Reviewerly/exHarmony huggingface dataset:
Learning Three-Dimensional Shapes in Geometry Using Mobile-Based Augmented Reality

&lt;pre&gt;&lt;span lang="IN"&gt;Learning three-dimensional shape of geometry is important for improving students’ spatial abilities. However, the concept of three-dimensional shape is still considered as one of difficult subject matters for teacher to teach and for student to understand in Indonesia. Therefore, it is necessary to find an alternative way that can allow teachers and students to easily understand some concepts of geometry. In this research, we try to implement mobile-based augmented reality in presenting three-dimensional geometry materials to help teachers and students of junior high school in Bandung, Indonesia. The results show that by using mobile based augmented reality, teachers can teach three dimensional shape of geometry clearly and students could understand the concept of three-dimensional shape of geometry faster. In addition, students can learn the concept of geometry comprehensively through geometry objects that can be seen from all points of view and students' spatial activities are increased&lt;/span&gt;&lt;/pre&gt;
Personalized Query Auto-Completion for Large-Scale POI Search at Baidu Maps

Query auto-completion (QAC) is a featured function that has been widely adopted by many sub-domains of search. It can dramatically reduce the number of typed characters and avoid spelling mistakes. These merits of QAC are highlighted to improve user satisfaction, especially when users intend to type in a query on mobile devices. In this article, we will present our industrial solution to the personalized QAC for the point of interest (POI) search at Baidu Maps, a well-known Web mapping service on mobiles in China. The industrial solution makes a good tradeoff between the offline effectiveness of a novel neural learning model that we devised for feature generation and the online efficiency of an off-the-shelf learning to rank (LTR) approach for the real-time suggestion. Besides some practical lessons from how a real-world QAC system is built and deployed in Baidu Maps to facilitate a large number of users in searching tens of millions of POIs, we mainly explore two specific features for the personalized QAC function of the POI search engine: the spatial-temporal characteristics of POIs and the historically queried POIs of individual users . We leverage the large-volume POI search logs in Baidu Maps to conduct offline evaluations of our personalized QAC model measured by multiple metrics, including Mean Reciprocal Rank (MRR), Success Rate (SR), and normalized Discounted Cumulative Gain (nDCG). Extensive experimental results demonstrate that the personalized model enhanced by the proposed features can achieve substantial improvements (i.e., +3.29% MRR, +3.78% SR@1, +5.17% SR@3, +1.96% SR@5, and +3.62% nDCG@5). After deploying this upgraded model into the POI search engine at Baidu Maps for A/B testing online, we observe that some other critical indicators, such as the average number of keystrokes and the average typing speed at keystrokes in a QAC session, which are also related to user satisfaction, decrease as well by 1.37% and 1.69%, respectively. So the conclusion is that the two kinds of features contributed by us are quite helpful in personalized mapping services for industrial practice.
Tensored Generalized Hough Transform for Object Detection in Remote Sensing Images

To avoid using a large 4D-Hough counting space (HCS) and complex invariant features of generalized Hough transform (GHT) or its extensions when detecting objects in remote sensing image (RSI), a tensored GHT (TGHT) is proposed to extract object contour by simple gradient angle feature in a 2D-HCS using a single training sample. Considering that tensor can record the structure relationship of object contour, tensor representation R-table is constructed to record the contour information of template. For slice centered at each position of RSI, the tensor-space-based voting mechanism is presented to use the tensor that records the contour information of slice to gather votes at the same entry of 2D-HCS. Furthermore, a multiorder binary-tree-based searching method is presented to accelerate voting by searching the index numbers of elements in tensors. In addition, by solving the tensor-space-based optimization problem that is used to determine the candidates objects, the cause of false alarms (FAs) caused by interferences with complex contour and FAs caused by interferences that are partial-similar to objects is revealed, and the matching rate and matching sparsity-based strategies are then proposed to remove these FAs. Using public RSI datasets with different scenes, experimental results demonstrate that TGHT reduces nearly 99% storage requirement compared with GHT for RSI with size exceeding 1000 × 1000 under small time consumption, and outperforms the well-known contour extraction methods and state-of-the-art deep-learning-based methods in terms of precision and recall.
Comparison of Evolving Granular Classifiers applied to Anomaly Detection for Predictive Maintenance in Computing Centers

Log-based predictive maintenance of computing centers is a main concern regarding the worldwide computing grid that supports the CERN (European Organization for Nu-clear Research) physics experiments. A log, as event-oriented ad-hoc information, is quite often given as unstructured big data. Log data processing is a time-consuming computational task. The goal is to grab essential information from a continuously changeable grid environment to construct a classification model. Evolving granular classifiers are suited to learn from time-varying log streams and, therefore, perform online classification of the severity of anomalies. We formulated a 4-class online anomaly classification problem, and employed time windows between landmarks and two granular computing methods, namely, Fuzzy-set-Based evolving Modeling (FBeM) and evolving Granular Neural Network (eGNN), to model and monitor logging activity rate. The results of classification are of utmost importance for predictive maintenance because priority can be given to specific time intervals in which the classifier indicates the existence of high or medium severity anomalies.
Explaining Image Classifiers Generating Exemplars and Counter-Exemplars from Latent Representations

We present an approach to explain the decisions of black box image classifiers through synthetic exemplar and counter-exemplar learnt in the latent feature space. Our explanation method exploits the latent representations learned through an adversarial autoencoder for generating a synthetic neighborhood of the image for which an explanation is required. A decision tree is trained on a set of images represented in the latent space, and its decision rules are used to generate exemplar images showing how the original image can be modified to stay within its class. Counterfactual rules are used to generate counter-exemplars showing how the original image can “morph” into another class. The explanation also comprehends a saliency map highlighting the areas that contribute to its classification, and areas that push it into another class. A wide and deep experimental evaluation proves that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability, besides providing the most useful and interpretable explanations.
Denotational recurrence extraction for amortized analysis

A typical way of analyzing the time complexity of functional programs is to extract a recurrence expressing the running time of the program in terms of the size of its input, and then to solve the recurrence to obtain a big-O bound. For recurrence extraction to be compositional, it is also necessary to extract recurrences for the size of outputs of helper functions. Previous work has developed techniques for using logical relations to state a formal correctness theorem for a general recurrence extraction translation: a program is bounded by a recurrence when the operational cost is bounded by the extracted cost, and the output value is bounded, according to a value bounding relation defined by induction on types, by the extracted size. This previous work supports higher-order functions by viewing recurrences as programs in a lambda-calculus, or as mathematical entities in a denotational semantics thereof. In this paper, we extend these techniques to support amortized analysis, where costs are rearranged from one portion of a program to another to achieve more precise bounds. We give an intermediate language in which programs can be annotated according to the banker's method of amortized analysis; this language has an affine type system to ensure credits are not spent more than once. We give a recurrence extraction translation of this language into a recurrence language, a simply-typed lambda-calculus with a cost type, and state and prove a bounding logical relation expressing the correctness of this translation. The recurrence language has a denotational semantics in preorders, and we use this semantics to solve recurrences, e.g analyzing binary counters and splay trees.
Admissible Perturbation of Demicontractive Operators within Ant Algorithms for Medical Images Edge Detection

Nowadays, demicontractive operators in terms of admissible perturbation are used to solve difficult tasks. The current research uses several demicontractive operators in order to enhance the quality of the edge detection results when using ant-based algorithms. Two new operators are introduced, χ -operator and K H -operator, the latter one is a Krasnoselskij admissible perturbation of a demicontractive operator. In order to test the efficiency of the new operators, a comparison is made with a trigonometric operator. Ant Colony Optimization (ACO) is the solver chosen for the images edge detection problem. Demicontractive operators in terms of admissible perturbation are used during the construction phase of the matrix of ants artificial pheromone, namely the edge information of an image. The conclusions of statistical analysis on the results shows a positive influence of proposed operators for image edge detection of medical images.
Survey Paper for Credit Card Fraud Detection Using Data Mining Techniques

With the advent of new Technologies, nowadays electronic gadgets and online shopping's more popular.Banking and Online shopping has now become most common activities amongst the masses.As technology advances so does the risk associate with these transactions.The ease of use in this online transaction has now become more popular across the world.So it essential that we need to be very cautious on the increased Fraud activities.Online Fraud is an illegal activity that can occur when we do electronic transactions.Fraud has increased and created more risk that has serious financial loss in the financial industry.As a result, these financial institutions have enforced various techniques to improve their fraud detection methods.Since we are in the age of Information Technology, Data rules the world.So, Data mining techniques are widely used to for fraud detection.There are various algorithms such as Anomaly Detection Algorithm, Decision Tree, Random Forest, K-Nearest Neighbor, K-Means used for fraud deduction.The type of fraud doesn't remain the same in each case, so this becomes very crucial in coming up with the best algorithm for the fraudulent transaction.This paper presents the survey of those techniques and predicts the best algorithm to detect the fraudulent transaction based on a given scenario.
Considering Likelihood in NLP Classification Explanations with Occlusion and Language Modeling

Recently, state-of-the-art NLP models gained an increasing syntactic and semantic understanding of language, and explanation methods are crucial to understand their decisions.Occlusion is a well established method that provides explanations on discrete language data, e.g. by removing a language unit from an input and measuring the impact on a model's decision.We argue that current occlusionbased methods often produce invalid or syntactically incorrect language data, neglecting the improved abilities of recent NLP models.Furthermore, gradient-based explanation methods disregard the discrete distribution of data in NLP.Thus, we propose OLM: a novel explanation method that combines occlusion and language models to sample valid and syntactically correct replacements with high likelihood, given the context of the original input.We lay out a theoretical foundation that alleviates these weaknesses of other explanation methods in NLP and provide results that underline the importance of considering data likelihood in occlusion-based explanation. 11 Our experiments are available at https:
Association mining method based on neighborhood perspective

Important tasks in big data association mining are identification of potentially complex associations among massive variables and determination of the strength of different forms of associations. However, uncertain data distributions and diverse associations make it difficult to ensure the applicability and accuracy of measures based on distribution assumptions and data-driven non-parametric measurement methods. Therefore, an effective association measure that is unbiased relative to relationship types is urgently needed. In this article, starting from the fair ordering requirement of potential relationships in big data, we review the current axiomatic conditions of association metrics, provide some possible properties that association measures in big data should satisfy, discuss some limitations of two types of association methods based on neighborhood perspective, and propose a new association measure based on $k$-NN granule, which we refer to as maximum neighborhood coefficient. Experiments using artificial and real datasets verify the effectiveness and superiority of the proposed method from different perspectives. Finally, we identify interesting phenomena in the experiment and theoretical issues to be solved that we hope will motivate deeper thinking and research in this field.
Feature Difference Makes Sense: A medical image captioning model exploiting feature difference and tag information

Medical image captioning can reduce the workload of physicians and save time and expense by automatically generating reports. However, current datasets are small and limited, creating additional challenges for researchers. In this study, we propose a feature difference and tag information combined long short-term memory (LSTM) model for chest x-ray report generation. A feature vector extracted from the image conveys visual information, but its ability to describe the image is limited. Other image captioning studies exhibited improved performance by exploiting feature differences, so the proposed model also utilizes them. First, we propose a difference and tag (DiTag) model containing the difference between the patient and normal images. Then, we propose a multi-difference and tag (mDiTag) model that also contains information about low-level differences, such as contrast, texture, and localized area. Evaluation of the proposed models demonstrates that the mDiTag model provides more information to generate captions and outperforms all other models.
VR-Based Interactive Learning in Architectural Education: A Case on Safranbolu Historical Bathhouse

PurposeHistorical buildings provide important information about the social life and architectural style of the era in which they were built. Perception of the effect of space by walkthrough is very important to perceive design philosophy of the structure for architecture students. However, for some reason, it may not be possible to see these structures in situ. VR technology has the potential to provide efficient solution to this problem. The study aims to provide an environment where students can learn architectural details and experience spatial effects of the historical buildings with the help of the VR technology as if they were in place. Design/Methodology/ApproachIn the scope of the study, an interactive VR program (IHVR) related with a historical Greek bathhouse has been developed and the effectiveness of it has been tested with a scientific experiment. A group of 45 volunteers from the architectural students were included in the study. The students were divided into three groups of 15 each called VR, field, and classroom. The students in the VR group experienced the IHVR program with virtual reality glasses while the students in the field group experienced the building on site and the classroom group students are told by the traditional method.FindingsAccording to the test results, the students using the IHVR had a significant success compared to the other groups. It was observed that the interactive structure of the software helped the students to learn the historical building more effectively. As a result of the study, it was seen that IHVR was an efficient hypermedia tool in learning the architectural details and functions of the building.Practical ImplicationsExperiencing historical structures in virtual environment as in real helps students to learn the architectural details more efficiently and experience spatial effects of the structure in a realistic way.Social ImplicationsThe widespread use of virtual reality will facilitate architectural education to keep up with technology and accelerate its adaptation to innovative approaches. The use of these technologies in architectural education will help to attract the interests of students to the subject that is intended to be explained more easily.Originality/ValueThe study leads to the development of VR-based hypermedia tools in applied architectural education.
Adversarial Robustness Guarantees for Classification with Gaussian Processes.

We investigate adversarial robustness of Gaussian Process Classification (GPC) models. Given a compact subset of the input space $T\subseteq \mathbb{R}^d$ enclosing a test point $x^*$ and a GPC trained on a dataset $\mathcal{D}$, we aim to compute the minimum and the maximum classification probability for the GPC over all the points in $T$. In order to do so, we show how functions lower- and upper-bounding the GPC output in $T$ can be derived, and implement those in a branch and bound optimisation algorithm. For any error threshold $\epsilon > 0$ selected a priori, we show that our algorithm is guaranteed to reach values $\epsilon$-close to the actual values in finitely many iterations. We apply our method to investigate the robustness of GPC models on a 2D synthetic dataset, the SPAM dataset and a subset of the MNIST dataset, providing comparisons of different GPC training techniques, and show how our method can be used for interpretability analysis. Our empirical analysis suggests that GPC robustness increases with more accurate posterior estimation.
SCAR: Sentence Compression using Autoencoders for Reconstruction

Sentence compression is the task of shortening a sentence while retaining its meaning.Most methods proposed for this task rely on labeled or paired corpora (containing pairs of verbose and compressed sentences), which is often expensive to collect.To overcome this limitation, we present a novel unsupervised deep learning framework (SCAR) for deletion-based sentence compression.SCAR is primarily composed of two encoder-decoder pairs: a compressor and a reconstructor.The compressor masks the input, and the reconstructor tries to regenerate it.The model is entirely trained on unlabeled data and does not require additional inputs such as explicit syntactic information or optimal compression length.SCAR's merit lies in the novel Linkage Loss function, which correlates the compressor and its effect on reconstruction, guiding it to drop inferable tokens.SCAR achieves higher ROUGE scores on benchmark datasets than the existing stateof-the-art methods and baselines.We also conduct a user study to demonstrate the application of our model as a text highlighting system.Using our model to underscore salient information facilitates speed-reading and reduces the time required to skim a document.
Doc2Dial: A Framework for Dialogue Composition Grounded in Documents

We introduce Doc2Dial, an end-to-end framework for generating conversational data grounded in given documents. It takes the documents as input and generates the pipelined tasks for obtaining the annotations specifically for producing the simulated dialog flows. Then, the dialog flows are used to guide the collection of the utterances via the integrated crowdsourcing tool. The outcomes include the human-human dialogue data grounded in the given documents, as well as various types of automatically or human labeled annotations that help ensure the quality of the dialog data with the flexibility to (re)composite dialogues. We expect such data can facilitate building automated dialogue agents for goal-oriented tasks. We demonstrate Doc2Dial system with the various domain documents for customer care.
Characterizing Variation in Toxic Language by Social Context

How two people speak to one another depends heavily on the nature of their relationship. For example, the same phrase said to a friend in jest may be offensive to a stranger. In this paper, we apply this simple observation to study toxic comments in online social networks. We curate a collection of 6.7K tweets containing potentially toxic terms from users with different relationship types, as determined by the nature of their follower-friend connection. We find that such tweets between users with no connection are nearly three times as likely to be toxic as those between users who are mutual friends, and that taking into account this relationship type improves toxicity detection methods by about 5% on average. Furthermore, we provide a descriptive analysis of how toxic language varies by relationship type, finding for example that mildly offensive terms are used to express hostility more commonly between users with no social connection than users who are mutual friends.
Generating Stories that Include Failed Actions by Modeling False Character Beliefs

Previous work on story planning has lacked a knowledge representation for characters that make mistakes in the execution of their actions. In particular, characters' execution mistakes that arise from errors in belief have not been modeled. In this paper, we describe a state-space planning system and its belief model, together called HeadSpace, that generates stories that track and manipulates characters' belief about the story world around them. This model is used to produce actions in stories that are attempted but that fail. We show an example story plan that contains failed-action content that cannot be generated by typical planning-based approaches to story creation.
Impact of Tactile and Visual Feedback on Breathing Rhythm and User Experience in VR Exergaming

Combining interconnected wearables provides fascinating opportunities like augmenting exergaming with virtual coaches, feedback on the execution of sports activities, or how to improve on them. Breathing rhythm is a particularly interesting physiological dimension since it is easy and unobtrusive to measure and gained data provide valuable insights regarding the correct execution of movements, especially when analyzed together with additional movement data in real-time. In this work, we focus on indoor rowing since it is a popular sport that's often done alone without extensive instructions. We compare a visual breathing indication with haptic guidance in order for athletes to maintain a correct, efficient, and healthy breathing-movement-synchronicity (BMS) while working out. Also, user experience and acceptance of the different modalities were measured. The results show a positive and statistically significant impact of purely verbal instructions and purely tactile feedback on BMS and no significant impact of visual feedback. Interestingly, the subjective ratings indicate a strong preference for the visual modality and even an aversion for the haptic feedback, although objectively the performance benefited most from using the latter.