Reviewerly/exHarmony huggingface dataset:
Stories of the town

Procedural narrative generation systems often focus on autonomous agent based simulations to create emergent interactions, plan-based approaches to provide guarantees for coherence, or using elements of simulation to guide plan-based approaches. These different approaches, with some exceptions, tend to trade off character autonomy in service of more designer controlled experiences or content authoring in service of encoding domain knowledge of possible branches of the narrative and participating characters. We have developed a system, called Stories of the Town, that automatically generates narratives by synthesizing three distinct approaches to traditional narrative generation: context-free grammars, planning, and simulation. More specifically, our system generates narratives via probabilistic context-free grammars applied to state-space planning problem solutions from planning problem formulations of simulated character models. Our system uses character simulations to generate variety in narratives and ensures narrative coherence through authoring probabilistic context-free grammars. By doing so, this system takes advantage of the strengths of each individual approach (e.g. controllability, scalability, intentionality, and variety) to generate narratives that are extensible, expressive, consistent with simulated character personalities and histories, and controllable. We show that this system has strong potential in automatically generating varied, complex, consistent, and goal-oriented narratives. Further development of the system will allow for more efficient utilization of the strengths of each narrative generation approach while also using these strengths to supplement their individual shortcomings.
Transition From E-Learning to U-Learning

E-learning systems have increased the prevalence of information and computer technologies in education. U-learning is a modern teaching system based on the use of computer technologies (ubiquitous computing technology) everywhere in the environment of existing wired, wireless, mobile, and sensor systems. The interaction between information, object/device, and user/learner/student is formed at any time, anywhere, and form in the communication environment called u-environment. In u-learning, the presence of information in objects (embeded) and mobility is the highest. Training services are among mobile systems and sensors that can move independently in the environment. The status of the learners is followed due to the characteristics of the server systems and objects. Researches on u-learning are ongoing. Especially u-learning system theory and application methods are being investigated. Most of the researches are about u-learning applications rather than u-learning framework. This chapter focuses on basic features, media, and research in the transition from e-learning to u-learning.
A novel realization of quantum stream cipher with key-modulated local light

Quantum-noise stream cipher (QSC) is a potentially information-theoretic secure physical-layer encryption method for high-speed and long-distance fiber communications. In this paper, a novel QSC scheme employing Y-00 protocol is proposed and experimentally demonstrated under general commercial components. The structure of our scheme can provide a convenience for general researchers to perform QSC experimental studies. Specifically, at receiver, the local light is modulated by M/2-ary extended secret key, which is introduced as optical threshold signal, and the balanced photodetector is applied as optical comparator to decode the M-ary encrypted signal. As a result, the error-free secure communication is realized at 2.5 Gb/s over 100-km optically amplified links, where the intensity levels of QSC signals are attained to 1024. Meanwhile, in strict theory, the minimum number of masked levels by only quantum noise is calculated to be more than two, which indicates the security of this realization. The system performance is investigated from both experimental and theoretical results. We reveal the new fact that the increasing number of signal levels will degrade the QSC BER performance for intensity modulation, which is different from that for phase modulation.
SuBViS

Game development is a naturally iterative process where many ideas are tested and prototyped before final decisions are made. Given the increased usage of visual scripting systems in game development recently, it is apparent that these tools must be able to support every aspect of this process. One aspect that is not well captured is the exploration of alternatives. SuBViS was developed as a solution to this problem. It is a visual scripting system for exploring parallel ideas in game development through the use of alternatives at graph and node levels. These two levels of exploring alternatives can be combined or used separately. This paper presents a use case example, which demonstrates how SuBViS can improve workflow and communication between team members. It also discusses a small-scale user study and the results obtained therein. SuBViS was developed on top of Unreal Engine's existing Blueprint Visual Scripting system.
Quasi-Newton Optimization Methods for Deep Learning Applications

Deep learning algorithms often requireRafati, Jacob solving a highly nonlinear and non-convex unconstrained optimization problem. Methods for solving optimization problems in large-scale machine learning, such as deep learning and deep reinforcement learning (RL), are generally restricted to the class of first-order algorithms, like stochastic gradient descent (SGD). While SGD iterates are inexpensive to compute, they have slow theoretical convergence rates. Furthermore, they require exhaustive trial-and-error to fine-tune many learning parameters. Using second-order curvature information to find search directionsMarica, Roummel F. can help with more robust convergence for non-convex optimization problems. However, computing Hessian matrices for large-scale problems is not computationally practical. Alternatively, quasi-Newton methods construct an approximate of the Hessian matrix to build a quadratic model of the objective function. Quasi-Newton methods, like SGD, require only first-order gradient information, but they can result in superlinear convergence, which makes them attractive alternatives to SGD. The limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) approach is one of the most popular quasi-Newton methods that constructs positive definite Hessian approximations. In this chapter, we propose efficient optimization methods based on L-BFGS quasi-Newton methods using line search and trust-region strategies. Our methods bridge the disparity between first- and second-order methods by using gradient information to calculate low-rank updates to Hessian approximations. We provide formal convergence analysis of these methods as well as empirical results on deep learning applications, such as image classification tasks and deep reinforcement learning on a set of Atari 2600 video games. Our results show a robust convergence with preferred generalization characteristics as well as fast training time.
Coordination of Multiple Autonomous Agents Using Naturally Generated Languages in Task Planning

Language plays a prominent role in the activities of human beings and other intelligent creatures. One of the most important functions of languages is communication. Inspired by this, we attempt to develop a novel language for cooperation between artificial agents. The language generation problem has been studied earlier in the context of evolutionary games in computational linguistics. In this paper, we take a different approach by formulating it in the computational model of rationality in a multi-agent planning setting. This paper includes three main parts: First, we present a language generation problem that is connected to state abstraction and introduce a few of the languages’ properties. Second, we give the sufficient and necessary conditions of a valid abstraction with proofs and develop an efficient algorithm to construct the languages where several words are generated naturally. The sentences composed of words can be used by agents to regulate their behaviors during task planning. Finally, we conduct several experiments to evaluate the benefits of the languages in a variety of scenarios of a path-planning domain. The empirical results demonstrate that our languages lead to reduction in communication cost and behavior restriction.
Analyzing user typing behaviour in different positions using keystroke dynamics for mobile phones

AbstractAbstractHold time and latency are the most frequently used features for developing a user typing profile for authenticating mobile phone users. In this paper, we combine these features along with motion features, i.e., the X, Y and Z coordinates at which the user holds the mobile while typing and take the input from the user when he/she is sitting, walking and relaxing. We then address three questions (1) whether there is any change in the typing pattern of the user when his typing position (relaxing, sitting or walking) changes?; (2) in which position the user has the unique typing pattern?; (3) which is the best feature vector to consider for authentication? The results showed that the user typing pattern differs in different positions and also with the change in phone orientation. A user has the unique pattern when he/she is in relaxing mode and holding the mobile in landscape orientation and the best feature vector to build a user profile is by combining hold time, latency and motion features.Subject Classification: 86A17Keywords: Keystroke DynamicsMobile PhonesAuthenticationTyping PatternRandom Forest
Quantum Advantage with Noisy Shallow Circuits in 3D

Prior work has shown that there exists a relation problem which can be solved with certainty by a constant-depth quantum circuit composed of geometrically local gates in two dimensions, but cannot be solved with high probability by any classical constant depth circuit composed of bounded fan-in gates. Here we provide two extensions of this result. Firstly, we show that a separation in computational power persists even when the constant-depth quantum circuit is restricted to geometrically local gates in one dimension. The corresponding quantum algorithm is the simplest we know of which achieves a quantum advantage of this type. Our second, main result, is that a separation persists even if the shallow quantum circuit is corrupted by noise. We construct a relation problem which can be solved with near certainty using a noisy constant-depth quantum circuit composed of geometrically local gates in three dimensions, provided the noise rate is below a certain constant threshold value. On the other hand, the problem cannot be solved with high probability by a noise-free classical circuit of constant depth. A key component of the proof is a quantum error-correcting code which admits constant-depth logical Clifford gates and single-shot logical state preparation. We show that the surface code meets these criteria.
Creating an Augmented Book from a Geography Textbook

With the popularization of mobile devices, Augmented Reality has been increasing the offer of additional support in education. One of the applications is the augmented book that adds virtual features in printed books. Textbooks are an expensive investment for government, but students are big consumers of digital material. In this work, we have developed an experiment in which we model maps from the textbook of three aspects of the African continent so that students can visualize them in 3D on their smartphones. We conducted a section where they used the application and then held a competition in teams answering questions based on the maps of the application. Pre and post-tests were administered to verify if there was any improvement in performance, and students completed a six-question survey to answer their satisfaction with the application and experience.
Automatic Article Detection in a Jumbled Scene Using Point Feature Matching

Recognition of items in jumbled scenes is a basic test that has as of late been generally embraced by computer vision frameworks. This chapter proposes a novel technique how to distinguish a specific item in jumbled scenes. Given a reference picture of the article, a method for recognizing a particular article dependent on finding point correspondences between the reference and the objective picture is presented. It can distinguish objects in spite of a scale change or in-plane revolution. It is additionally strong to little measure of out-of-plane rotation and occlusion. This technique for article location works for things that show non-reiterating surface precedents, which offer rising to exceptional part coordinates. This method furthermore works honorably for reliably shaded articles or for things containing repeating structures. Note that this calculation is intended for recognizing a particular article.
An Open Model for Student Assessment Visualization

Effective Student Models, capable of identifying and predicting the level of knowledge of students, are a key requirement in modern educational systems. In addition, it is important that the students themselves realize their level of knowledge at a given time, so they can self-regulate their learning process. Thus, this paper describes OSM-V, an Open Student Model with Information Visualization capabilities which allow students and instructors to assess information inferred in educational systems. To evaluate the perception of utility and behavioral changes in students who used the model, this paper also brings experiments regarding its integration to a real educational platform. Results showed that this proposal can positively impact students' learning and positively influence their behavior.
The Effects of Data Sources: A Baseline Evaluation of the MoCA Dataset

In this work we discuss the action classification performance obtained with a baseline assessment of the MoCA dataset: a multimodal, synchronised dataset including Motion Capture data and multi-view video sequences of upper body actions in a cooking scenario. To this purpose, we setup a classification pipeline to manipulate the two data type. For the MoCap, we employ a representation based on the use of 3D+t histograms modelling the space-time evolution of an action, classified using a classical Support Vector Machine with a linear kernel. As for the videos, we learn the representation using a variant of the Inception 3D model, followed by a Single Layer Perceptron as a classifier. Discussing the experimental analysis will be the opportunity to observe the diversity of MoCap and video data at work in two scenarios of uneven complexity, i.e. on streams of data describing regular repetitions of the same action, or when actions are part of a more complex and structured activity where actions influence each other.
Visual and Textual Sentiment Analysis of Daily News Social Media Images by Deep Learning

In recent years, following the exploding spread of Social Networking platforms, more and more people have started to share large quantities of data on Internet where they express personal opinions, ideas or emotional states regarding any kind of topic. With the increasing amount of these type of data, finding a way to analyzing it has become a need for major companies, political parties or whatever organization based on its own customers' feedback. This paper proposes a new case of study that has seen a limited assortment of similar proposals in the current state-of-art: in particular, we propose an innovative approach for analyzing both visual and textual features of Social Media images using Deep Convolutional Neural Networks (DCNNs), in order to collect more accurate results than the single analysis of both type can do alone. The deep learning approach estimates the overall sentiment of daily news-related pictures from social media based on both visual and textual clues. The proposed approach was applied and tested on a new public dataset with more than 9.000 annotated Instagram images. Experimental results confirmed the effectiveness of the approach, showing high values of accuracy.
Hybrid Corrective Critic Neural Network for Sentiment Classification in Community Media

The issues faced by sentiment classification in sentiment analysis are a growing research field. The main goal of the sentiment classification is to classify the text into various sentiment classes, like negative class or positive class. Here, a technique for the sentiment analysis of the movie reviews from community media is introduced. Hybrid Corrective Critic Neural Network is used for the purpose. The movie reviews collected from the community media are fed to pre-processing for removing unnecessary and redundant words from the data. After pre-processing, the feature extraction is carried out. Sentiment related features, such as Hashtags, All - caps, Emoticons, sentiment lexicon and elongated units from the reviews are used. Based on the extracted features, the sentiment classification is performed by Hybrid Corrective Critic Neural Network. Evaluation is carried out on the output generated by the developed technique based on metrics sensitivity, specificity and accuracy. The reviews from the movie review database are used for training the approach. The proposed Hybrid Corrective Critic Neural Network based sentiment classification is found to be effective with accuracy of 74.42%.
Sketch Image Classification Using Component Based k-NN

Recently, several researches on recognition of novice-users' hand-drawn images have been conducted, specifically those on novel feature extraction techniques. Generation of a robust sketch descriptor is one of the most challenging problems in hand-drawn image recognition. Drawing or sketching is a common skill that everyone can do to a varying degree of success. Although an individual may or may not be able to create a beautifully drawn image, any human beings can recognize the shape of each part of a hand-drawn object and the entire object. In this work, we proposed a sketch image classification method that creates an image descriptor from its own components and uses k-NN algorithm for learning/classification. A sketch image can be created simply by drawing some simple geometric shapes. The drawing order, size, and number of the shapes are features that can be extracted and used to recognize an image. After an object in a drawn image has been classified as a car or a human being or anything else, the recognized object can be selected and paired with the motion associated with it. It was found that our proposed method was able to achieve a recognition accuracy rate of 92.33%. We also did a survey with children 7-12 years of age asking them whether they wanted an easy tool that can animate their hand-drawn objects and got almost unanimous affirmative responses from them.
Design and Implementation of Chatbot Framework For Network Security Cameras

In recent years, with the development of science and technology Internet Protocol camera is getting and getting popular and widely used. In this paper, we present a chatbot framework to help user get the human detection information from the cameras via Facebook messenger instead of observing 24/7 called Security Bot (Sbot). To build Sbot, we design a system including camera network, Human Detection Server (HDS), and Sbot server. In the system, Sbot transfer information between user and HDS using Facebook Messenger Platform. In the human detection task, we use SSD-MobileNet1 network architecture for detecting human in real-time and updating dataset for retraining using transfer learning method with more case taken from surveillance camera.
Visualizing the Stability of 2D Point Sets from Dimensionality Reduction Techniques

Abstract We use k ‐order Voronoi diagrams to assess the stability of k ‐neighbourhoods in ensembles of 2D point sets, and apply it to analyse the robustness of a dimensionality reduction technique to variations in its input configurations. To measure the stability of k ‐neighbourhoods over the ensemble, we use cells in the k ‐order Voronoi diagrams, and consider the smallest coverings of corresponding points in all point sets to identify coherent point subsets with similar neighbourhood relations. We further introduce a pairwise similarity measure for point sets, which is used to select a subset of representative ensemble members via the PageRank algorithm as an indicator of an individual member's value. The stability information is embedded into the k ‐order Voronoi diagrams of the representative ensemble members to emphasize coherent point subsets and simultaneously indicate how stable they lie together in all point sets. We use the proposed technique for visualizing the robustness of t‐distributed stochastic neighbour embedding and multi‐dimensional scaling applied to high‐dimensional data in neural network layers and multi‐parameter cloud simulations.
Video super resolution using non-linear regression and deep learning

In this paper, a hybrid model is developed by integrating a non-linear regression model and optimization-driven deep learner for video super resolution. Initially, the low-resolution frames are subjected to framing, and each frame is provided to both Fractional-Group Search Optimizer-based Deep Belief Network (FrGSO-DBN) classifier and the nonlinear regression model. Then, the output frames of both methods are averaged by the proposed hybrid model and the enhanced video frame is generated. Here, the FrGSO is developed by modifying the update process of the GSO using the fractional theory, to train the DBN such that the weights in the DBN are selected optimally. Finally, the simulation results reveal that the proposed hybrid model achieves high values of 0.8697, 25.637 dB, 0.9171, and 49.821 dB, for the metrics Structural Similarity Index Measure, Peak Signal to Noise Ratio, Feature Similarity Index Measure, and Second Derivative like Measurement of Enhancement, respectively.
Gesture Tracking and Locating Algorithm Based on Federated Tracking Filter

Abstract In this paper, a joint gesture tracking method combining particle filter and mean shift algorithm is proposed to improve the accuracy and robustness of the system. During the slow movement of the human hand, the average movement of the particles is first used to cause most of the particles to drift into the gesture area. In the case where the movement of the human hand is faster or there is occlusion, when the mean shift of the particle is performed, if the region of the gesture is not detected, the particle will return to the state before the drift, and then the next frame is processed. The method can directly calculate the position of the gesture based on the particles used for subsequent testing, and can save the tracking time of the algorithm. Through experimental simulation, compared with the Cam-shift algorithm, when the sampling point of the joint tracking algorithm proposed in this paper is 200, the tracking accuracy is improved to 95.2%. Compared with 90.6% of the Cam-shift algorithm, the tracking time is reduced from 83.7 ms to 25.8 ms. Therefore, the proposed algorithm can greatly improve the tracking accuracy and real-time, and can also effectively reduce the impact of complex environments on the tracking effect.