Reviewerly/exHarmony huggingface dataset:
Intensifying Stress Perception Using Visual Effects in VR Games

Visual effects are used in commercial video games to enhance the players' perception without being investigated in terms of their actual impact on the players. In our work, we implemented five visual effects, namely vignetting, image noise, chromatic aberration, color grading, and blur and investigated their impact on the players' perceived stress, presence, and simulator sickness. A user study (N = 6), conducted with a virtual reality (VR) head-mounted display (HMD), shows that these visual effects can intensify the players' stress perception without negatively effecting perceived presence and without increasing simulator sickness.
Application of dynamic association rules in network data mining

Under the background of Internet big data era, the amount of data information in all walks of life is increasing day by day. How to filter, mine and manage different kinds of data information has become an important topic of network communication, public service data analysis, privacy protection and other activities. This paper mainly discusses the algorithm of association rules mining based on Apriori algorithm. The association algorithm is used to process the raw data and privacy data of different network data sets, and dig out the multi-level frequent itemsets in the database.
Correction of converging verticals distortion due to camera pose and position

This paper discusses the development of a vision-based intra-row navigation system for an agricultural robot. The development of this system was used to reduce the effects of converging verticals distortion due to the inclined, forward facing pose and position of the navigation vision system of the agricultural robot. The mitigation of this distortion was achieved through relating the homography between the image plane view and the ground plane projection of the vision system's field of view (FOV) to create an auxiliary view of the crop. This auxiliary view represented an aerial view of the visible crop area within the camera's FOV. This aerial view was used to discern crop row geometry when used in conjunction with a dual segmentation technique to isolate crop plants from foreign vegetation. Through the implementation of this system, mitigation of converging verticals distortion and successful crop geometry detection for the intra-row navigation of an agricultural robot was achieved.
Conversational Agents for Recommender Systems

In my Ph.D. work, my objective is to improve the state of the art in Conversational Recommender Systems, by proposing a model that closely follows the process that people enact when searching for products and services. Rich user profiles are elicited using natural language dialogue. Item descriptions will be extracted from a combination of structured and unstructured data such as user reviews. Natural language explanations will ensure that users can quickly understand the reasoning behind the recommendations. Interactive explanation will then allow them to further compare several alternatives. This extended abstract presents the motivations of my work, it details the research plan, and the research questions. Finally, it shows some preliminary results, and outlines the next steps for my Ph.D. program.
The Basics of Communicative AI

In this chapter, we propose a working definition of AI to narrow the inquiry to chatbots. We discuss the requirement of embodiment for constructing AI and reject its importance. After a short review of chatbots development, we discuss whether the current method of AI construction, machine learning, will remain a promising approach. Due to the increasing size of linguistic base models and more refined algorithms for open-domain chatbots, this is affirmative. Lastly, we discuss the Turing test as an estimate for the intelligence of chatbots and reject its relevance.
Collaborative Filtering-Based Film Recommendation Technique Utilizing Time and Film Genres

Abstract Due to the uncertainty of users’ preferences for different movies at different times, movie recommendation poses a huge challenge. We present an improved collaborative filtering algorithm for film recommendation whose design was motivated by a need to extract meaningful information for further recommending from film viewing applications. To this end, the method we propose utilises an improved method for analyzing user interest in film genres, which can then be subsequently used to predict users’ ratings of unwatched movies in combination with the current viewing time. In our new algorithm, we extract genre information considering time decay factor and combine it with matrix factorization collaborative filtering algorithm, and propose a novel algorithm structure, the MFTGICF algorithm. Compared with general traditional collaborative filtering algorithms and some other methods, this algorithm has more precision and higher stability. Both the theoretical analysis and extensive experiments show the improvement of the effectiveness and efficiency of the proposed method.
Testing large convex aspheres using a single wedge compensation and stitching method

Testing large convex aspheric mirrors has always been a challenging metrology problem in optics. In response to this problem, a modified method that utilizes single wedge compensation to measure large convex aspheric mirrors is proposed in this paper. The principles of this method and aberration theory are first analyzed. Two simulation examples are designed to verify the method's applicability, and the sensitivity of this configuration is also analyzed, which proves that the accuracy of the configuration can be better than 0.006 λ (3.8 nm) with careful alignment and calibration. These examples and this analysis show that this configuration is feasible and flexible.
Functional Clustering Approach for Analysis of Concentration

In this research, we study characteristics of concentration variation with functional data analysis. Functional data analysis, which combines traditional data analysis with the characteristics of functions, is suitable to analyze changing trend of observed data with the utility of derivatives. We applied functional clustering analysis to milk dataset, which contains milk concentration, sum of rain, average speed of wind, and average temperature, to reveal the relationship between milk concentration and other variables. We get eight dendrograms in functional clustering analysis. Cophenetic correlation coefficient is used to measure the similarities among the dendrograms. Multidimensional scaling is used to visualize the dissimilarities of the dendrograms clearly. As a result, we find that the trend of milk concentration has relation to those of sum of rain and of average speed of wind.
Investigation of the capabilities of convolutional neural networks in object classification problems

Abstract This paper considers convolutional neural networks to solve the problem of improving the quality of images compressed by the JPEG algorithm. To analyze the qualitative characteristics of networks, the convolutional network L04 is implemented using the Caffe framework. By choosing the optimal optimization algorithm for training the network, its qualitative characteristics have been improved.
Generalized NetET Modelling of the Payment Process Workflow

In this paper we are going to explain all the payment statuses in a payment work-flow and their exact meaning. All these status flags are important since they make the process flawless, each one have an exact meaning and it is a part of a larger mechanism that is responsible for the payment process. These label (tags) are used as labels and they cannot just change the values of a credit card or make a refund or a resubmission. This can only done by the gateway interface software. This paper is continued from a previous work [4] and examines in depth the status flags used in a payment process so that a payment can be submitted, processed and troubleshot if necessary.
67‐1: <i>Distinguished Paper:</i> Efficient Multi‐Quality Super Resolution Using a Deep Convolutional Neural Network for an FPGA Implementation

We propose an efficient deep convolutional neural network for a super‐resolution which is capable of multiple‐quality input, by analyzing the input quality and choosing appropriate features automatically. To implement the network in an FPGA and an ASIC, we employ a network trimming technique to compress the neural network.
A Knowledge Reasoning Algorithm Based on Network Structure and Representation Learning

Representation learning of knowledge graphs is widely used in the field of artificial intelligence, but it is currently far from comprehensive. All previous translation-based models ignore the multi-relational network structure attributes. Inspired by the law of gravitation, a novel algorithm strTransE proposed by us to integrate the network structure attributes into representation learning. In strTransE, the relation is the gravity between entities. The magnitude of gravity is determined by the weight of the entity. In order to solve the gravitational quantification, we introduced the network structure attribute to represent the weight of the entity. Experimental results on datasets WN18 and FB15K show that, our algorithm achieves significant and continuous improvements in link prediction.
Extractive Text Summarization Methods in the Spanish Language

The quantity of information in the world is increasing every day on a fast level. This fact will be an obstacle in some situations; text summarization is involved in this kind of problem. It is used to minimize the time that people spend searching for information on the web and in a lot of digital documents. In this chapter, three algorithms were compared; all of them are an extractive text summarization algorithm. Popular libraries that influence the performance of these kinds of algorithms were used. It was necessary to configure and modify these methods so that they work for the Spanish language instead of their original one. The authors use some metrics found in the literature to evaluate the quality and performance of these algorithms.
Research on Data-driven Models of Teaching Quality Evaluation

In order to realize the data-driven teaching quality evaluation activities and the construction of its system, we must deeply study the relevant models used to guide its algorithm selection, evaluation implementation and system integration. This paper focuses on the analysis model, process model and system model of data-driven teaching quality evaluation. The analysis model is used to form the reference model of how to deal with relevant data and provide reference for data processing. In this part, the cluster analysis, association analysis and prediction analysis are discussed in detail; the process model is used to form the teaching quality evaluation cycle of evaluation to promote learning, which provides reference for organizing evaluation activities, this part analyzes the teaching activities, evaluation activities, intervention activities and their interrelations in the process model. The system model is used to form a reference mode for guiding the design, construction and transformation of teaching quality evaluation big data system, which provides reference for system implementation, this part analyzes the functions of data acquisition layer, data management layer, data interface layer, data processing layer, business application layer and service interface layer.
78‐2: Using Physical Books as Interfaces to Digital Displays

A new form of interaction with digital displays is described, using the pages and binding of a physical book as the interface. This leads to a form of augmented book, or a‐book, which can seamlessly trigger multimedia content on a nearby device using embedded light, pressure or touch sensors.
Research on Intelligent Lighting System of Concentrator Based on NRF24L01P

Aiming at the shortcomings of high communication cost and power consumption in the existing intelligent lighting system, an intelligent lighting system based on low power wireless transceiver chip NRF24L01P is studied. The overall architecture of the system is given and the realization of system functions is introduced. The system adopts NRF24L01P networking technology to realize interconnection and intercommunication between LED lighting devices, which reduces the cost and power consumption of wireless communication; converts Wi-Fi protocol into NRF24L01P communication protocol by designing IoT router, enables user terminal to remotely control and monitor LED The lighting device and the fuzzy PID algorithm are used to improve the stability of the system. The solar temperature difference power supply based on the thermoelectric power generation chip is studied to realize the green environmental protection for the IoT router.
Framework of Multiple-point Statistical Simulation Using Manifold Learning for the Dimensionality Reduction of Patterns

Multiple-point statistics (MPS) has been successfully used for stochastic simulation by reproducing the features from training images (TIs) to the simulated regions. However, because these features mostly have intrinsic nonlinear relations, the MPS methods using linear dimensionality reduction are not suitable to handle nonlinear data. Since manifold learning methods can discover the intrinsic features of high-dimensional data by mapping them to a low-dimensional manifold, a framework using manifold learning for dimensionality reduction in MPS is proposed to resolve this issue, in which manifold learning methods are integrated with MPS to properly reduce the dimension of patterns from TIs so that the subsequent simulation can be more accurate. Besides, the proposed framework is designed for different situations of conditional data because of the great influence on simulation from conditional data. Tests are compared between manifold learning methods and linear method of dimensionality reduction, showing that compared with the typical linear dimension reduction method, the framework has obvious advantages in simulation quality.
Molecular Surface Estimation by Geometric Coupled Distance Functions

Estimating the surface from given atoms with location and size information is a fundamental task in many fields, such as molecular dynamics and protein analysis. In this paper, we present a novel method for such surface estimation. Our method is based on level set representations, which can efficiently handle complex geometries. The proposed method is analyzed from mathematical point of view and from computation point of view. The method does not require any prior information about the surface. This property is fundamentally important for the surface estimation task. The presented method is evaluated on both synthetic and real data. Several numerical experiments confirm that our method is effective and computationally efficient. Finally, the method is applied on protein surface estimation. This method is suitable for high performance molecular dynamics study, protein surface analysis, etc.
Unsupervised Object-aware Learning from Videos

We consider a novel unsupervised learning setup in which training examples are grouped into small bundles that preserve an identity of an object. Such setup may practically arise when we are able to detect moving objects in videos without being able to classify their identity. Our approach is based on a construction of a similarity graph of bundles from which we are able to recover the identities of objects by applying a community detection algorithm. Finally, we train Siamese Neural Network to discriminate examples from different components and show that thus acquired representations produce well-separated clusters. Part of our contribution is also a unique dataset we assembled in order to test the presented idea.
The Fast Image Recognition System Based on Neuro-Fuzzy Units and its Online Learning for Data Stream Mining Tasks

In recent years, the need for real-time pattern recognition applications has sharply increased. Along with deep and probabilistic neural networks, hybrid architectures such as neo-fuzzy networks and networks based on neo-fuzzy units turned out to be quite effective for solving this problem. In this paper, we consider the task of developing an architecture and a learning algorithm for a real-time pattern recognition system built on neo-fuzzy units. The objective function based on cross-entropy allows formulating a learning criterion that provides a high learning rate.
Extended Abstract: Everything Old Is New Again

The 2015 report Skills, Challenges, and Trends in Instructional Design published by the Association for Talent Development (ATD) and its partners identified fourteen “emerging trends” in instructional design. These trends include: adaptive learning (e.g. personalized learning), badges, designing content for use in various cultures or regions, game-based learning; infographics, learning analytics, mobile learning, MOOCs, QR codes, social media and social learning, storytelling, virtual simulation, wearable technologies (e.g. Google Glass), and 3-D printing.
Improving the Behavior of Evasive Targets in Cooperative Target Observation

The Cooperative Targets Observation (CTO) problem consists of two groups of agents: observers and targets. The observer agents aim to maximize the Average Number of Observed Targets (ANOT) in environments where there are more targets than observers. In most of the approaches to this problem, the behavior of the target agents is very simple, out of reality in competitive multiagent environments. More recently, two strategies improved the behavior of the targets when under observation, i.e., the straight-line strategy and controlled randomization. However, in a surveillance scenario, it is reasonable to assume that targets can be modeled as an organization, with rules, structures of authorities and relationships, and rationality to try to predict the behavior of observers. The objective of this work is to propose and validate four strategies for the team of target agents in the CTO problem, three involving clustering algorithms and two organizational paradigms and one using neural networks. The approaches were implemented and tested on the NetLogo agent-based simulation platform. The results showed that target team performance increased considerably when these were modeled as rational agents in an organization and able to try to predict the behavior of their observers.
Optimization of Parallel Applications Under CPU Overcommitment

As cloud computing is widely used, even parallel applications run in virtual machines (VMs) of clouds. When CPU overcommitment is performed in clouds, physical CPU cores (pCPUs) can become less than virtual CPUs (vCPUs). In such a situation, it is reported that application performance degrades more largely than expected by the decrease of pCPUs available to each VM. To address this issue, several researchers have proposed optimization techniques of reducing the number of vCPUs assigned to each VM. However, their effectiveness is confirmed only in a limited VM configuration. In this paper, we have first investigated application performance under three configurations and revealed that the previous work cannot always achieve optimal performance. Then we propose pCPU-Est for improving application performance under CPU overcommitment. pCPU-Est dynamically optimizes the number of vCPUs on the basis of correlation between CPU utilization and execution time (dynamic vCPU optimization). In addition, it dynamically optimizes the number of application threads when possible (thread optimization). According to our experiments, dynamic vCPU optimization improved application performance by up to 42%, while thread optimization did by up to 72x.
A Cognitive Approach to Parsing with Neural Networks

According to Cognitive Grammar (CG) theory, the overall structure of a natural language is motivated by a relatively small set of domain-independent cognitive abilities. In this paper, we draw insights from CG to propose an approach to natural language parsing with little syntactic annotation. A sentence functions as a cohesive whole because its parts are meaningfully linked. We propose that every part of a sentence can be analysed along three axes: composition, interaction and autonomy. When two expressions semantically correspond in all the three axes we call them cohesive. We present an algorithm that reads parts of sentences incrementally, recognises their construction schemas along the three axes, assembles any two component schemas into one composite schema if they are cohesive, parses a span of text as incrementally successive assembly of components into composites, retains multiple running parses within the span and chooses the best parse. The basic construction schema definitions and their patterns of assembly are implemented as dictionary-cum-rules because they are fewer in number, largely language-independent and can be extended to handle language-specific variations. A basic feedforward neural network component was trained to learn all valid patterns of assemblies possible in a span of text and to choose the best parse. A successful parse exhausts all the words in the sentence and ensures local cohesion and assembly at every stage of analysis. We present our approach, parser implementation and evaluation results in Welsh and English. By adding WordNet synsets we are able to show improvements in parser performance.
Mincer Algorithm: Privacy Protection for Publishing Trajectories Dynamically

Some applications require real-time analysis of trajectory data. But the direct release of the original trajectory to a third party for analysis will threaten the privacy of the trajectory subject. So the protection of trajectory privacy published dynamically is important. In this paper, a Mincer algorithm is proposed, which realizes the dynamic publishing of trajectories through the Translocation policy of trajectory points. The algorithm is low delay and reduces the information loss. The effectiveness of the algorithm and the usefulness of the published data are verified by experiments.
Semantic Information Detection of Webpage Based on Word Vector and Infomap

For Chinese web pages, we use regular expression and Viterbi algorithm to realize Chinese filtering and word segmentation, then use ngram2vec algorithm to get the word vector set of web page and pre train the word vector set of Baidu Encyclopedia. Baidu Encyclopedia word vector set is based on Infomap clustering algorithm to realize word vector Clustering and tagging types, training neural network through training data set and Baidu Encyclopedia corpus to determine the type of unknown web pages through neural network, and achieve the purpose of detecting the semantic information of unknown web pages. This algorithm is has few super parameters and high calculation efficiency. Experiments show that the accuracy of the trained neural network model reaches 96.73%, which can quickly and accurately identify the type of web page.