was not available. Our patient had horizontal palpebral fissures as described with EP300 related RTS, compared with CREBBP related RTS, who have anti-mongoloid slant [4]. Although patients with EP300 mutation have broad thumbs or toes, radial deviation has not been reported [2] (Fig. 1). This mutation was inherited from her mother who had mild hypertelorism, broad nasal bridge, normal intelligence and hands/feet. Negri et al. reported a patient with mutation in EP300 gene (p.N1511T) that was inherited from a healthy mother. Low penetrance in EP300 related RTS may present a challenge during genetic counseling and prenatal diagnosis [2]. EP300 gene somatic mutations are reported in acute leukemia and RTS patients have an increased incidence of cancer [2, 5]. Thus, we report an Indian child with Rubinstein Taybi Syndrome with familial EP300 gene mutation.

negative: Optimized Edge Detection Algorithm for Face Recognition Face recognition is one of the most challenging tasks in the field of image processing. This paper presents an optimized edge detection algorithm for the task of face recognition. In this method a gradient based filter using a wide convolution kernel is applied on the image to extract the edges. Later a thinning algorithm optimized for the wide convolution kernel is applied on the extracted edges. The advantages of this method over other gradient based methods is its ability to find the missing edges more successfully and boost the significant edges of the overall facial contour.

negative: Modeling and control strategy for the transition of a convertible tail-sitter UAV This paper addresses the problem of the transition between rotary-wing and fixed-wing flight of a tail-sitter unmanned aerial vehicle (UAV). A nonlinear control design is presented to regulate the vertical-flight dynamics of the vehicle. We present the dynamic and aerodynamic equations that model the behavior of the vehicle before (vertical flight), during and after (forward flight) the transition. A low-cost embedded system, including an homemade inertial measurement unit (IMU), is used to perform autonomous attitude-stabilized flight in vertical mode.

negative: Towards Knowledge-Driven Annotation While the Web of data is attracting increasing interest and rapidly growing in size, the major support of information on the surface Web are still multimedia documents. Semantic annotation of texts is one of the main processes that are intended to facilitate meaning-based information exchange between computational agents. However, such annotation faces several challenges such as the heterogeneity of natural language expressions, the heterogeneity of documents structure and context dependencies. While a broad range of annotation approaches rely mainly or partly on the target textual context to disambiguate the extracted entities, in this paper we present an approach that relies mainly on formalized-knowledge expressed in RDF datasets to categorize and disambiguate noun phrases. In the proposed method, we represent the reference knowledge bases as co-occurrence matrices and the disambiguation problem as a 0-1 Integer Linear Programming (ILP) problem. The proposed approach is unsupervised and can be ported to any RDF knowledge base. The system implementing this approach, called KODA, shows very promising results w.r.t. state-of-the-art annotation tools in cross-domain experimentations.

negative: Time - motion analysis of professional rugby union players during match-play. The aim of this study was to quantify the movement patterns of various playing positions during professional rugby union match-play, such that the relative importance of aerobic and anaerobic energy pathways to performance could be estimated. Video analysis was conducted of individual players (n=29) from the Otago Highlanders during six "Super 12" representative fixtures. Each movement was coded as one of six speeds of locomotion (standing still, walking, jogging, cruising, sprinting, and utility), three states of non-running intensive exertion (rucking/mauling, tackling, and scrummaging), and three discrete activities (kicking, jumping, passing). The results indicated significant demands on all energy systems in all playing positions, yet implied a greater reliance on anaerobic glycolytic metabolism in forwards, due primarily to their regular involvement in non-running intense activities such as rucking, mauling, scrummaging, and tackling. Positional group comparisons indicated that while the greatest differences existed between forwards and backs, each positional group had its own unique demands. Front row forwards were mostly involved in activities involving gaining/retaining possession, back row forwards tended to play more of a pseudo back-line role, performing less rucking/mauling than front row forwards, yet being more involved in aspects of broken play such as sprinting and tackling. While outside backs tended to specialize in the running aspects of play, inside backs tended to show greater involvement in confrontational aspects of play such as rucking/mauling and tackling. These results suggest that rugby training and fitness testing should be tailored specifically to positional groups rather than simply differentiating between forwards and backs.

negative: The motivations and experiences of the on-demand mobile workforce On-demand mobile workforce applications match physical world tasks and willing workers. These systems offer to help conserve resources, streamline courses of action, and increase market efficiency for micro- and mid-level tasks, from verifying the existence of a pothole to walking a neighbor's dog. This study reports on the motivations and experiences of individuals who regularly complete physical world tasks posted in on-demand mobile workforce marketplaces. Data collection included semi-structured interviews with members (workers) of two different services. The analysis revealed the main drivers for participating in an on-demand mobile workforce, including desires for monetary compensation and control over schedules and task selection. We also reveal main reasons for task selection, which involve situational factors, convenient physical locations, and task requester profile information. Finally, we discuss the key characteristics of the most worthwhile tasks and offer implications for novel crowdsourcing systems for physical world tasks.

negative: A short literature review on reward-based crowdfunding In this short article, we discuss about a popular online fundraising method, named crowdfunding. One of the most useful types is reward-based crowdfunding, where the return is tangible products. We review related researches from three streams, the conceptual research, the empirical research, and the modelling research. Some possible research directions are also discussed in the paper.

negative: Properties of cutaneous mechanoreceptors in the human hand-related to touch sensation Recordings from single peripheral nerve fibres made it possible to analyse the functional properties of tactile afferent units supplying the glabrous skin of the human hand and to assess directly the relation between impulse discharge and perceptive experiences. The 17,000 tactile units in this skin area of the human hand are of four different types: two fast adapting types, FA I and FA I1 (formerly RA and PC), and two slowly adapting types, SA I and SA 11. The receptive field characteristics and the densities in the skin of the type I units (FA I and SA I) indicate that these account for the detailed spatial resolution that is of paramount importance for the motor skill and the explorative role of the hand. The relationship between the stimulus amplitude and perceived intensity during sustained skin indentations did not match the corresponding stimulus response functions of SA units suggesting non-linear transformations within the central nervous system. These transformations, in turn, appear to vary between subjects. A single impulse in a single FA I unit may be felt when originating from the most important tactile regions of the hand, indicating that the psychophysical detection may be set by the threshold of the sense organs. Moreover, no significant noise seems to be superimposed in the respective central sensory pathways.

negative: A functional imaging study of translation and language switching. The neural systems underlying translation and language switching were investigated using PET. Proficient German-English adult bilinguals were scanned whilst either translating or reading visually presented words in German (L1), English (L2) or alternating L1/L2. We refer to alternating L1/L2 as 'switching'. The results revealed contrasting patterns of activation for translation and switching, suggesting at least partially independent mechanisms. Translation, but not switching, increased activity in the anterior cingulate and subcortical structures whilst decreasing activation in several other temporal and parietal language areas associated with the meaning of words. Translation also increased activation in regions associated with articulation (the anterior insula, cerebellum and supplementary motor area) arguably because the reading response to the stimulus must be inhibited whilst a response in a different language is activated. In contrast, switching the input language resulted in activation of Broca's area and the supramarginal gyri, areas associated with phonological recoding. The results are discussed in terms of the cognitive control of language processes.

negative: The comparability of measurements of attitudes toward immigration in the European Social Survey : exact versus approximate measurement equivalence International survey datasets are analyzed with increasing frequency to investigate and compare attitudes toward immigration and to examine the contextual factors that shape these attitudes. However, international comparisons of abstract, psychological constructs require the measurements to be equivalent–i.e., they should measure the same concept on the same measurement scale. Traditional approaches to assessing measurement equivalence quite often lead to the conclusion that measurements are cross-nationally incomparable but have been criticized for being overly strict. In the current study, we present an alternative Bayesian approach that assesses whether measurements are approximately (rather than exactly) equivalent. This approach allows small variations in measurement parameters across groups. Taking a multiple group confirmatory factor analysis framework as a starting point, this study applies approximate and exact equivalence tests to the anti-immigration attitudes scale that was implemented in the European Social Survey (ESS). Measurement equivalence is tested across the full set of 271,220 individuals in 35 ESS countries over six rounds. The results of the exact and the approximate approaches are quite different. Approximate scalar measurement equivalence is established in all ESS rounds, thus allowing researchers to meaningfully compare these mean scores and their relationships with other theoretical constructs of interest. The exact approach, however, eventually proves to be overly strict and leads to the conclusion that measurements are incomparable for a large number of countries and time points. DOI: https://doi.org/10.1093/poq/nfv008 Posted at the Zurich Open Repository and Archive, University of Zurich ZORA URL: https://doi.org/10.5167/uzh-110767 Published Version Originally published at: Davidov, Eldad; Cieciuch, Jan; Meuleman, Bart; Schmidt, Peter; Algesheimer, René; Hausherr, Mirjam (2015). The comparability of measurements of attitudes toward immigration in the European Social Survey: exact versus approximate measurement equivalence. Public Opinion Quarterly, 79(S1):244-266. DOI: https://doi.org/10.1093/poq/nfv008 © The Author 2015. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com THE COMPARABILITY OF MEASUREMENTS OF ATTITUDES TOWARD IMMIGRATION IN THE EUROPEAN SOCIAL SURVEY EXACT VERSUS APPROXIMATE MEASUREMENT EQUIVALENCE

negative: Mining Invariants from Console Logs for System Problem Detection Detecting execution anomalies is very important to the maintenance and monitoring of large-scale distributed systems. People often use console logs that are produced by distributed systems for troubleshooting and problem diagnosis. However, manually inspecting console logs for the detection of anomalies is unfeasible due to the increasing scale and complexity of distributed systems. Therefore, there is great demand for automatic anomaly detection techniques based on log analysis. In this paper, we propose an unstructured log analysis technique for anomaly detection, with a novel algorithm to automatically discover program invariants in logs. At first, a log parser is used to convert the unstructured logs to structured logs. Then, the structured log messages are further grouped to log message groups according to the relationship among log parameters. After that, the program invariants are automatically mined from the log message groups. The mined invariants can reveal the inherent linear characteristics of program work flows. With these learned invariants, our technique can automatically detect anomalies in logs. Experiments on Hadoop show that the technique can effectively detect execution anomalies. Compared with the state of art, our approach can not only detect numerous real problems with high accuracy but also provide intuitive insight into the problems.

negative: Deep analysis for development of RDF, RDFS and OWL ontologies with protege In SW (SW), Ontology is an important layer architecture that is develop with the help of web ontology language (OWL) and makes the web semantic. OWL is designs and creates metadata that required for developing SW applications. It is an essential part of SW. In this paper we design an education and non-education domain ontology framework which is developed by using protégé an ontology editor tool developed by Stanford University. We also show the output for both ontologies by using pellet reasoner based on the proposed ontology frameworks. We analyze from our result that OWL improves the inference compare to Resource Description Framework (RDF) and Resource Description Framework Schema (RDFS). OWL also provides more relevant information compare to RDF and RDFS and OWL precision and recall is better compare to RDF and RDFS. In our paper we used Pellet reasoner that retrieve relevant information from the education and non education ontology and protégé 4.3 alpha tool for the construction of education and non education domain ontology.

negative: Teleoperation control of Baxter robot using body motion tracking In this paper, we use Kinect Xbox 360 sensor to implement the motion control of Baxter robot, a semi humanoid robot with limbs of 7 DOF joints with collision avoidance capabilities. Two different methods using vector approach and inverse kinematics approach have been designed to achieve a given task. Primitive experiments have been carried out to verify the effectiveness of the developed methods. Human motions is captured by Kinect sensor and calculated with Processing Software using SimpleOpenNI wrapper for OpenNI and NITE. UDP protocol is adopted to send reference motion to Baxter robot. Python and RosPy script programming kit is used to calculate joint angles of Baxter robot based on vector approach and inverse kinematics approach. The experimental results demonstrate that both of our proposed approaches have achieved satisfactory performance.

negative:
Effects of manual lymphatic drainage on breast cancer-related lymphedema: a systematic review and meta-analysis of randomized controlled trials BACKGROUND
Lymphedema is a common complication of axillary dissection for breast cancer. We investigated whether manual lymphatic drainage (MLD) could prevent or manage limb edema in women after breast-cancer surgery.

METHODS
We performed a systematic review and meta-analysis of published randomized controlled trials (RCTs) to evaluate the effectiveness of MLD in the prevention and treatment of breast-cancer-related lymphedema. The PubMed, EMBASE, CINAHL, Physiotherapy Evidence Database (PEDro), SCOPUS, and Cochrane Central Register of Controlled Trials electronic databases were searched for articles on MLD published before December 2012, with no language restrictions. The primary outcome for prevention was the incidence of postoperative lymphedema. The outcome for management of lymphedema was a reduction in edema volume.

RESULTS
In total, 10 RCTs with 566 patients were identified. Two studies evaluating the preventive outcome of MLD found no significant difference in the incidence of lymphedema between the MLD and standard treatment groups, with a risk ratio of 0.63 and a 95% confidence interval (CI) of 0.14 to 2.82. Seven studies assessed the reduction in arm volume, and found no significant difference between the MLD and standard treatment groups, with a weighted mean difference of 75.12 (95% CI, -9.34 to 159.58).

CONCLUSIONS
The current evidence from RCTs does not support the use of MLD in preventing or treating lymphedema. However, clinical and statistical inconsistencies between the various studies confounded our evaluation of the effect of MLD on breast-cancer-related lymphedema.

negative: Prediction of Primary Pupil Enrollment in Government School Using Data Mining Forecasting Technique This research concentrates upon predictive analysis of enrolled pupil using forecasting based on data mining technique. The Microsoft SQL Server Data Mining Add-ins Excel 2007 was employed as a software mining tool for predicting enrollment of pupils. The time series algorithm was used for experiment analysis. U-DISE (Unified District Information System for Education) Dataset of Aurangabad district in Maharashtra (India) was obtained from SSA (Sarva Shiksha Abhiyan) and was used for analysis. Data mining for primary pupil enrollment research provides a feasible way to analyze the trend and to addresses the interest of pupil. The dataset was studied and analyzed to forecast registered pupils in government for upcoming years. We conclude that for upcoming year, pupil strength in government school will be dropped down and number of teachers will be supersizing in the said district. Keywords— Data mining, Microsoft SQL Server Data Mining Add-ins Excel 2007, Time series algorithm, U-DISE

negative:
Health-related quality of life in breast cancer patients: A bibliographic review of the literature from 1974 to 2007 BACKGROUND
Quality of life in patients with breast cancer is an important outcome. This paper presents an extensive overview on the topic ranging from descriptive findings to clinical trials.

METHODS
This was a bibliographic review of the literature covering all full publications that appeared in English language biomedical journals between 1974 and 2007. The search strategy included a combination of key words 'quality of life' and 'breast cancer' or 'breast carcinoma' in titles. A total of 971 citations were identified and after exclusion of duplicates, the abstracts of 606 citations were reviewed. Of these, meetings abstracts, editorials, brief commentaries, letters, errata and dissertation abstracts and papers that appeared online and were indexed ahead of publication were also excluded. The remaining 477 papers were examined. The major findings are summarized and presented under several headings: instruments used, validation studies, measurement issues, surgical treatment, systemic therapies, quality of life as predictor of survival, psychological distress, supportive care, symptoms and sexual functioning.

RESULTS
Instruments-Several valid instruments were used to measure quality of life in breast cancer patients. The European Organization for Research and Treatment of Cancer Core Cancer Quality of Life Questionnaire (EORTC QLQ-C30) and its breast cancer specific complementary measure (EORTC QLQ-BR23) and the Functional Assessment Chronic Illness Therapy General questionnaire (FACIT-G) and its breast cancer module (FACIT-B) were found to be the most common and well developed instruments to measure quality of life in breast cancer patients. Surgery-different surgical procedures led to relatively similar results in terms of quality of life assessments, although mastectomy patients compared to conserving surgery patients usually reported a lower body image and sexual functioning. Systemic therapies-almost all studies indicated that breast cancer patients receiving chemotherapy might experience several side-effects and symptoms that negatively affect their quality of life. Adjuvant hormonal therapies also were found to have similar negative impact on quality of life, although in general they were associated with improved survival. Quality of life as predictor of survival-similar to known medical factors, quality of life data in metastatic breast cancer patients was found to be prognostic and predictive of survival time. Psychological distress-anxiety and depression were found to be common among breast cancer patients even years after the disease diagnosis and treatment. Psychological factors also were found to predict subsequent quality of life or even overall survival in breast cancer patients. Supportive care-clinical treatments to control emesis, or interventions such as counseling, providing social support and exercise could improve quality of life. Symptoms-Pain, fatigue, arm morbidity and postmenopausal symptoms were among the most common symptoms reported by breast cancer patients. As recommended, recognition and management of these symptoms is an important issue since such symptoms impair health-related quality of life. Sexual functioning-breast cancer patients especially younger patients suffer from poor sexual functioning that negatively affect quality of life.

CONCLUSION
There was quite an extensive body of the literature on quality of life in breast cancer patients. These papers have made a considerable contribution to improving breast cancer care, although their exact benefit was hard to define. However, quality of life data provided scientific evidence for clinical decision-making and conveyed helpful information concerning breast cancer patients' experiences during the course of the disease diagnosis, treatment, disease-free survival time, and recurrences; otherwise finding patient-centered solutions for evidence-based selection of optimal treatments, psychosocial interventions, patient-physician communications, allocation of resources, and indicating research priorities were impossible. It seems that more qualitative research is needed for a better understanding of the topic. In addition, issues related to the disease, its treatment side effects and symptoms, and sexual functioning should receive more attention when studying quality of life in breast cancer patients.

negative: Characterizing Horn Antenna Signals for Breast Cancer Detection An in-depth analysis of signals from horn antennas used in breast cancer detection research has been carried out. It was found that following the excitation signal, both the double-ridged and the quad-ridged ultra-wideband horn antennas radiate additional unwanted signals that persistently oscillate. Undesirable signal oscillations sources were identified as the horn antenna cavity resonance and inherent antenna LC resonance. These signals interfere with the tumor’s signal response and need to be eliminated for successful detection of the cancerous growth. This paper proposes solutions to remove or minimize these signals without affecting antenna parameters such as bandwidth, gain, ports isolation, and polarization isolation. Modification of the antenna cavity successfully suppressed the unwanted cavity oscillation. Modification of the antenna waveguide reduced inductance and consequently mitigated LC oscillation. The resulting time and frequency domain horn antenna signals demonstrate the effectiveness of the proposed methods. Finally, a breast phantom with a tumor is simulated using signals from the original and a modified horn antenna. The delay and sum method is used to create images. The breast images demonstrate enhanced image quality through the reduction of clutter using the proposed techniques. The signal-to-clutter ratios are 0.448 and 1.6823 dB for the images produced by using the original and modified antennas, respectively.

negative: Accessory Axillary Breast Tissues Dr. Emsen recently published a case report describing a patient with accessory axillary breast tissue treated with ultrasound-guided liposuction [3]. We recently treated a similar case of bilateral axillary breast tissue, and we offer our contribution to the debate (Figs. 1, 2). Accessory axillary breast tissues are common embriogenic alterations found in 1% to 6% of women that often manifest bilaterally [1,2]. Ectopic breast tissue is at risk for benign and malignant breast disease with reported diagnoses of fibrocystic disease, mastitis, fibroadenoma, atypical hyperplasia, and carcinoma [4 6]. In a review of 82 ectopic breast cancer cases, Marshall et al. [5] found an increased incidence of cancer in aberrant breast tissue. Ectopic cancers located in the axilla seemed to present with more extensive disease at an earlier age, suggesting that aberrant breast tissue may be at increased risk for malignant change. According to this view, as for any ectopic mass, tissue diagnosis is required and mandatory [4]. We recently treated a 43-year-old woman who was asymptomatic until her second pregnancy (2004), when two bilateral masses appeared in axillae and began to increase in size. Six months after parturition, a clear fluid was continuously secreted throughout few small pores on the skin. Ultrasounddiagnosed breast tissue and excision with histologic examination confirmed the radiologic findings. In this case, we preoperatively contemplated, as Dr. Emsen did, less invasive and aesthetic methods such as liposuction. However, a collegial discussion with our pathologist and an analysis of literature data suggested en bloc removal for a more precise analysis not only of cellular, but also of histologic characteristics of suspected mammary ducts. We believe that liposuction, more traumatic on tissues than simple excision, does not allow for this. Furthermore, although liposuction leaves minimal incision scars, the Fig. 1. A 43-year-old woman. Right axilla with accessory breast tissue.

negative: Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment In this paper a novel approach is proposed to predict intraday directional-movements of a currency-pair in the foreign exchange market based on the text of breaking financial news-headlines. The motivation behind this work is twofold: First, although market-prediction through text-mining is shown to be a promising area of work in the literature, the text-mining approaches utilized in it at this stage are not much beyond basic ones as it is still an emerging field. This work is an effort to put more emphasis on the text-mining methods and tackle some specific aspects thereof that are weak in previous works, namely: the problem of high dimensionality as well as the problem of ignoring sentiment and semantics in dealing with textual language. This research assumes that addressing these aspects of text-mining have an impact on the quality of the achieved results. The proposed system proves this assumption to be right. The second part of the motivation is to research a specific market, namely, the foreign exchange market, which seems not to have been researched in the previous works based on predictive text-mining. Therefore, results of this work also successfully demonstrate a predictive relationship between this specific market-type and the textual data of news. Besides the above two main components of the motivation, there are other specific aspects that make the setup of the proposed system and the conducted experiment unique, for example, the use of news article-headlines only and not news article-bodies, which enables usage of short pieces of text rather than long ones; or the use of general financial breaking news without any further filtration. In order to accomplish the above, this work produces a multi-layer algorithm that tackles each of the mentioned aspects of the text-mining problem at a designated layer. The first layer is termed the Semantic Abstraction Layer and addresses the problem of co-reference in text mining that is contributing to sparsity. Co-reference occurs when two or more words in a text corpus refer to the same concept. This work produces a custom approach by the name of Heuristic-Hypernyms Feature-Selection which creates a way to recognize words with the same parent-word to be regarded as one entity. As a result, prediction accuracy increases significantly at this layer which is attributed to appropriate noise-reduction from the feature-space. The second layer is termed Sentiment Integration Layer, which integrates sentiment analysis capability into the algorithm by proposing a sentiment weight by the name of SumScore that reflects investors’ sentiment. Additionally, this layer reduces the dimensions by eliminating those that are of zero value in terms of sentiment and thereby improves prediction accuracy. The third layer encompasses a dynamic model creation algorithm, termed Synchronous Targeted Feature Reduction (STFR). It is suitable for the challenge at hand whereby the mining of a stream of text is concerned. It updates the models with the most recent information available and, more importantly, it ensures that the dimensions are reduced to the absolute minimum. The algorithm and each of its layers are extensively evaluated using real market data and news content across multiple years and have proven to be solid and superior to any other comparable solution. The proposed techniques implemented in the system, result in significantly high directional-accuracies of up to 83.33%. On top of a well-rounded multifaceted algorithm, this work contributes a much needed research framework for this context with a test-bed of data that must make future research endeavors more convenient. The produced algorithm is scalable and its modular design allows improvement in each of its layers in future research. This paper provides ample details to reproduce the entire system and the conducted experiments. 2014 Elsevier Ltd. All rights reserved. A. Khadjeh Nassirtoussi et al. / Expert Systems with Applications 42 (2015) 306–324 307

negative: Twitter sentiment analysis using deep learning methods The social media has Immense and popularity among all the services today. Data from SNS (Social Network Service) can be used for a lot of objectives such as prediction or sentiment analysis. Twitter is a SNS that has a huge data with user posting, with this significant amount of data, it has the potential of research related to text mining and could be subjected to sentiment analysis. But handling such a huge amount of unstructured data is a difficult task, machine learning is needed for handling such huge of data. Deep learning is of the machine learning method that use the deep feed forward neural network with many hidden layers in the term of neural network with the result of the experiment about 75%.