Reviewerly/exHarmony huggingface dataset:
The Development Trend of Intelligent Speech Interaction

To make the computers have capabilities of listening, speaking, understanding and even thinking is the latest development direction of human-computer interaction. As one of the most convenient and natural ways for communication, speech has become the most promising way of human-computer interaction in the future, which has more advantages than other interaction ways. As one of the most popular artificial intelligence (AI) technologies, intelligent speech interaction technology has been widely applied in many industries such as electronic commerce, smart home and intelligent industry as well as manufacturing. It will change the user behavior habits and become the new mode of human input and output. In this paper, we state the current situation of intelligent speech interaction at home and abroad, take many examples to illustrate the application scenarios of speech interaction technology and finally introduce its development trend in the future.
Two patterns in conversion between HTTP2 and CoAP : Request-Reponse and Publish-Subscribe

The Internet of Things is an important part of our lives. It consists of traditional networks and constrained networks. We use HTTP protocol to connect every endpoint in traditional networks, which has been upgraded to HTTP2 in 2015. In terms of constrained networks, IETF established the Core Working Group and defined the CoAP protocol to implement communication of restricted sensors. In order to make every endpoint can really connect to each other in IoT, it is necessary to study how HTTP2 and CoAP can be converted to each other in request-response pattern. In order to make HTTP2 supported IoT feature better, this paper also implements publish-subscribe pattern in this conversion.
GAC Application to Corner Detection Based on Eccentricity

The geometric algebra GAC offers the same efficiency for calculations with arbitrary conic sections as provided by CGA for spheres. We shall exploit these properties in image processing, particularly in corner detection. Indeed, the point cluster will be approximated by a conic section and, consequently, classified as points with ellipse–like or hyperbola–like neighbourhood. Then, by means of eccentricity, we decide whether the points form a corner.
Tag-Based User Profiling

As already pointed out by a constantly growing literature, explainability in recommender systems field is a key aspect to increase users' satisfaction. With the increase of user generated content, tags have proven to be highly relevant when it comes to describe either users or items. A number of strategies that rely on tags have been proposed, yet, many of these algorithms exploit the frequency of user-tags interactions to gain information. We argue that a pure frequentist description might lack of specificity to grasp user's peculiar tastes. Therefore, we propose a novel approach based on game theory that tries to find the best trade-off between generality and detailing. The identified user's description can be used to keep her in the loop and allows the user to have control over system's knowledge. Additionally, we propose a user interface that embeds the proposed user's description and it can be used by the user herself to guide her catalogue's exploration toward novel and serendipitous items.
Hotspots Integrating of Expert and Beginner Experiences of Machine Operations through Egocentric Vision

Beginners can often provide useful information regarding machine operations, e.g., how difficult a specific operation is or how beginners deal with difficulties. However, their experiences oftentimes widely vary, and it is difficult to summarize these diverse experiences in an extensive way. To try and solve this problem, this study focused on developing a framework for integrating beginners' and experts' experiences into a unified operation model. A baseline model was first obtained based on hand-machine interactions automatically extracted from experts' egocentric vision records. Then, the beginners' behaviors were integrated by dynamically aligning them to the baseline model. Through this process, an integrated model based on the experiences of a wide range of users was achieved. We applied our method to the operation experiences of two tabletop devices, an IH heater and a sewing machine. The results show good potentials in modeling the common and different behaviors among experts and beginners.
GoOrg: Automated Organisational Chart Design for Open Multi-Agent Systems

The organisational structure is a key factor for open multi-agent systems. It is the way agents can enter into an organisation taking its position and cooperating to achieve mutual goals. In spite of its importance, there are few studies on automatic designers that generate explicit organisational structures. This paper introduces GoOrg, a proposal for automated design of organisations. Our designer uses as input a goals tree and other features such as necessary skills to achieve the goal, predicted workload and throughput. The output of GoOrg is an organisational chart. The generated structure, for instance, can be flatter or taller, accepting matrix connections of not, according to preferences and needing of more coordination levels.
Avatar-Based Management Techniques for Breaking Down Language and Cultural Barriers in Modern Russian Corporations

This chapter is about study results of use of avatar-based management techniques for breaking down language and cultural barriers in modern Russian corporations. In the chapter, specially developed avatar-based management technique mixed with RTI—respond to intervention—is used in USA for education process correction. The authors of the chapter developed the innovation tools and based the results analysis of the deliberations of citizens in social networks on topics related to online services. Intelligent visualization techniques for big data analytic was used with avatar-based management techniques introduced by Professor Mkrttchian in 2017 in his book Sliding Mode in Intellectual Control and Communication: Emerging Research and Opportunitie”, published by IGI Global and Indexed by SCOPUS.
Learning large margin support correlation filter for visual tracking

Correlation filter-based tracking methods realize dense sampling via the circular shift of an image patch, thereby improving the generalization ability of the trackers. However, the discriminative power of these methods is still limited due to the boundary effect and ridge regression framework. Instead of a traditional correlation filter, a classifier is learned in the form of a support correlation filter. Aiming at the enhancement of the discriminative power, the support correlation filter learning on the training samples is accomplished under the large margin framework. Furthermore, a cropping operation is implemented on each feature map to extract the central part of the feature as a reliable training sample. In particular, this operation is implicitly carried out through the integration into the learning framework. An alternating optimization algorithm is further developed to obtain the solution for the large margin support correlation filter. The experimental results on the benchmark dataset demonstrate the superior performance of the method.
Research on UAV Multi-Obstacle Detection Algorithm based on Stereo Vision

The UAV has the advantages of small size, low cost and flexibility, and has a wide application prospect in military, civilian and scientific research. While applying the drone to perform the mission, it is of great significance to ensure that the drone detects obstacles during the flight. Due to the high maneuverability of UAVs, detection algorithms based on single-target often miss other potential obstacles, resulting in insufficient reliability. This paper proposes multi-obstacle detection algorithm based on stereo vision. The dual-camera platform is established and the image computational hardware is applied to run the algorithm. After 3D reconstruction and stereo matching, the depth map of the scene is obtained. After that, a series of image processing algorithms are applied to find out five subtle dangerous objects and give them bounding boxes. At last, this paper establishes the hardware system and carries out several tests outsides. According to the multi-obstacle detection algorithm proposed in this paper, the results show that the algorithm can detect at most five obstacles in 15m and has practical application value.
Language Style Matching in Preschooler–Adult Dyads: Associations with Dyad Familiarity and Children’s Age

Language style matching (LSM) offers promise as an unobtrusive measure of synchrony between members of conversational dyads, but no studies have explored key questions related to LSM in developmental context. We examined LSM in young children’s ( N = 87, M age = 54.63 months) interactions with caregivers versus experimenters, and evaluated links between LSM and expressive vocabulary. LSM was significantly higher among caregiver–child than experimenter–child dyads and was positively associated with children’s expressive vocabulary.
A Forward-Backward Labeled Multi-Bernoulli Smoother

A forward-backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking which includes a forward LMB filtering part and a backward LMB smoothing part. The forward LMB filtering is the standard LMB filter, and the backward LMB smoothing recursion is closed. The derived backward smoothed density is a LMB distribution under LMB prior.
CS-BKT: introducing item relationship to the Bayesian knowledge tracing model

Bayesian knowledge tracing model (BKT) is a typical student knowledge assessment method. It is widely used in intelligent tutoring systems. In the standard BKT model, all knowledge and skills are independent of each other. However, in the process of student learning, they have a very close relation. A student may understand knowledge B better when he masters knowledge A. Therefore, this work introduces a new student model based on BKT. It takes the relationship between knowledge into account. By doing this, the new model proves higher prediction accuracy and performs better. Then this paper uses the new model to make a cognitive diagnosis according to students' test scores. The diagnostic results can help teachers provide personalized guidance to students and improve teaching efficiency.
On the Complete Weight Distribution of Subfield Subcodes of Algebraic-Geometric Codes

In this paper, we first study deviations of the complete weight distribution of a linear code from that of a random code. Then, we consider a large family of subfield subcodes of algebraic-geometric codes over prime fields which include BCH codes and Goppa codes and prove that the complete weight distribution is close to that of a random code if the code length is large compared with the genus of the curve and the degree of the divisor defining the code.
Exploitation of Open Data Repositories for the Creation of Value-Added Services

Open data repositories are information repositories that offer data that can be accessed by anyone using a typical REST type web services API. The API allows sending GET requests to obtain the desired data and return based on the requests made, data sets in various data formats. These repositories can be both public and private. In Spain you can find a large number of public open data sources. For example, the open data source of the City of Madrid offers a large amount of data of different kinds such as traffic accidents, CO2 emissions, noise pollution, etc. In this article we propose a model for the production of value-added services that did not previously exist using open data. This model is illustrated by creating a service aimed at creating tourist visits in the city of Madrid using open data about museums, temples and monuments found in the open data source of the City of Madrid.
Appreciation of Proportion in Architecture: A Comparison Between Facades Primed in Virtual Reality and on Paper

Virtual reality (VR) is a rapidly emerging tool in design and architecture. VR allows to define and evaluate design equations at full scale providing the user a sense of depth, volume and distances, as opposed to drawings, plans and CAD designs displayed on screen. Currently, VR enables to provide subjects with a preliminary experience of their own body and relative position towards the virtually designed space. That real time virtual body experience is expected to become more realistic the forthcoming decade, given the uprising of motion tracking and posture generating algorithms, and the expected breakthrough of augmented reality. These evolutions will make VR a tool that is complementary to design and simulation of body near products with 3D anthropometry and rapid prototyping such as 3D printing. Whereas the latter allows simulation of interaction with artifacts at scale of the human body or smaller, VR will enable to simulate and evaluate structures that surrounds the human body: rooms, spaces, buildings and urban implants. In particular, evaluation of scale with relation to human proportion is expected to benefit from VR. The present research is a first step in comparing classical drawing techniques with VR. This paper is confined to evaluate the aesthetic preference of subjects when exposed to facades in a virtual urban planning environment. The preference for a certain proportion is evaluated by exposing subjects to the same facades in both mediums. In this first pilot study, we isolated one variable: the length to width ratio of windows in the facade of a house with a flat roof. Subjects were primed with three length to width ratios: the square root of 2, the golden (in approximation 1.618) section and a value that was intuitively designed to be most pleasing, turned out to be 1.80. A total of n = 30 subjects were enrolled in this study. Only descriptive statistics were used due to small sample size. Results were interpreted as tendencies. There were no differences found in preferences when exposed to drawings compared to when exposed to the same models in VR. These results indicate that-restricted to our simple and controlled setting- evaluation in VR coincides with regular evaluation techniques, and thus could extend the latter, not only to relative proportion, but also in relation to subject’s body dimensions as a sense of absolute scale. The test setting presented in this pilot can be used to plan large scale-controlled studies to further investigate this hypothesis.
Faster region-based convolutional neural network method for estimating parameters from Newton's rings

Newton’s rings are the fringe patterns of quadratic phase, the curvature radius of optical components can be obtained from the coefficients of quadratic phase. Usually, the coordinate transformation method has been used to the curvature radius, however, the first step of the algorithm is to find the center of the circular fringes. In recent years, deep learning, especially the deep convolutional neural networks (CNNs), has achieved remarkable successes in object detection task. In this work, an new approach based on the Faster region-based convolutional neural network (Faster R-CNN) is proposed to estimate the rings’ center. Once the rings’ center has been detected, the squared distance from each pixel to the rings’ center is calculated, the two-dimensional pattern is transformed into a one-dimensional signal by coordinate transformation, fast Fourier transform of the spectrum reveals the periodicity of the one-dimensional fringe profile, thus enabling the calculation of the unknown surface curvature radius. The effectiveness of this method is demonstrated by the simulation and actual images.
Efficient Distributed Authentication and Access Control System Management for Internet of Things Using Blockchain

Internet of things (IoT) enables a huge network of connected devices inter-working and collaborating to provide relevant services and applications. This technology entered the market and is expected to grow in the upcoming years, as the critical questions related to the management and communication security continue to be challenging research problems. Current solutions of access control system management that enables communication between devices depend mainly on the use of digital certificates for authentication. However, such an approach imposes significant overhead on IoT devices since it is computationally demanding and requires validation of the certificate within a limited period. In addition, relying on a central node for deciding on issuing and revoking certificates introduces a single point of failure and could even risk the safety of personal information or physical damages related to IoT services. In this paper, we propose a new distributed authentication and access control system management for IoT by the use of Blockchain technology to keep track of the certificate of each IoT device (valid or revoked) in distributed and immutable records. In essence we replace certificate verification with a lightweight blockchain-based authentication approach. In addition, we propose a fully distributed IoT admission/revocation scheme. We show that our scheme could alleviate the computation overhead and enhance the response time while improving the overall system security.
Alternative inputs for games and AR/VR applications

demonstration Share on Alternative inputs for games and AR/VR applications: deep headbanging on the web Authors: Philipp Moll Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Andreas Leibetseder Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Sabrina Kletz Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Mathias Lux Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile , Bernd Muenzer Alpen-Adria-Universität Klagenfurt Alpen-Adria-Universität KlagenfurtView Profile Authors Info & Claims MMSys '19: Proceedings of the 10th ACM Multimedia Systems ConferenceJune 2019 Pages 320–323https://doi.org/10.1145/3304109.3323832Published:18 June 2019Publication History 2citation167DownloadsMetricsTotal Citations2Total Downloads167Last 12 Months20Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Skew Correction of Handwritten Chinese Character Based on ResNet

Inclined characters can affect visual perception and optical character recognition (OCR). For the tilt problem of Chinese handwritten character, a 4-direction classification and a 181-angle classification model were proposed. These two models were built based on residual neural network (ResNet). After tuned and optimized, the models have implemented an end-to-end skew correction system. In the experiment of CASIA-HWDB1.1 data set, The Top-1 accuracy of the 4-classification model is 98.4% which is 3.1% higher than AlexNet, and the average loss of Top-1 score in the 181-classification model is 0.6° which is 1.7° shorter than AlexNet. Experimental results show that the model can effectively predict the tilt degree of Chinese handwriting character, also the corrected characters can obtain higher OCR recognition accuracy.
Mercury$$^\mathrm{\textregistered }$$: A Software Based on Fuzzy Clustering for Computer-Assisted Composition

We present Mercury, a new software for computer-assisted composition based on fuzzy clustering algorithms. This software is able to generate a big number of transitions between any two different melodies, harmonic progressions or rhythmical patterns. Mercury works with symbolic music notation. The software is, therefore, able to read music and to export the generated musical production into MusicXML format. This paper focusses on some theoretical aspects of the CFT algorithm implemented in the software in order to create those complete transitions, overviewing not only the structure of the program but the user's interface and its music notation module. Finally, the wide variety of compositional possibilities of Mercury are shown by means of several computational examples.
Full thermal panorama from a long wavelength infrared and visible camera system

We present a method of constructing a full thermal panorama from a collection of images taken by a long wavelength infrared and visible camera system. Due to recent development in joint stereo calibration, our method is able to project a thermal image onto a corresponding visible image. After both images are in the same domain, panoramic projection from visible images can also be applied to thermal images. Our method also extends the scope of the projection function by allowing nonzero distance between center of rotation and optical center of rotating camera as well as arbitrary camera’s primary axes that are no longer parallel to the world axes. We show that our method works on a dataset with various camera movements and compare our results with standard software made specifically for a visible panorama and a thermal panorama. Furthermore, we are able to combine thermal panoramas of the same scene with a simple calculation. With an addition of guided upsampling, we can simultaneously improve thermal image resolution and field of view with a single run of the method.
A Study on Software Analysis and Design Education Model based on Computational Thinking

최근 컴퓨팅 사고력은 초중등 학생이 함양해야 할 기본 역량으로 인식되고 있다. 특히 제4차 산업혁명을 주도할 인재는 융합력과 창의력이 필요하며, 컴퓨팅 사고력은 창의·융합 사고능력과 협업 능력에 효과적이라고 알려져 있다. 이러한 배경으로 컴퓨팅 사고력과 함께 창의·융합 능력을 갖춘 미래 인재 양성이 대학교육에서도 강조되고 있다. 본 연구에서는 컴퓨팅 사고력의 개념과 요소를 살펴보고 시스템분석설계 운영 사례와 컴퓨팅 사고력 기반 소프트웨어 분석설계 교과에서의 교육 모델을 제시하였다. 본 연구에서 제안하는 소프트웨어 분석설계 교육 모델을 활용한다면 컴퓨팅 사고력을 배양하여 자기 주도 학습, 창의·융합 사고능력, 의사소통과 협업 능력 등을 키우는 데 효과적이며, 프로그래머로서 분석설계 능력을 지닌 소프트웨어 개발자로 성장하는 데 도움을 줄 것으로 기대한다. Computational Thinking(CT) has recently been recognized as a basic skill that all primary and secondary school students need to develop. In particular, those who will lead the 4th Industrial Revolution should have convergent thinking and creativity. CT is known to be effective in cultivating creative and convergent thinking ability and collaboration skills. In this context, the cultivation of future human resources with creativity and convergence ability along with CT is also emphasized in university education. In this study, we review the concepts and elements of CT and present an education model for CT-based software Analysis&Design course through the case study of Systems A&D class. We expect that the education model proposed in this study will help students effectively cultivate CT ability and develop skills for self-directed learning, creative and convergent thinking, and communication and collaboration. The model will allow students to grow as programmers and software developers with A&D capabilities.