Reviewerly/exHarmony huggingface dataset:
Class Activation Map generation by Multiple Level Class Grouping and Orthogonal Constraint

Class activation map (CAM) highlights regions of classes based on classification network, which is widely used in weakly supervised tasks. However, it faces the problem that the class activation regions are usually small and local. Although several efforts paid to the second step (the CAM generation step) have partially enhanced the generation, we believe such problem is also caused by the first step (training step), because single classification model trained on the entire classes contains finite discriminate information that limits the object region extraction. To this end, this paper solves CAM generation by using multiple classification models. To form multiple classification networks that carry different discriminative information, we try to capture the semantic relationships between classes to form different semantic levels of classification models. Specifically, hierarchical clustering based on class relationships is used to form hierarchical clustering results, where the clustering levels are treated as semantic levels to form the classification models. Moreover, a new orthogonal module and a two-branch based CAM generation method are proposed to generate class regions that are orthogonal and complementary. We use the PASCAL VOC 2012 dataset to verify the proposed method. Experimental results show that our approach improves the CAM generation.
I’ve wondered for years why #macos and ...

I’ve wondered for years why #macos and #windows don’t supply an update service for applications to hook into to keep themselves updated. I guess it’s
Windy Wyndham and the Wagon Who Couldn’t Swim by P. Gould

Gould, Peter. Windy Wyndham and the Wagon Who Couldn’t Swim. Bassendean, Western Australia, Peter Gould and Donna Franklin, 2018.&#x0D; This is the sixth volume in Peter Gould’s Stories from the Engine Shed series. These books tell the stories of the train engines and cars of the Bennett Brook Railway at Whiteman Park, near Perth in Western Australia. Apart from being a fun series of children’s books, this series is important because it demonstrates that the concept of anthropomorphized train cars is generic and does not belong to any particular franchise. The series has its own cast of characters and a unique Western Australian world in which they operate. While earlier volumes have been set in Whiteman Park, this is a story told by the large diesel locomotive, Windy Wyndham that harkens back to a time when Windy worked at the harbour in the northwestern Australian coastal town of Wyndham. &#x0D; The book is full of Donna Franklin’s brightly coloured pictures. The style of illustration varies significantly, even within pictures. Some of the images, particularly those with machines and people, are quite cartoonish. Drawings of people sometimes lack detail. However, when the subject is something in nature, Franklin’s work is stronger. Her rendition of a turtle, a crocodile, magpie-larks and seahorses are lovely. &#x0D; Each page is a picture with text overprinted. The text includes local references, such as the Freemantle Doctor, the cooling breeze that comes from the Indian Ocean. There are also some railway-specific words, such as “buffer beam”, “shunter”, and “cow catcher.” These words are explained in the glossary, “Ashley’s Railway Words”, at the end of the book. &#x0D; While not widely distributed, the series is available through the Bennett Brook Railway. This volume includes a loose single page insert that explains the history of the real Windy Wyndham engine, which has been at Whiteman Park since 1984. &#x0D; Overall, this is an excellent series, which is suitable for elementary school libraries and public libraries. &#x0D; Highly recommended: 4 out of 4 starsReviewer: Sandy Campbell&#x0D; Sandy is a Health Sciences Librarian at the University of Alberta, who has written hundreds of book reviews across many disciplines. Sandy thinks that sharing books with children is one of the greatest gifts anyone can give.
Centre-surround Suppression of Contrast through the Form and Motion Pathways

Centre-surround suppression of contrast is the phenomenon where the perceived contrast of a central stimulus is reduced when presented with a surround. A number of stimulus features influence the amount of suppression the surround induces on the perceived contrast of the centre, with the focus of this study being on the orientation and direction of motion of the surround relative to the centre. Suppression declines as the similarity between centre and surround stimulus properties increases and this decline is thought to reflect multiple mechanisms of centre-surround suppression. However, this is yet to be established in the motion pathway in human participants. A two-mechanism model has been proposed involving a narrowly-tuned process, requiring very similar stimuli in the centre and surround, and a weaker, broadly-tuned process unselective for stimulus features. Five participants completed a two-interval forced choice contrast discrimination task relative to a 40% reference. Stimuli included both static and moving Gabor grating patterns whose high contrast (95%) surrounds had either parallel or orthogonal (90°) orientations relative to the centre. The motion manipulation included surrounds drifting in either the same or opposite direction to the centre. The results with static stimuli showed contrast suppression when surrounds were present and replicated the more substantial suppression when the centre and surround had the same orientation than when they were orthogonal. With motion stimuli all surround conditions were significantly more suppressed than the no-surround condition and the surround condition with the same orientation and direction of motion as the centre was significantly more suppressed than either the opposite direction of motion conditions or the orthogonal orientation conditions. There was no difference between the orthogonal orientation conditions and the opposite direction of motion conditions. These findings support the extension of the two mechanism model of contrast suppression to the motion pathways.
Know Your Community: Sensor‐Based Water Management

CSA NewsVolume 64, Issue 9 p. 18-18 Membership Know Your Community: Sensor-Based Water Management Davie Kadyampakeni, Davie Kadyampakeni 2019 ASA Sensor-Based Water Management Community LeaderSearch for more papers by this author Davie Kadyampakeni, Davie Kadyampakeni 2019 ASA Sensor-Based Water Management Community LeaderSearch for more papers by this author First published: 19 September 2019 https://doi.org/10.2134/csa2019.64.0904Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume64, Issue9September 2019Pages 18-18 RelatedInformation
A relational database with pharmaceutical information: problems of creation and primary filling for provision of a qualitative statistical processing

The disclosure of pharmaceutical information is not intended to provide the possibility of further automated processing of the data provided by independent experts, and, accordingly, the choice of the methods of presentation is aimed to optimize visual perception by users.&#x0D; The aim of the work was to analyze in the internet-sources the availability of information in the appropriate format for automatic filling of the relational database. The subjects of the study were: ATC: ICD-10 (International Classification of Diseases); content of EF 9.8; classification of dosage forms by the Ministry of Health of Ukraine, FDA (Food and Drug Administration, USA), EMA (European Medicines Agency) and EphMRA (European Pharmaceutical Market Research Association). Methods used are review of information with the corresponding structure in the internet, processing of the information found using computer code.&#x0D; Approbation was carried out on the basis of the digital online system «Likypedia» (http://likypedia.zzz.com.ua; http://facebook.com/likypedia).&#x0D; The basic goals for the quality of the results of statistical processing of pharmaceutical information are formed. To achieve them, a list of required information for the initial download to the relational database is defined. The sources of the received information which was used for initial loading to the relational database are presented. The multicomponent record of pharmaceutical information is described.&#x0D; The list of dosage forms was formed on the basis of own practical experience, trends in the indication on packaging of medicines by manufacturers, electronic databases of pharmaceutical wholesalers, the titles of articles given in the EF 9.8 edition. Information from different sources about pharmaceutical dosage forms was analyzed and divided into five information blocks: made by manufacturer; its characteristic; for DF, which will be prepared with DF produced by the manufacturer; its characteristic; way of using DF.&#x0D; A variant has been developed and presented for structuring the information record of the drug dose in the relational database in several variants, which makes it possible to further carry out automated calculations, optimize the selection and sorting of data.
Retraction Notice: Semantic Web Identity of Rajiv Gandhi University and Health Science Libraries [Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 418s–425s.]

RETRACTION NOTICE Semantic Web Identity of Rajiv Gandhi University and Health Science Libraries [Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 418s–425s.] http://sciencejournals.stmjournals.in/index.php/JoALS/article/view/1804  This article has been retracted.  It is brought to the attention of the editor that large section of abstract, method, result, and discussion are similar with the article “Semantic Web Identity of Academic Libraries” published by “Kenning Arlitsch” in the “Journal of Library Administration”, 2017; volume 57, issue 3, pages 346–358, doi: 10.1080/01930826.2017.1288970.  The authors deeply regret and apologized for the unethical practice and research misconduct.  We apologize the readers of this journal that plagiarism is not detected during submission process. All articles submitted in this journal are pass through plagiarism check and peer review process. Originality of this article is not detected during the process.
Nailed It: Autonomous Roofing with a Nailgun-Equipped Octocopter.

This paper presents the first demonstration of autonomous roofing with a multicopter. A DJI S1000 octocopter equipped with an off-the-shelf nailgun and an adjustableslope roof mock-up were used. The nailgun was modified to allow triggering from the vehicle and tooltip compression feedback. A mount was designed to adjust the angle to match representative roof slopes. An open-source octocopter autopilot facilitated controller adaptation for the roofing application. A state machine managed autonomous nailing sequences using smooth trajectories designed to apply prescribed contact forces for reliable nail deployment. Experimental results showed that the system is capable of nailing within a required three centimeter gap on the shingle. Extensions to achieve a complete autonomous roofing system are discussed as future work.
Book review

After a break of about five years, in 2016 the classical QBFEVAL has been revived. QBFEVAL is a competitive evaluation of solvers for quantified Boolean formulas (QBF), the extension of propositional formulas with existential and universal quantifiers over the propositional variables.Due to the enormous interest in QBFEVAL'16, more recently, QBFEVAL'17 was organized. Both competitions were affiliated to the respective editions of the International Conference on Theory and Applications of Satisfiability Testing (SAT'16 and SAT'17), the major conference in research on SAT and related areas.In this paper we report about the 2016 and 2017 competitive evaluations of QBF solvers (QBFEVAL'16 and QBFEVAL'17), the two most recent events in a series of competitions established with the aim of assessing the advancements in reasoning about QBFs. This report gives an overview of the setup of these two events, on their participants and on the results of the experiments that were performed for evaluating the participating systems.
Novel offline technique to process and understand interaction with printed imagery

Gaze fixations are used to monitor tasks and guide hand movements. Simple tasks have been studied extensively, and some complex tasks have been studied in 2D environments (e.g., Ballard, et al. 1992). Much remains to be learned about complex interactions in the natural world. How is gaze distributed to support motor movement and information gathering in complex interactions? We designed a sorting task with 2-dimensional printed imagery and monitored gaze and grasp of participants to explore these questions and understand how gaze and grasp interact when a sorting task requires information gathering, manual interaction, and placement. Using a head-mounted Pupil Labs eye tracker, which recorded their eyes at 120 Hz and the scene at 60 Hz and a custom-designed system to monitor their grasp, we designed a novel system which used a template of the objects, and mapped the fixations and grasp data onto the templates. The eye data-processing procedure starts with finding the fixations in the scene, then detecting if the fixation is on an object, and identifying that object from among the templates. Once the object is identified, the fixation is projected onto the object’s template. A similar procedure is followed for processing the hand data. The hand is located, followed by detecting and identifying the object, and finally the hand is mapped onto the object’s template. Monitoring gaze and grasp concurrently, we observed that during the interaction, eye movements are made for both seeking information and guiding motor movements. This now allows us to perform finer spatio-temporal analyses to understand eye and hand coordination in complex interactions. We report results in the sorting task.
Learn to Segment Organs with a Few Bounding Boxes

Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology. Deep neural networks can perform this task well by leveraging the information from a large well-labeled data-set. This paper aims to present a method that mitigates the necessity of an extensive well-labeled data-set. This method also addresses semi-supervision by enabling segmentation based on bounding box annotations, avoiding the need for full pixel-level annotations. The network presented consists of a single U-Net based unbranched architecture that generates a few-shot segmentation for an unseen human organ using just 4 example annotations of that specific organ. The network is trained by alternately minimizing the nearest neighbor loss for prototype learning and a weighted cross-entropy loss for segmentation learning to perform a fast 3D segmentation with a median score of 54.64%.
Transsaccadic prediction of real-world objects

During a saccade, a target object’s retinal position and spatial resolution change. Nevertheless, we do not perceive these changes, but instead experience a stable percept of our environment. One mechanism of how the brain deals with these saccade induced changes is that it forms associations between presaccadic peripheral and postsaccadic foveal input of saccade-target objects. Based on these transsaccadic associations the visual system can predict visual features across saccades. Up to now, studies investigating how transsaccadic feature prediction affects peripheral perception have focused primarily on simple visual features (e.g., spatial frequency, size and shape). They could show that peripheral perception is biased toward previously associated foveal input. The present study tested whether also complex visual features constituting real-world objects (fruits and balls) are predicted across saccades. In an eyetracking experiment, twenty-four participants first underwent an acquisition phase, in which they learned new object-specific transsaccadic associations. Six out of twelve objects were systematically swapped to an object of the opposite category (from fruit to ball or vice versa) during saccades. In the following test phase, participants were briefly presented peripheral saccade target objects and had to identify which object they saw. Objects which had previously been swapped during the acquisition phase were more often perceived as belonging to a different category compared to objects which stayed the same during acquisition. These category errors occurred mainly because participants confused the peripherally presented object with its transsaccadically associated foveal counterpart. This result indicates that transsaccadic prediction is object-specific and not limited to a small set of simple visual features.
Connections between physics, mathematics, and deep learning

Starting from Fermat’s principle of least action, which governs classical and quantum mechanics and from the theory of exterior differential forms, which governs the geometry of curved manifolds, we show how to derive the equations governing neural networks in an intrinsic, coordinate-invariant way, where the loss function plays the role of the Hamiltonian. To be covariant, these equations imply a layer metric which is instrumental in pretraining and explains the role of conjugation when using complex numbers. The differential formalism clarifies the relation of the gradient descent optimizer with Aristotelian and Newtonian mechanics. The Bayesian paradigm is then analyzed as a renormalizable theory yielding a new derivation of the Bayesian information criterion. We hope that this formal presentation of the differential geometry of neural networks will encourage some physicists to dive into deep learning and, reciprocally, that the specialists of deep learning will better appreciate the close interconnection of their subject with the foundations of classical and quantum field theory.
Introduction to the 35th International Conference on Logic Programming Special Issue

This volume contains the regular papers of the 35th International Conference on Logic Programming (ICLP 2019), held in Las Cruces, New Mexico, USA, from September 20 to September 25, 2019.
Saliency and the population receptive field model to identify images from brain activity

One of the goals of visual neuroscience is to develop predictive models of brain activity to better understand the underlying mechanisms of the visual cortex. One example is the identification of presented images from fMRI recordings. Recently, Zuiderbaan et al. (2017) used the population receptive field (pRF) model and image contrast information to identify the presented stimulus from a set of natural images. Here, we extend the study by analyzing the predictive power of saliency-related information. Ultimately, we seek answers to these questions: What fraction of the responses is driven by saliency? Where in the visual cortex is saliency most represented? We test the Deep Gaze II and ICF (Kümmerer et al., 2017) saliency models. Deep Gaze II predicts the fixation density by using high-level features computed from a deep artificial neural network, while ICF uses only intensity and contrast features. We calculate the prediction response profile of every image as the summed overlap of its saliency map with the pRF at each cortical location. Then, we compute the correlation between the fMRI recordings and the prediction profiles of all images. An image is correctly identified if its predicted response elicits the highest correlation of the set. We find that ICF achieves the highest performance with a median correlation of .44 and 46.7 % accuracy (baseline 2.2 %) on V1, while Deep Gaze and the contrast model achieve .34 and .26 median correlation and 34.4 and 33.3 accuracy, respectively. On higher visual areas the performance gradually decreases. Both saliency models seem to be therefore better predictors of the presented stimuli than contrast information. The better performance of ICF might be explained because, while the pRF model only captures contrast energy information, the ICF maps may contain other information present in the measured responses, hence increasing the correlation of the predictions.
4차 산업혁명에 따른 쌍방향 인공지능을 통한 영어학습의 변화

The purpose of this study is to examine innovative methods of English language learning in the era of the fourth industrial revolution. It signifies the challenges as well as measures for successful English language education. First, it explains the shift of the paradigm of English education and stipulates changes in current English education environment. Especially, it shows the need to focus on artificial intelligence-assisted language learning and expand self-directed cyber English learning. This study also shows how to apply a teaching and learning model for cyber English education. Finally, it provides examples of the development and application of artificial intelligence learning contents. Through the development of an English teaching and learning method suitable for the computer-mediated language learning age, we can strategically explore effective ways in which we can teach foreign language in the advent of fourth industrial revolution.
Multiple Profiles Sensor-based Engineering Data Processing

Nowadays big data is widely adopted in industry field. In an advanced manufacturing system hundreds of sensors are deployed to collect key variables for system performance and the real-time data would be used for further monitoring and anomaly detection. However, there are many challenges for applying the sensor-based data directly, including the profile data has unsynchronized different length for different samples, the existence of obvious long-term drift, strong correlation of sensor clusters and the particular feature extraction. To solve these problems this invention presents a multiple profiles sensor-based engineering data processing system, including (1) preprocessing the signals to align the data and remove long-term drift, (2) clustering the sensors which have strong correlations, and (3) extracting particular features from different sensor clusters.
Multi-sense Definition Modeling using Word Sense Decompositions

Word embeddings capture syntactic and semantic information about words. Definition modeling aims to make the semantic content in each embedding explicit, by outputting a natural language definition based on the embedding. However, existing definition models are limited in their ability to generate accurate definitions for different senses of the same word. In this paper, we introduce a new method that enables definition modeling for multiple senses. We show how a Gumble-Softmax approach outperforms baselines at matching sense-specific embeddings to definitions during training. In experiments, our multi-sense definition model improves recall over a state-of-the-art single-sense definition model by a factor of three, without harming precision.