Reviewerly/exHarmony huggingface dataset:
Understanding and Modelling Human Attention for Soft Biometrics Purposes

Soft biometrics systems have spread among recent years, both as a mean to empower classical biometrics, as well as stand-alone and complete solutions, with several application scopes ranging from digital signage to human-robot interaction. Among all, in the recent years emerged the possibility to consider as soft biometrics also the temporal evolution of both the human attention and the emotional states, and some recent works in the literature partially explored this exciting research line making use of either expensive tools or RGB-D devices. This work is instead the first attempt to perform soft biometrics identification of individuals on the basis of data acquired by a consumer camera, looking at users' attention evolution in time. The experimental evidence of the feasibility of using the proposed framework as soft-biometrics is given on a set of 22 users recorded by a tablet front-facing camera while watching, in an unconstrained mobile setting, a video running on the tablet screen.
Analysis and Compensation of Color Chromatic Aberration

The refractive index of a lens varies for different wavelengths of light, which causes chromatic aberration. Although most camera lens are achromatic, but the residual chromatic aberration still decrease the image's quality and the measurement accuracy seriously. In this paper, the camera lens chromatic aberration models for commercial camera lens are analyzed by experiments, and the optimized model is determined. According to the model, method for compensating chromatic aberration is given in this paper. The experiments have shown that the proposed method can eliminate the influence of chromatic aberration effectively.
Towards Intelligent Interactive Theatre: Drama Management as a Way of Handling Performance

In this paper, we present a new modality for intelligent interactive narratives within the theatre domain. We discuss the possibilities of using an intelligent agent that serves as a drama manager and as an actor that plays a character within the live theatre experience. We pose a set of research challenges that arise from our analysis towards the implementation of such an agent, as well as potential methodologies as a starting point to bridge the gaps between current literature and the proposed modality.
Minkowski Metric Based Soft Subspace Clustering with Different Minkowski Exponent and Feature Weight Exponent

Soft subspace clustering (SSC) methods can simultaneously performance clustering and find the subspace where each cluster lie in. A Minkowski metric based SSC (MSSC) algorithm recently is proposed to improve the adaptability of SSC to data. The empirical results have shown its favorable performance in comparison with several other popular clustering algorithms. However, this algorithm has the following two main defects: (1) The role that the Minkowski exponent \(\beta \) in MSSC plays is not clear. And the Minkowski exponent \(\beta \) is set as the same as the feature weight exponent \(\alpha \) in MSSC that may lead to MSSC missing better clustering performance. (2) the steepest descent method based MSSC (SD-MSSC) is computationally expensive for large data. In this paper, a general formulation for MSSC is presented, in which the Minkowski exponent \(\beta \) can be set not equal to the feature weight exponent \(\alpha \). A novel algorithm for computing the clustering centroids in MSSC is presented using the fixed-point iteration (FPI) method. The FPI based MSSC (FPI-MSSC) algorithm is more efficient than the SD-MSSC algorithm, and it becomes a noise-robust SSC procedure when the \(\beta <2\). Extensive experiments on real-world data sets are presented to show the effectiveness of the proposed algorithm.
Understanding Lexical Features for Chinese Essay Grading

Essay grading is an important and difficult task in natural language processing. Most of the existing works focus on grading non-native English essays, such as essays in TOEFL. However, these works are not applicable for Chinese essays due to word segmentation and different syntax features. Considering lexical features are important for essay grading, in this paper, we study the expert evaluation standard and propose an interpretable lexical grading method for essays. We first study different levels of vocabulary provided by experts and introduce a quantitative evaluation framework on lexical features. Based on these standards, we quantify the Chinese essay dataset of 12 education grades in primary and middle schools and propose a set of interpretable features. Then a Bi-LSTM network model is proposed for semantically grading essay, which accepts a sequence of word vectors as input and integrates attention mechanism in terms of lexical richness. We evaluate our method on real datasets and the experimental results show that it outperforms other methods on the task of lexically Chinese essay grading. Besides, our method gives interpretable results, which are helpful for practical applications.
FreeMo: Extending Hand Tracking Experiences Through Capture Volume and User Freedom

Camera-based hand tracking technologies can enable unprecedented interactive contents with gripping impressions of embodiment and immersion. However, current systems are often restricted by the tracking volume of their sensors which results in limited experiences and content creation possibilities. We present two demonstrations of FreeMo, a hand tracking device that extends the interaction space of a Leap Motion by augmenting it with self-actuating capabilities to chase after the user’s palms. By doing so, we can offer content featuring significantly enhanced user freedom and immersion as showcased by our applications: an interactive room-scale VR experience with hand tracking from head to lap, and two-person competitive virtual air hockey game.
Fitness Critics for Multiagent Learning: Extended Abstract

This paper presents fitness critics as noise-reduced fitness evaluation functions for cooperative coevolution of multiagent teams. Noise in the evaluation function can have a negative impact on evolutionary learning, especially in multiagent learning. The evaluation function for an individual agent can be noisy as each agent in the team changes their policies independently. Existing noise-reduction methods are not suitable for efficient evolutionary algorithms. The noise-reduction provided by actor-critic methods cannot be directly applied to the evolutionary algorithms when learning policies in episodic problems as the critic provides stepwise evaluations but evolutionary algorithms require episodic fitness evaluations. Fitness critics can provide noise-reduced episodic fitness evaluations by aggregating the multiple stepwise evaluations of an intermediate critic. With demonstrations on the tightly coupled multi-rover domain, this paper shows that teams trained with fitness critics achieve comparable or increased team performance scores compared to teams trained with other noise- reduction methods.
A Deep Learning Based Reasoner for Global Consistency in Named Entity Recognition

Named Entity Recognition (NER) is a basic task of Natural Language Processing (NLP), it’s a challenging task in a variety of special applications. This paper aims to solve the global consistency of NER, and to improve the performance. Inspired by human reading process, we propose a NE-Reasoner model, which combine deep neural networks and memory artificial neural network to identify named entities with global consistency. The advantages of the model are: (1) The multi-layer deep architecture, allowing it to bootstrap the recognized entity set from coarse to fine. (2) The candidate pool memory mechanism, allowing it to exchange identified entity information between layers. (3) The reasoner, combing encoder-decoder and cached information to infer to get global entities. The experimental results show that the NE-Reasoner can identity ambiguous words and named entities that rarely or never met before.
Developing User Persona Based on the Factors of Visitor Recreation Activities in Hongneung Experimental Forest

Much research has been conducted on user behavior by taking surveys and interviews to plan the green space effectively. However, there is a limitation in understanding detailed user characteristics such as personalities and values. This study applied the Persona-based Scenario Method (PSM) to Hongneung Experimental Forest to understand the detailed needs and behaviors of the users in the forest recreation area. The PSM is a user experience modeling technique, which tries to understand the users by describing the type of users as real people. This study 1) extracts the factors of visitor recreation activities in Hongneung Experimental Forest based on the results of the survey, 2) develops user personas based on the results of survey and comes up with activity factors, and 3) designs user scenarios. As a result of applying the PSM, 64 factors of visitor activities were derived from the observation survey in 14 sites of Hongneung Experimental Forest and 25 key factors of visitor activities were chosen through observerâs brainstorming. Second, three types of personas were developed considering the key factors and the results of user characteristics with quantitative and qualitative analysis. Lastly, context scenarios were designed by applying the key factors of visitor recreation activities to the persona model. We identified the design problems of the space and design requirements through the scenarios. This study has significance in that it takes an approach from the user perspective and was applied to the forest recreation area, which was mainly used in product design. The developed personas could be used for deriving design elements and setting the direction for planning considering detailed needs, behaviors and characteristics of users. Key words: characteristics, design, forest, PSM(persona-based scenario method), user behavior
Invariance Matters: Person Re-identification by Local Color Transfer

Person re-identification is a complex image retrieval problem. The color of the image is distorted due to changes in illumination, etc., which makes pedestrian recognition more challenging. In this paper, we take the conditional image, the reference image and its corresponding clothing segmentation image as input, and then restore the true color of the person through color conversion. In addition, we calculate the similarity between the conditional image and the image dataset by the chromatic aberration similarity and the clothing segmentation invariance. We evaluated the proposed method on a public dataset. A large number of experimental results show that the method is effective.
Xword: A Multi-lingual Framework for Expanding Words

The word expansion task has applicability in information retrieval and question answering systems. It relieves the vocabulary mismatch problem leading to a higher recall. The recent word embedding models demonstrated merit for the word expansion task in comparison to the traditional n-gram models. However, to acquire quality embeddings in each language, the processes of corpus compilation, normalization and parameter tuning are time-consuming and challenging especially for poor resources languages such as Arabic. In this paper, we introduce Xword as an online multi-lingual framework for automatic word expansion. Xword relies on both pre-trained ad hoc word embedding models and n-gram models for the expansion task. Xword currently includes the two languages Arabic, and German. Xword represents the results of each model both individually and collectively. Additionally, Xword can filter out the result set based on sentiment and part of speech (POS) tag of every single word. Xword is available as a Web API along with the downloadable models and sufficient documentation on our public GitHub.
Research on Auxiliary Construction System of Transmission Engineering Based on 3D Digital Achievements

Abstract In order to take full advantage of the three-dimensional digital design data and solve the traditional construction schemes’ problems better, characteristics of three-dimensional digital data and the shortcomings of traditional construction management methods in the paper are analyzed. The organization management mode of 3D digital results is studied to construct a full information 3D model library by combing the relationship between models, attributes, documents, drawings, etc. On the basis of this, combined with the construction process of transmission engineering, a three-dimensional auxiliary construction application platform for transmission engineering based on three-dimensional digital design results is established. Through the software platform to manage, process, analyze and mine massive data, on the one hand, the integration, interaction and sharing of multi-source heterogeneous data are realized; on the other hand, the three-dimensional digital technology is applied to the construction process to realize the three-dimensional visualization of the construction process of the transmission engineering, thus providing a new solution for solving the construction problem.
Distributed Training of 3DPyranet over Intel AI DevCloud Platform

Neural network architectures have demonstrated to achieve impressive results across a wide range of different domains. The availability of very large datasets makes possible to overcome the limitation of the training stage thus achieving significant level of performance. On the other hand, even though the advancements in GPU hardware, training a complex neural network model still represents a challenge. Long time is required when the computation is demanded to a single machine. In this work, a distributed training approach for 3DPyraNet model built for a specific domain, that is the emotion recognition from videos, is discussed. The proposed work aims at distributing the training procedures over the nodes of the Intel DevCloud Platform and demonstrating how the training performance are affected in terms of both computational demand and achieved accuracy compared to the use of a single machine. The results obtained in an experimental design suggests the feasibility of the approach for challenging computer vision tasks even in presence of limited computing power based on exclusive use of CPUs.
rasdaman

Flexible, scalable services on massive geo data receive much attention today. In particular, the OGC Web Coverage Service (WCS) and Web Coverage Processing Service (WCPS) standards suites have established a best practice for versatile access and analytics on spatio-temporal "Big Data".
Employing Real-Time Game Technology for Immersive Experience (VR and Videogames) for all at MAIO Museum

3D real time game technologies create an opportunity to design interactive immersive experiences developing affordable, easy-to-use, and incredible virtual worlds for Museums. This chapter presents the potential of these technologies for the development of edutainment content for their visitors at MAIO - Museum of Art Taken Hostage in Cassina de' Pecchi (Milan). The Museum presents the story of 1,623 masterpieces such as Michelangelo, Tiziano, Raffaello, and Canaletto that were stolen in Italy during World War II and never found again. Visitors can explore the artworks through 2 installations: MAIO Virtual Museum, through VR inside an oniric 3D environment, and MAIO Play, a multiplayer video game.
M-Voting with Government Authentication System

In the development of advanced mobile technologies, the historic balloting method can be replaced to a modernistic and potent scheme titled as mobile voting. The mobile voting system affords an uncomplicated, beneficial, powerful way to vote wipe out the drawback of traditional approach. In this paper we urge a mobile voting system which is in essence an online voting system over which users can cast their vote through their smartphones or by applying an e-voting network page. To bring out the security, OTP accession is used which is most generally on the network to confess the variation between a human using a network services automated bot thus effecting the network page more guarded against spam-bot attacks. If the consequence of the matching algorithm is three-point match then audits whether this person possess voter ID after that it will authorizes with AADHAAR ID, if he has the right to vote then a balloting form is granted to him and the third level of attestation is carried out by using One Time Password (OTP) and then the biometric recognition of fingerprint sensor is used for an authentication. Nowadays technology is being utilized progressively as a key to contribution voters to cast their ballots. In order to utilize the rights, approximately all voting system around the world embodies the steps: balloter identification and authentication, voting and documenting of votes expulsion, vote toll, notification of election results.
Video Authentication using Shot Boundary Detection Technique-An Overview

Abstract Video is the most challenging and has lots of issues than other type of multimedia data and video authentication is one of the popular issues regarding the video. Technologies of communication nowadays can simplify sharing of multimedia data such as text, images and videos through online or offline and a wide spread of video content its significant to assure the integrity and originality of video information. Recently, a lot of cases have been stated regarding an illegal activity in the video. Such as, video editing or video tampering attacks to ruin the authenticity of the video. With only minor alterations could change the meaning of the video. Video authentication detection is a popular issue and of high importance in various fields such as in forensic investigations, court of law and video surveillance. A lot of techniques are proposed by previous study to assure the originality of video in their own way. In this study, it focuses to detect tampering attacks and alterations in video using shot boundary detection techniques. Shot boundary detection is a very challenging task and it’s commonly used for structured video because these techniques can detect the changes made with normal shot change rate. Meanwhile, this research will emerge to produce new model for video structure detection using shot boundary detection with the histograms feature that is significant to design of powerful tools to detect an originality of video and to locate the tampering attacks in video.
A Modified Method Using Fusing Weighted Filter for Faded Image Dehazing

A novel method for which is an uncommon mix for different accessible strategies likewise it has new methodology so as to picture extending and picture adjustment. In this work concealing picture dehazing procedure reliant on merging a perceptible and a nearby Haze (NIR) pictures for a comparative scene. In outside scenes, picture separation for got pictures is most likely going to be lost by dimness. Close Haze light is less scattered than detectable light thus for its long wavelength; in like manner NIR pictures have unpredictability enough. Our philosophy restores picture separate by merging point for intrigue parts for NIR picture into recognizable picture. To turn away overemphasizing haze free regions by NIR information, we present weighting NIR picture using transmission map. Exploratory results show that our system beats customary techniques.
Aspect Extraction and Sentiment Analysis for E-Commerce Product Reviews

Throughout the globe, with the immense increase of the number of users of the internet and simultaneously the massive expansion of the e-commerce platform, millions of products are sold online, and users are more involved in shopping online. To improve user experience and their satisfaction, online shopping platform enables every user to give their feedback, rating, and review for each and every product that they buy online to help other users. Some popular products on a leading e-commerce platform have of thousands of reviews. Many of those reviews are long and contains only a few sentences which are related to a particular feature of a product. Thus, it becomes really hard for a customer to understand a review and make a decision in buying that product. Manufacturer also need to keep track of customer review regarding the different features of the product to improve the sales of poorly performed one. It becomes very difficult for the user and manufacturer of the product to understand customer view about different features of the product. So, we need accurate opinion-based product review sentiment analysis which will help both customers and product manufacturer to understand and focus on a particular aspect of the product.
Congenial Benchmarking of RDF Storage Solutions

Many SPARQL benchmark generation techniques rely on SPARQL query templates or on selecting representative queries from a set of input queries by inspecting their syntactic features. Hence, prototype queries from such benchmarks mainly capture combinations of SPARQL features, but not the semantics nor the conceptual association between queries. We present congenial benchmarks---a novel type of benchmark that can detect conceptual associations and thus reflect prototypical user intentions when selecting prototype queries. We study SPARROW, an instantiation of congenial benchmarks, where the conceptual associations of SPARQL queries are measured by concept similarity measures. To this end, we transform unary acyclic conjunctive SPARQL queries into ELH-description logic concepts. Our evaluation of three popular triple stores on two datasets shows that the benchmarks generated by SPARROW differ considerably from benchmarks generated using a feature-based approach. Moreover, our evaluation suggests that SPARROW can characterize the performance of common triple stores with respect to user needs by exploiting conceptual associations to detect prototypical user needs.
Historical aspects of the rocket and space industry enterprises participation in professional training of engineering personnel

A brief analysis of genesis was made in regard to the forms of cooperation between the rocket and space industry enterprises and the higher educational institutions in professional training of engineering personnel. Concrete examples are provided to demonstrate development of these forms until the recent times.
Comparative Analysis of Approaches to Building Medical Dialog Systems in Russian

Nowadays dialog systems have great promise in the field of medicine and healthcare. Only a few medicine dialog systems presented in the literature are accompanied by an experimental effectiveness evaluations. Moreover, they cover only English-speaken context. In this paper we set the task to conduct a comparative analysis of the effectiveness of Russian-language mixed-initiative medical dialog systems, depending on the chosen architecture and method of processing the users' intentions. We have developed and compared three types of chat-bots; Frame-based, ML-based and Ontology-based. As the metrics used the accuracy of the intent recognition as the percentage of intents correctly defined by the system relative to the total number of processed utterances. The results show that the accuracy of the intent recognition in all three approaches is quite the same, so finally we propose the architecture of s combined dialog system which covers all the needs for Russian-language medical domain.