Reviewerly/exHarmony huggingface dataset:
Effects of Elementary School Students Survival Swimming Education Using Virtual Reality (VR) Software in Corona 19 Situation on Exercise Self-Efficacy and Water Safety Awareness

The purpose of this study was to investigate the effects of survival swimming education using survival swimming virtual reality VR software on elementary school students’ exercise self-efficacy and water safety awareness in situations where the mandatory survival swimming education practice was suspended due to Corona 19. In order to clarify the purpose of the study, virtual reality VR survival and swimming education software was used in the experiment, and frequency analysis, technical statistics, reliability analysis, and response T-test were performed using the SPSS 22.0 statistical package. Based on the derived research results, the following conclusions were drawn. First, it was found that education through virtual reality survival swimming VR had a positive effect on the formation of exercise self-efficacy in elementary school students. Second, it was found that education through virtual reality survival swimming VR had a positive effect on the formation of awareness of water safety in elementary school students. Third, it was found that education through virtual reality survival swimming VR had a positive effect on the formation of water safety prevention consciousness of elementary school students. Fourth, it was found that education through virtual reality survival swimming VR had a positive effect on the formation of water safety education consciousness of elementary school students. Survival swimming VR education using virtual reality technology is an alternative method for improving the effectiveness of the mandatory survival swimming education policy for elementary school students in the situation where students’ survival swimming education practice has been suspended according to the government’s social distancing prevention guidelines due to Corona 19. Feed.
Associative knowledge feature vector inferred on external knowledge base for dialog state tracking

The dialog state tracker is one of the most important modules on task-oriented dialog systems, its accuracy strongly affects the quality of the system response. The architecture of the tracker has been changed from pipeline processing to an end-to-end approach that directly estimates a user’s intention from each current utterance and a dialog history because of the growth in the use of the neural-network-based classifier. However, tracking appropriate slot-value pairs of dialog states that are not explicitly mentioned in user utterances is still a difficult problem. In this research, we propose creating feature vectors by using inference results on an external knowledge base. This inference process predicts associative entities in the knowledge base, which contribute to the dialog state tracker for unseen entities of utterances. We extracted a part of a graph structure from an external knowledge base (Wikidata). Label propagation was used for inferring associative nodes (entities) on the graph structure to produce feature vectors. We used the vectors for the input of a fully connected neural network (FCNN) based tracker. We also introduce a convolutional neural network (CNN) tracker as a state-of-the-art tracker and ensemble models of FCNN and CNN trackers. We used a common test bed, Dialog State Tracking Challenge 4 for experiments. We confirmed the effectiveness of the associative knowledge feature vector, and one ensemble model outperformed other models.
Reverse Engineering Code Dependencies: Converting Integer-Based  Variability to Propositional Logic

A number of SAT-based analysis concepts and tools for software product lines exist, that extract code dependencies in propositional logic from the source code assets of the product line. On these extracted conditions, SAT-solvers are used to reason about the variability. However, in practice, a lot of software product lines use integer-based variability. The variability variables hold integer values, and integer operators are used in the conditions. Most existing analysis tools can not handle this kind of variability; they expect pure Boolean conditions. This paper introduces an approach to convert integer-based variability conditions to propositional logic. Running this approach as a preparation on an integer-based product line allows the existing SAT-based analyses to work without any modifications. The pure Boolean formulas, that our approach builds as a replacement for the integer-based conditions, are mostly equivalent to the original conditions with respect to satisfiability. Our approach was motivated by and implemented in the context of a real-world industrial case-study, where such a preparation was necessary to analyze the variability. Our contribution is an approach to convert conditions, that use integer variables, into propositional formulas, to enable easy usage of SAT-solvers on the result. It works well on restricted variables (i.e. variables with a small range of allowed values); unrestricted integer variables are handled less exact, but still retain useful variability information.
Emergence of Scenario-Appropriate Collaborative Behaviors for Teams of Robotic Bodyguards

We are considering the problem of controlling a team of robotic bodyguards protecting a VIP from physical assault in the presence of neutral and/or adversarial bystanders in a variety of scenarios. This problem is challenging due to the large number of active entities with different agendas and dynamic movement patterns, the need of cooperation between the robots as well as the requirement to take into consideration criteria such as social norms in addition to the main goal of VIP safety. We show how a multi-agent reinforcement learning approach can evolve behavior policies that outperform hand-engineered approaches. Furthermore, we propose a novel multi-agent reinforcement learning algorithm inspired by universal value function approximators that can learn policies that exhibit appropriate, distinct behavior in environments with different requirements.
Bayes-ToMoP: A Fast Detection and Best Response Algorithm Towards Sophisticated Opponents

Multiagent algorithms often aim to accurately predict the behaviors of other agents and find a best response accordingly. Previous works usually assume an opponent uses a stationary strategy or randomly switches among several stationary ones. However, an opponent may exhibit more sophisticated behaviors by adopting more advanced reasoning strategies, e.g., using a Bayesian reasoning strategy. This paper proposes a novel approach called Bayes-ToMoP which can efficiently detect the strategy of opponents using either stationary or higher-level reasoning strategies. Bayes-ToMoP also supports the detection of previously unseen policies and learning a best-response policy accordingly. We also propose a deep version of Bayes-ToMoP by extending Bayes-ToMoP with DRL techniques. Experimental results show both Bayes-ToMoP and deep Bayes-ToMoP outperform the state-of-the-art approaches when faced with different types of opponents in two-agent competitive games.
Parameters estimation and calibration of BLDC motor for electric powered wheelchair

In order to analyses the numerical analysis, parameters are very important in the mathematical modelling work. The correspondence output between mathematical modelling and real system is depending on the given parameters. In this paper, estimation and calibration method of brushless DC (BLDC) motor parameters that used in Electric Powered Wheelchair (EPW) through experimental and simulation comparison are proposed. The setup of experiment consists of in-wheel BLDC motor with a driver, 36-volt battery, Arduino microcontroller, speed sensor and current measurement tool. Firstly, the initial parameters of motor are estimated by using related equation and fitting curve method. Then, initial estimated parameters are simulated in EPW modelling. Finally, parameters of the BLDC motor are calibrated according to output speed of modelling and real DC motor using Matlab-Parameter Estimation tool. Result of error shows that the modelling and real EPW output speed is 2.7% after calibration. In conclusion, the calibrated parameters verified to use in modelling and for the further work in designing the control for the EPW.
Using embodied experiences to re-design enabling technologies

Purpose This study introduces a phenomenological approach to the design of enabling technologies for older adults focusing on capabilities rather than disabilities. The purpose of this paper is to use embodied experiences to structure an alternative understanding of the human–technology relationship and demonstrates the significance and implications of this approach. Design/methodology/approach In total, 31 older citizens ( M =80.5 years, SD=5.97 years) were engaged in a participatory design process revolving around the embodied experiences of the participants. The aim was to design new wireless chargers for their existing enabling technologies. The paper presents design results and statistical analyses of performance evaluations. Findings The co-designed alternatives were compared to the current alternative. The statistical analyses revealed an average increase in performance of 45.35 percent across all participants when using designs anchored in embodied experiences. The significance of these results suggests that a shift from disabilities to capabilities provides new opportunities to understand and facilitate interaction between older adults and technology. Research limitations/implications The study follows the theoretical argument into real-use scenarios involving co-designed artifacts to demonstrate how the suggested approach can be a viable alternative strategy to how we facilitate the design of enabling technologies for older adults. Originality/value The suggested design approach contributes to the ongoing development of enabling technologies for older adults by introducing a respectful and inclusive alternative strategy.
Clustering Techniques

Clustering is a process of grouping a set of data points in such a way that data points in the same group (called cluster) are more similar to each other than to data points lying in other groups (clusters). Clustering is a main task of exploratory data mining, and it has been widely used in many areas such as pattern recognition, image analysis, machine learning, bioinformatics, information retrieval, and so on. Clusters are always identified by similarity measures. These similarity measures include intensity, distance, and connectivity. Based on the applications of the data, different similarity measures may be chosen. The purpose of this chapter is to produce an overview of much (certainly not all) of clustering algorithms. The chapter covers valuable surveys, the types of clusters, and methods used for constructing the clusters.
Intrinsic Image Sequence Decomposition Using Low-Rank Sparse Model

Intrinsic image decomposition from a single image or image sequences is always a challenging task in computer vision and image processing due to the ill-posed characteristics. In order to attain a reasonable estimation of intrinsic images, in this paper, we present a low-rank sparse model (LRSM) to derive intrinsic images from an image sequence of the same scene under various lightings. Due to the dependance of varying lightings and the excellent edge-preserving ability of the total variation constraint, both are joined together to formulate the LRSM as a spatiotemporal prior. When considering the relationships among images of a scene as well as those between color channels of a color image, the proposed model involves a complex objective function and makes the solving much more difficult than that on a single gray-scale image. Therefore, we design an iterative numerical scheme based on the alternating direction method of multipliers framework to solve the objective function efficiently. We further specify the application of the proposed method to object recoloring. The experimental results demonstrate that the proposed LSRM and the iterative scheme are effective and efficient.
Learning to refine source representations for neural machine translation

Machine translation is one of the most classic application technologies in artificial intelligence and natural language processing. Neural machine translation models generally adopt an encoder–decoder architecture for modeling the entire translation process. However, without considering target context (e.g., decoding state) to guide the encoding, encoded source representations struggle to put great emphasis on important information for predicting some target word, yielding the weakness in generating more discriminative attentive representations across different decoding steps. Towards tackling this issue, we propose a novel encoder–refiner–decoder framework, which dynamically refines the source representations based on the generated target-side information at each decoding step. Since the refining operations are time-consuming, we propose a policy network to decide when to refine at specific decoding steps. We solve such a problem using the Gumbel-Softmax reparameterization, which makes our network differentiable and trainable through standard stochastic gradient methods. Experimental results on both Chinese–English and English–German translation tasks show that the proposed approach significantly and consistently improves translation performance over the standard encoder–decoder framework. Furthermore, when refining strategy is applied, experimental results still show a reasonable improvement over the baseline with much decrease in decoding speed.
A Novel Partial 3D Object Retrieval Method Using Adaptive Slices Clustering

The similarity measuring between 3D objects can be reduced to a distance between their descriptors.Therefore, the principal challenge is the mapping of objects into reduced and compact representations referred to as descriptors.Generally speaking, descriptors based on global features cannot be effective to describe incomplete 3D objects.This paper presents a novel partial 3D object retrieval approach called adaptive slices clustering (ASC).To ensure that similar objects will be decomposed similarly, normalization is applied to the objects as a preprocessing step.Then, we characterize each object by a set of 2D slices corresponding to its three principal axes, transforming the shape-matching problem between objects into similarity measuring between their slices.Next, we choose among the extracted 2D slices the most representative to reduce searching time without diminishing the performance of our approach.The results show that our approach provides better performance than the descriptors that characterize the whole object.
Modeling and Predicting Chaotic Circuit Data

Analyzing chaotic observations generated from some unknown nonlinear dynamics presents significant challenges for modeling the process and predicting future evolutions. We consider time series data measured from an electrical circuit that exhibits chaotic behavior. We compare and contrast the performance of Gaussian process (GP) and neural network (NN) models in short-term prediction and capturing the long-term dynamics. Major difficulties in modeling observations generated by such a physical process include distinguishing and characterizing the model and observational errors. We explore the effects of different types of model and observational errors on the likelihood function of the initial state using data generated under our fitted NN model. Our results show the distinctive effects of model and observational errors on inferring the initial state. In the absence of model error, we obtain exponentially growing information about the initial state for more observations even under temporally correlated observational errors. However, even with a tiny model error much smaller than what is measured physically and otherwise correct dynamics, the likelihood method no longer identifies the true initial state and hence cannot track the system for an indefinitely long time. Nonetheless, for this circuit system, our fitted predictors based on the GP and NN models appear to be promising in capturing the long-term dynamics in the presence of stochastic thermal noise.
Hand Gestures Categorisation and Recognition

In this digital era, the focus is now on the development of applications that allow human beings and machines to interact directly. Up to now, many hand gesture recognition systems have been developed for different applications such as sign language recognition and smart surveillance. In recent years, researchers have shown interest in the development of hand gesture recognition applications for dancing movements, which involve dynamic hand gestures. However, there are still various challenges such as extraction of invariant factors, automatic segmentation, the transition between gestures, mixed gestures issues, nature of dynamic hand gestures and occlusions that need to be addressed. This research work aims at developing an application to categorise and recognise the classical “Bharatanatyam” dance hand gestures. Since no online database of “Bharatanatyam” gestures is available to the public for research purposes, a customised database has been built with 900 images, consisting of 15 instances for each hand gesture. In this work, Chain Codes and Histogram of Oriented Gradients (HOG) are proposed for the feature representation of the hand gestures. For the classification of the images, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) are explored. From the experiments conducted, Chain codes with SVM provide a recognition rate of 99.9% and a false rejection rate of only 0.1%, which is a promising technique for the deployment of movement recognition applications.
Text Infilling

Recent years have seen remarkable progress of text generation in different contexts, such as the most common setting of generating text from scratch, and the emerging paradigm of retrieval-and-rewriting. Text infilling, which fills missing text portions of a sentence or paragraph, is also of numerous use in real life, yet is under-explored. Previous work has focused on restricted settings by either assuming single word per missing portion or limiting to a single missing portion to the end of the text. This paper studies the general task of text infilling, where the input text can have an arbitrary number of portions to be filled, each of which may require an arbitrary unknown number of tokens. We study various approaches for the task, including a self-attention model with segment-aware position encoding and bidirectional context modeling. We create extensive supervised data by masking out text with varying strategies. Experiments show the self-attention model greatly outperforms others, creating a strong baseline for future research.
Viewpoint Invariant Change Captioning.

Describing what has changed in a scene can be useful to a user, but only if generated text focuses on what is semantically relevant. It is thus important to distinguish distractors (e.g. a viewpoint change) from relevant changes (e.g. an object has moved). We present a novel Dual Dynamic Attention Model (DUDA) to perform robust Change Captioning. Our model learns to distinguish distractors from semantic changes, localize the changes via Dual Attention over before and after images, and accurately describe them in natural language via Dynamic Speaker, by adaptively focusing on the necessary visual inputs (e.g. before or after image). To study the problem in depth, we collect a CLEVR-Change dataset, built off the CLEVR engine, with 5 types of scene changes. We benchmark a number of baselines on our dataset, and systematically study different change types and robustness to distractors. We show the superiority of our DUDA model in terms of both change captioning and localization. We also show that our approach is general, obtaining state-of-the-art results on the recent realistic Spot-the-Diff dataset which has no distractors.
Deep Query Ranking for Question Answering over Knowledge Bases

We study question answering systems over knowledge graphs which map an input natural language question into candidate formal queries. Often, a ranking mechanism is used to discern the queries with higher similarity to the given question. Considering the intrinsic complexity of the natural language, finding the most accurate formal counter-part is a challenging task. In our recent paper [1], we leveraged Tree-LSTM to exploit the syntactical structure of input question as well as the candidate formal queries to compute the similarities. An empirical study shows that taking the structural information of the input question and candidate query into account enhances the performance, when compared to the baseline system. Code related to this paper is available at: https://github.com/AskNowQA/SQG .
Making Large Collections of Handwritten Material Easily Accessible and Searchable

Libraries and cultural organisations contain a rich amount of digitised historical handwritten material in the form of scanned images. A vast majority of this material has not been transcribed yet, owing to technological challenges and lack of expertise. This renders the task of making these historical collections available for public access challenging, especially in performing a simple text search across the collection. Machine learning based methods for handwritten text recognition are gaining importance these days, which require huge amount of pre-transcribed texts for training the system. However, it is impractical to have access to several thousands of pre-transcribed documents due to adversities transcribers face. Therefore, this paper presents a training-free word spotting algorithm as an alternative for handwritten text transcription, where case studies on Alvin (Swedish repository) and Clavius on the Web are presented. The main focus of this work is on discussing prospects of making materials in the Alvin platform and Clavius on the Web easily searchable using a word spotting based handwritten text recognition system.
Estimating the Number of Posts in Sina Weibo

Sina Weibo, an online social network site, has gained popularity but lost it in recent years. Now we are still curious on the number of posts in Sina Weibo in its golden age. Besides checking this number in Sina’s operating results, we aim to estimate and verify this number through measurement by using statistical techniques. Existing approaches on measurement always rely on the supported streaming application programming interface (API) which provides proportional sampling. However no such API is available for Sina Weibo. Instead, Sina provides a public timeline API which provides non-proportional sampling but always returns a (nearly) fixed number of s amples. In this paper, we present a novel method utilizing this API and estimate the number of posts in Sina Weibo in its golden age.
A Homotopy Method for Motion Planning.

We propose a novel method for motion planning and illustrate its implementation on several canonical examples. The core novel idea underlying the method is to define a metric for which a path of minimal length is an admissible path, that is path that respects the various constraints imposed by the environment and the physics of the system on its dynamics. To be more precise, our method takes as input a control system with holonomic and non-holonomic constraints, an initial and final point in configuration space, a description of obstacles to avoid, and an initial trajectory for the system, called a sketch. This initial trajectory does not need to meet the constraints, except for the obstacle avoidance constraints. The constraints are then encoded in an inner product, which is used to deform (via a homotopy) the initial sketch into an admissible trajectory from which controls realizing the transfer can be obtained. We illustrate the method on various examples, including vehicle motion with obstacles and a two-link manipulator problem.