Reviewerly/exHarmony huggingface dataset:
Exploring Machine Reading Comprehension for Continuous Questions via Subsequent Question Completion

In recent years, the Sq-MRC (machine reading comprehension for separate questions) task, where the questioner poses a separate question each time, has experienced rapid development. However, we often encounter circumstances where the questioner poses a series of nonstop questions. The corresponding task, called Cq-MRC (machine reading comprehension for continuous questions), has been rarely investigated and is nontrivial due to (1) the incomplete expressions of subsequent questions, such as anaphora or subject ellipsis, and (2) the shortage of annotated samples for model training and performance evaluation. We explore this challenging task, in this article, and propose a completion-then-prediction approach. To tackle the first issue, we identify the key-entity in the first question and employ it to complete the subsequent ones, and hence decompose the task into a set of Sq-MRC tasks which are accomplished using a BERT-based model. In addition, to mitigate the shortage of annotated samples, we train a meta-learner on the Sq-MRC task which is related to Cq-MRC and has abundant labeled data, and then perform few-shot learning by fine-tuning the model on our target task, viz. Cq-MRC, using the limited number of samples available. The results of extensive experiments conducted on a multi-document Chinese machine reading comprehension dataset show the effectiveness and superiority of our method, achieving 2.3% to 3.7% absolute improvements in terms of major metrics.
Reinforcement Learning with Latent Flow

Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such information is given as part of the state space or, when learning from pixels, use the simple heuristic of frame-stacking to implicitly capture temporal information present in the image observations. This heuristic is in contrast to the current paradigm in video classification architectures, which utilize explicit encodings of temporal information through methods such as optical flow and two-stream architectures to achieve state-of-the-art performance. Inspired by leading video classification architectures, we introduce the Flow of Latents for Reinforcement Learning (Flare), a network architecture for RL that explicitly encodes temporal information through latent vector differences. We show that Flare (i) recovers optimal performance in state-based RL without explicit access to the state velocity, solely with positional state information, (ii) achieves state-of-the-art performance on pixel-based challenging continuous control tasks within the DeepMind control benchmark suite, namely quadruped walk, hopper hop, finger turn hard, pendulum swing, and walker run, and is the most sample efficient model-free pixel-based RL algorithm, outperforming the prior model-free state-of-the-art by 1.9X and 1.5X on the 500k and 1M step benchmarks, respectively, and (iv), when augmented over rainbow DQN, outperforms this state-of-the-art level baseline on 5 of 8 challenging Atari games at 100M time step benchmark.
Preconditioned training of normalizing flows for variational inference in inverse problems

Obtaining samples from the posterior distribution of inverse problems with expensive forward operators is challenging especially when the unknowns involve the strongly heterogeneous Earth. To meet these challenges, we propose a preconditioning scheme involving a conditional normalizing flow (NF) capable of sampling from a low-fidelity posterior distribution directly. This conditional NF is used to speed up the training of the high-fidelity objective involving minimization of the Kullback-Leibler divergence between the predicted and the desired high-fidelity posterior density for indirect measurements at hand. To minimize costs associated with the forward operator, we initialize the high-fidelity NF with the weights of the pretrained low-fidelity NF, which is trained beforehand on available model and data pairs. Our numerical experiments, including a 2D toy and a seismic compressed sensing example, demonstrate that thanks to the preconditioning considerable speed-ups are achievable compared to training NFs from scratch.
Efficient Algorithms for Sign Detection in RNS Using Approximate Reciprocals

The residue number system (RNS) is a method for representing an integer x as an n-tuple of its residues with respect to a given set of moduli. In RNS, addition, subtraction, and multiplication can be carried out by independent operations with respect to each modulus. Therefore, an n-fold speedup can be achieved by parallel processing. The main disadvantage of RNS is that we cannot efficiently compare the magnitude of two integers or determine the sign of an integer. Two general methods of comparison are to transform a number in RNS to a mixed-radix system or to a radix representation using the Chinese remainder theorem (CRT). We used the CRT to derive an equation approximating a value of x relative to M, the product of moduli. Then, we propose two algorithms that efficiently evaluate the equation and output a sign bit. The expected number of steps of these algorithms is of order n. The algorithms use a lookup table that is (n+3) times as large as M, which is reasonably small for most applications including cryptography.
A Global &amp; Environmental Coral Analysis System with SPA-Based Semantic Computing for Integrating and Visualizing Ocean-Phenomena with “5-Dimensional World-Map”

Semantic computing is essentially significant for realizing the semantic interpretation of natural and social phenomena and analyzes the changes of various environmental situations. The 5D World Map (5DWM) System [4,6,8] has introduced the concept of “SPA (Sensing, Processing and Analytical Actuation Functions)” for global environmental system integrations [1–4], as a global environmental knowledge sharing, analysis and integration system. Environmental knowledge base creation with 5D World Map is realized for sharing, analyzing and visualizing various information resources to the map which can facilitate global phenomena-observations and knowledge discoveries with multi-dimensional axis control mechanisms. The 5DWM is globally utilized as a Global Environmental Semantic Computing System, in SDGs 9, 11, 14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes) for observing and analyzing disaster, natural phenomena, ocean-water situations with local and global multimedia data resources. This paper proposes a new semantic computing method as an important approach to semantic analysis for various environmental phenomena and changes in a real world. This method realizes “Self-Contained-Knowledge-Base-Image” &amp; “Contextual-Semantic-Interpretation” as a new concept of “Coral-Health-level Analysis in Semantic-Space for Ocean-environment” for global ocean-environmental analysis [8,9,12,18]. This computing method is applied to automatic database creation with coral-health-level analysis sensors for interpreting environmental phenomena and changes occurring in the oceans in the world. We have focused on an experimental study for creating “Coral-Health-level Analysis Semantic-Space for Ocean-environment” [8,9,12,18]. This method realizes new semantic interpretation for coral health-level with “coral-images and coral-health-level knowledge-chart”.
Influence of hearing attitude difference on sound quality evaluation of vacuum cleaner sound

Robot vacuum cleaner, which cleans rooms automatically, is a useful appliance that helps house work. The radiated sound should be soft for comfortable living. When we think about hearing situations, we hear sound passively because a robot vacuum cleaner moves automatically without external control indication. On the other hand, when we use a conventional vacuum cleaner, the sound is perceived actively because we operate it ourselves. This hearing attitude difference may affect the perception of sound. In this study, we attempted to clarify the difference in the degree of uncomfortable under different hearing conditions by subjective evaluation tests. In the tests, we prepared the above-mentioned two hearing conditions for the evaluation of the radiated sound. The results showed that the participants felt more uncomfortable under passive hearing condition than under active hearing condition.
CHALLENGES OF INFLUENCE DYNAMIC AND INTERACTIVE EVENTS ON RESOURCE DISCOVERY FUNCTIONALITY OUTSIDE OF DISTRIBUTED EXASCALE SYSTEMS

The resource discovery in Exascale systems should support the occurrence of dynamic nature in each stakeholder's elements in the resource discovery process. The occurrence of dynamic and interactive nature in the accountable computational element creates challenges in executing the activities related to resource discovery, such as the continuation of the response to the request, granting access rights, and the resource allocation to the process. In the case of a lack of management and dynamic and interactive event control in the accountable computational element, the process of activities related to the resource discovery will fail. In this paper, we first examine the concept function of resource discovery in the accountable computational element. Then, to analyze the effects of occurrence, dynamic, and interactive event effects on resource discovery function in the accountable computational element are discussed. The purpose of this paper is to analyze the use of the traditional resource discovery in the Exascale distributed systems and investigate the factors that should be considered in the function of resource discovery management to have the possibility of application in the Exascale distributed system.
MEDIA, EDUCATION, SOCIAL COMMUNICATION

The processes of interaction between social phenomena and individuals are presented and analyzed. This article examines media literacy as the basis of the link between media and education. Where the formation of digital competences becomes an indispensable part of the pedagogical interaction as well as of class curricula at all levels.
An Approach for Neural Machine Translation with Graph Attention Network

The achievement of Neural Machine Translation (NMT) drew the attention of the professionals in recent years. The translation quality outperforms traditional methods such as Statistical Machine Translation. However, for the document-level machine translation tasks, the research shows that there are still some problems with the Machine Translation model, such as the loss of the original document theme and lack of fluency.
The Reflection of Vocabulary Implementation Through Educational Texting on EFL Learner’s Reading Skill

This paper investigates the efficiency of using text messaging&lt;strong&gt; &lt;/strong&gt;in the EFL reading classroom at the Islamic Azad University-South Tehran Branch, Iran. After the administration of an Oxford Placement Test (OPT), 74 participants who scored one standard deviation above and below the mean were selected. Participants were randomly assigned into two groups, an experimental and a control, each including 37 individuals. The reading part of the Preliminary English Test (PET) was administered as a pretest. The experimental group received 108 words via short messages during 18 sessions. During each session, they received 6 sentences with 6 words in a meaningful context. In contrast, the participants of the control group received traditional instruction. They were given the same structural points through inductive and deductive instruction in the classroom and the words were presented while teaching each unit. After the treatment, the participants in both groups were assigned to do a reading posttest. The results of the ANCOVA revealed no statistically significant difference between the control and the experimental groups regarding their reading scores. Thus, the vocabulary learning via short message did not have any significant effect on Iranian EFL learners' ability to comprehend pre-intermediate level texts.
Camera auto-calibration for complex scenes

In this paper, we propose a novel method for automatic camera calibration based on pedestrians' observations. Our proposed method is capable of estimating calibration parameters for complex scenes having more than one ground plane. Unlike existing methods that require time-consuming optimization step, our method uses real-time re-estimation step based on fuzzy logic while relaxing the assumption on the number of ground planes in the scene. Furthermore, we propose a dominant ground plane detection step for better calibration parameter estimation on complex scenes. To evaluate our proposed method, we run comprehensive testing using 5 different datasets covering varieties of calibration parameters and scene properties, we also conduct tests on a synthetic dataset for more detailed analysis. Test results show that our proposed method outperforms existing state-of-the-art methods in both performance accuracy and time complexity.
Transversely Stable Extended Kalman Filters for Systems on Manifolds in Euclidean Spaces

Abstract In this article, we devise a variant of the extended Kalman filter that can be generally applied to systems on manifolds with simplicity and low computational cost. We extend a given system on a manifold to an ambient open set in Euclidean space and modify the system such that the extended system is transversely stable on the manifold. Then, we apply the standard extended Kalman filter derived in Euclidean space to the modified dynamics. This method is efficient in terms of computation and accurate in comparison with the standard extended Kalman filter. It has the merit that we can apply various Kalman filters derived in Euclidean space including extended Kalman filters for state estimation for systems defined on manifolds. The proposed method is successfully applied to the rigid body attitude dynamics whose configuration space is the special orthogonal group in three dimensions.
A User-Preference Driven Lexicographic Approach for Multi-Objective Distributed Web Service Composition

In distributed Data-intensive Web Service Composition (DWSC), multiple individual services from different locations are composed to accomplish a more complex service. Automatic DWSC must fulfil functional requirements and optimise multiple Quality of Service (QoS) attributes simultaneously. Users often have QoS preferences, e.g. one QoS attribute is deemed more important than the others. Users' QoS preferences are crucial parts of a service composition request. However, user preferences have not been considered in the current composition research. This is mainly due to the difficulties of accurately representing user preferences during the optimisation process. To model users preferences easily, we present them through the lexicographic ordering. We then develop an effective multi-objective evolutionary algorithm, i.e. Lex-NSGA-II, which uses a newly designed clustering technique to produce high-quality solutions that satisfy users' QoS preferences. Our experimental evaluations using benchmark datasets demonstrate the effectiveness of our proposed method.
Visual Exploration and Energy-aware Path Planning via Reinforcement Learning

Visual exploration and smart data collection via autonomous vehicles is an attractive topic in various disciplines. Disturbances like wind significantly influence both the power consumption of the flying robots and the performance of the camera. We propose a reinforcement learning approach which combines the effects of the power consumption and the object detection modules to develop a policy for object detection in large areas with limited battery life. The learning model enables dynamic learning of the negative rewards of each action based on the drag forces that is resulted by the motion of the flying robot with respect to the wind field. The algorithm is implemented in a near-real world simulation environment both for the planar motion and flight in different altitudes. The trained agent often performed a trade-off between detecting the objects with high accuracy and increasing the area coverage within its battery life. The developed exploration policy outperformed the complete coverage algorithm by minimizing the traveled path while finding the target objects. The performance of the algorithms under various wind fields was evaluated in planar and 3D motion. During an exploration task with sparsely distributed goals and within a UAV's battery life, the proposed architecture could detect more than twice the amount of goal objects compared to the coverage path planning algorithm in moderate wind field. In high wind intensities, the energy-aware algorithm could detect 4 times the amount of goal objects when compared to its complete coverage counterpart.
Supporting Named Entity Recognition and Document Classification for Effective Text Retrieval

In this research paper, we present a system for named entity recognition and automatic document classification in an innovative knowledge management system for Applied Gaming. The objective of this project is to facilitate the management of machine learning-based named entity recognition models, that can be used for both: extracting different types of named entities and classifying text documents from different sources on the Web. We present real-world use case scenarios and derive features for training and managing NER models with the Stanford NLP machine learning API. Then, the integration of our developed NER system with an expert rule-based system is presented, which allows an automatic classification of text documents into different taxonomy categories available in the knowledge management system. Finally, we present the results of two evaluations. First, a functional evaluation that demonstrates the portability of our NER system using a standard text corpus in the medical area. Second, a qualitative evaluation that was conducted to optimize the overall user interface of our system and enable a suitable integration into the target environment.
Merging the Analogue and the Digital: Combining Opposite Activities in a Mixed Media Game

While much of the games research field for the last two decades has focused on digital games, this article draws attention to the benefits of combining analogue and digital game components to cater for a serious but fun game experience. In this case, the game design provides a set of game rules for players, where the goal is to win by finding another player’s hidden treasure. But, the game also includes deceptive characters, initially unknown to the players, whose goal is to lure the players to reveal information, which will make a player lose the game. Hence, the players and the unknown characters are involved in opposite but intertwined activities. To describe the differing activities we use the activity system model found in Activity Theory. The theoretical conceptualisation, the game design and the play situation create what we term a &lt;em&gt;zone of experience&lt;/em&gt; where young players can experience the consequences of sharing too much information. The game design mimics real world online interactions, but under safe off-line conditions. The zone of experience also creates the foundation for an ensuing activity that fits well within the concept of the zone of proximal development: A follow-up conversation under adult guidance of game experiences aimed at raising children’s online risk awareness.
Probabilistic Weighted induced Multi-Class Support Vector Machines for Face Recognition

This paper deals with a probabilistic weighted multi-class support vector machines for face recognition. The support vector machines (SVM) has been applied to many application fields such as pattern recognition in last decade. The support vector machines determine the hyperplane which separates largest fraction of samples of the similar class on the same side. The SVM also maximizes the distance from the either class to the separating hyperplane. It has been observed that in many realistic applications, the achieved training data is frequently tainted by outliers and noises. Support vector machines are very sensitive to outliers and noises. It may happen that a number of points in the training dataset are misplaced from their true position or even on the wrong side of the feature space. The weighted support vector machines are designed to overcome the outlier sensitivity problem of the support vector machines. The main issue in the training of the weighted support vector machines algorithm is to build up a consistent weighting model which can imitate true noise distribution in the training dataset, i.e., reliable data points should have higher weights, and the outliers should have lower weights. Therefore, the weighted support vector machines are trained depending on the weights of the data points in the training set. In the proposed probabilistic weighted multi-class support vector machines the weights are generated by probabilistic method. The weighted multi-class support vector machines have been constructed using a combination of the weighted binary support vector machines and one-against-all decision strategies. Numerous experiments have been performed on the AR, CMU PIE and FERET face databases using different experimental strategies. The experimental results show that the performance of the probabilistic weighted multi-class support vector machines is superior to the multi-class support vector machines in terms of recognition rate.
Computerized diagnosis of prostate cancer based on whole slide histology images and deep learning methods

This paper presents the results of an experimental study and the development of tools for automatic analysis and recognition of histological images in order to obtain quantitative estimates of the presence and degree of aggressiveness of prostate cancer in the commonly used Gleason and ISUP scales. The input data consisted of 10 616 whole-slide histological images with the size of the largest side up to 100 000 pixels and 22 089 of their image tiles of 256×256 pixels in size. Two solutions were chosen as the final ones. The first solution is based on sequential analysis of image fragments and includes feature extraction using the ResNet50 network and the subsequent generalization of particular recognition results using a small convolutional network. The second solution is based on the simultaneous analysis of the selected informative sections, presented in the form of an intermediate pseudo-image, and its subsequent recognition using an ensemble of four variants of convolutional networks with the EfficientNetB0 architecture. Being independently tested on an unknown image dataset that was not available for authors, these approaches achieved the prediction accuracy of 0,9277 according to the ISUP scale.
Exact simulation of two-parameter Poisson-Dirichlet random variables

Consider a random vector (V1, . . ., Vn) where {V k } k=1,...,n are the first n components of a two-parameter Poisson-Dirichlet distribution P D(α, θ).In this paper, we derive a decomposition for the components of the random vector, and propose an exact simulation algorithm to sample from the random vector.Moreover, a special case arises when θ/α is a positive integer, for which we present a very fast modified simulation algorithm using a compound geometric representation of the decomposition.Numerical examples are provided to illustrate the accuracy and effectiveness of our algorithms.