Reviewerly/exHarmony huggingface dataset:
Globality-Locality Preserving Maximum Variance Extreme Learning Machine

An extreme learning machine (ELM) is a useful technique for machine learning; however, the existing extreme learning machine methods cannot exploit the geometric structure information or discriminate information of the data space well. Therefore, we propose a globality-locality preserving maximum variance extreme learning machine (GLELM) based on manifold learning. Based on the characteristics of the traditional ELM method, GLELM introduces the basic principles of linear discriminant analysis (LDA) and local preservation projection (LPP) into ELM, fully taking account of the discriminant information contained in the sample. This method can preserve the global and local manifold structures of data to optimize the projection direction of the classifier. Experiments on several widely used image databases and UCI datasets validate the performance of GLELM. The experimental results show that the proposed model achieves promising results compared to several state-of-the-art ELM algorithms.
Low Complex &amp; High Accuracy Computation Approximations to Enable On-Device RNN Applications

Recurrent Neural Networks (RNN) have demonstrated excellent results for various Automatic Speech Recognition (ASR) and Natural Language Processing (NLP) tasks. However, executing RNNs requires huge memory and computations which makes it difficult to achieve real time performance on low power devices like smartphones. Hence, currently ASR and NLP applications such as voice assistants are using cloud based solutions. In this paper, to enable on-device inference, we propose efficient approximations for weights of FC layers and activation functions to reduce the computational complexity. The proposed approximations eliminate multiplications, divisions and exponential operations by replacing them with simple arithmetic operations (shifts, additions) to significantly reduce the computation requirements without any perceivable loss of functional accuracy. The approximations also reduce the memory size and bandwidth requirements. We also present a lightweight VLIW based DSP architecture with these approximations to enable on-device inference. The approximations have been tested on the proposed DSP with various RNN applications like EESEN, LRCN and S2VT. The results with approximations show - accuracies similar to that of float (32-bit) reference, ~ 8x-12× performance gains, ~ 2x-4x gains in memory requirement and bandwidth. Moreover, the activation approximation results show better average and peak errors compared to the State of the Art.
Towards Understanding Interactions with Multi-Touch Spherical Displays

Interactive spherical displays offer unique educational and entertainment opportunities for both children and adults in public spaces. However, designing interfaces for spherical displays remains difficult because we do not yet fully understand how users naturally interact with and collaborate around spherical displays. This paper reports current progress on a project to understand how children (ages 8 to 13) and adults interact with spherical displays in a real-world setting. Our initial data gathering includes an exploratory study in which children and adults interacted with a prototype application on a spherical display in small groups in a public setting. We observed that child groups tended to interact more independently around the spherical display, whereas adult groups interacted with the sphere in a driver-navigator mode and did not tend to walk around the sphere. This work will lay the groundwork for future research into designing interactive applications for spherical displays tailored towards users of all age groups.
Synchronous Reconfiguration of Distributed Embedded Applications During Operation

Speed of adaptation to changing demand is a critical success factor in factory automation. The key to speed is to enable agile development by independent engineer offices and equipment producers with industrial-grade microservice architectures. The expensive drawback is: While software components evolve over time, manufacturers have to integrate and deploy more and more updates during costly production stops. To avoid production stops as much as possible, we propose reconfiguration extensions to a real-time container architecture proposed earlier. The original container approach addresses both the functional and nonfunctional aspects of integrating embedded software components in late engineering phases. The extended approach allows modifications of the running distributed embedded application even during operation, while continuously ensuring reactivity of the system. The agents running on each node prepare the reconfiguration in background and then synchronously perform the required modifications according to a detailed reconfiguration plan. We demonstrate our concept by describing a synchronous API change between two distributed software components of a running gesture recognition system. An evaluation shows the feasibility of the concepts, but also calls for further research.
Improved Hieroglyph Representation for Image Retrieval

In recent years, an interdisciplinary effort between archaeologists and computer vision experts has emerged to provide image retrieval tools that facilitate and support cultural heritage preservation. The performance of these tools largely depends on the hieroglyph representation quality. In the literature, the most successful hieroglyph representation for retrieval following the BoVW model includes a thinning hieroglyph process and selects interest points through uniform random sampling. However, thinned hieroglyphs could have noise or redundant information, and a random set of interest points could include non-useful interest points that are different in each iteration. In this article, we propose improving this hieroglyph representation by pruning thinned hieroglyphs and introducing an improved interest-point selection. Our experiments show that our proposal significantly improves the hieroglyph retrieval results of state-of-the-art methods.
Call Me by My Name

Voice Conversational Agents (VCAs) are increasingly becoming part of our daily life. Calling them by their names can solve more than just a problem of efficiency in interaction between users and VCAs, but also can cast them as actors capable of playing a role in a relationship. However, due to the immature state of the technology and its related services, such relationships between users and VCAs can be limited in practice. To broaden the scope in designing different relationships, this study explores VCAs depicted in sci-fi movies. Sci-fi is not purely based on fantasy, but also is a social reflection on technology, which in turn can inspire design researchers to understand and speculate the complexity of VCAs. Through community sourcing with sci-fi enthusiasts, 43 Sci-fi VCAs were deduced. A movie event was also organized to discuss the role of VCAs. Finally, this paper presents several possible design insights for designing the role of VCAs.
A cybersecurity strategy for Industry 4.0

There is a strong disconnect between cybersecurity and small to medium sized organizations that are members of Industry 4.0. Industry 4.0 (I4) is considered the 4th industrial revolution where automation and data exchange co-exist in manufacturing technologies. This next phase of industry includes cyber-physical systems, the Internet of things (IoT), cloud computing and cognitive computing. This report will survey three specific areas in relation to Industry 4.0 and its impact on the security of the supply chain: Automotive Cybersecurity, Industrial Internet of Things (IIoT) and Data Protection in the Manufacturing Industry.
Inferring Relationship Semantics in Social Networks with Dual-View Features Semi-Supervised Learning

Relationship semantics refer to the types of social relationships between users in a social network, e.g., friend, family, enemy, etc. Inferring the semantics of social relationships using digital social footprints plays an important role in understanding the social network and utilizing them for further application. In this paper, we propose a semi-supervised machine-learning model based on a dual-view features co-training framework by employing both interaction behaviors between social dyads and structure features of the social network. Specifically, the intensity of social interaction and geographical co-occurrence are used to characterize interaction behaviors between social dyads, while network representation learning is used to extracting structure features of the dyads in their ego-networks. We evaluated our approach on a real mobile terminal usage dataset. Results show that our method can significantly improve the performance of social relationship semantics inference in the case of limited labeled data compared to the state-of-art methods.
Psychological Readiness to Entrepreneurship of Economics Students

Political and economic changes in Russian society, the development of market relations resulted in the expansion of scientific research in the field of human behavior in the search for optimal ways to change public consciousness. The development of small and medium-sized businesses has led to the emergence of a new community of people - entrepreneurs who can greatly develop the economic progress of Russian society. Entrepreneurship as a form of economic activity is currently an important form of self-employment of a part of the population, a powerful factor in the creation of new jobs. Entrepreneurship is an economically efficient business entity that specifies its attractiveness for young people under modern conditions of Russian society development and gives rise to mass enthusiasm to this type of business entity. However, according to modern entrepreneurship research of this country, the fact that to establish one’s own business and effective entrepreneurship it is required to show certain psychological traits of character, is frequently not taken into account. So the research into psychological readiness to entrepreneurship has proved to be extremely acute and socially important. The article is dedicated to empirical study of psychological readiness to entrepreneurship of Economics students. In this work there has been used a questionnaire including a number of questions of standardized program of social and psychological research into economic consciousness of personality (A.P. Zhuravlev, N.A. Zhuravleva, V.P. Pozdnyakov, Psychology Institute of the Russian Academy of Sciences (RAS PI)), the Holland code career test; methods to diagnose social and psychological attitude of the personality by O.F. Potyomkina. The research resulted in defining types of students showing different combinations of psychological traits of character and orientation to establish their own business. There have been offered recommendations to form psychological readiness to establish their own business in students under higher school conditions. It is possible to practically realize the obtained results with state programs on support of youth entrepreneurship, small and medium-sized business, effective implementation of psychological counseling of these programs, the work of students’ centers on employment promotion and business incubation of higher schools.
Text Summarization Using Adaptive Neuro-Fuzzy Inference System

Nowadays, data present on the World Wide Web is growing exponentially. People use search engines like Google, Bing, and Yahoo for retrieving the required information. But as the information present on the Web is huge, it is necessary for user to make the summary of this information. User can easily understand the large volume of data with the help of summary and does not require spending so many for analyzing the collected information. Text summarization is the process of transforming the large text into short and meaningful text. While summarizing the text, one should preserve its data matter and general message. It is laborious for individual person to summarize the large documents as it takes much time. It is a very difficult and time-consuming process for humans to summarize large documents. Different methods are used for summarizing the text till now like neural network, fuzzy logic, genetic algorithm, and many more. The proposed system is a hybrid system of neural network and fuzzy logic, which is known as adaptive neuro-fuzzy inference system. So it will overcome the drawbacks of both neural network and fuzzy logic. This proposed system takes the learning ability of neural network and uncertainty data handling of fuzzy system.
Method of detecting hidden data transmission via the Koch-Zhao steganographic algorithm

The article proposes an algorithm for analyzing images with embedded message based on the Koha-Zhao steganographic method. The basic idea is to compare the pairs of coefficients of the discrete cosine transform. For this purpose the dependence of the coefficient difference from the block number is constructed. Numerical differentiation of this dependence allows us to identify the boundaries of the embedded message. After that the analysis of the initial dependence on the allocated interval allows to define the used parameters of Koch-Zhao method. The computer experiment was conducted. It is shown that the proposed algorithm allows to determine exactly the location, size and contents of a hidden message if it is embedded in a continuous sequence of blocks.
Smooth switching method for asynchronous multiple viewpoint videos using frame interpolation

This research proposes a method that generates viewpoint smooth switching by reducing the flickering artefacts that are observed at bullet-times generated from asynchronous multi-view videos using frame interpolation processing. When we asynchronously capture multi-view videos of an object moving at a high velocity, deviations occur in the observed position at the bullet-times. We apply a frame interpolation technique to smooth this problem. By selecting suitable interpolated images that produce the smallest movement of the subject's observed position, we smoothly generate a viewpoint-switched bullet-time video. In this paper, we examine the subjective evaluation of the video generated by the proposed method. And we also examine objective evaluation. Therefore, the effectiveness of the proposed method is shown. Furthermore, reproducibility was improved by considering the application conditions of the proposed method.
Mobile Robot with Previous Announcement of Upcoming Operation Using Face Interface

Encounters with robots in public spaces can be uncomfortable for people who have limited or no experience in coexisting with robots, due to the lack of intuitiveness of robots' behaviors and their mechanical appearances. This paper presents a new method that a mobile robot announces its upcoming operation previously to surrounding pedestrians by rotating a robot face model made with 3D computer graphics to the direction which the robot is traveling to. The rotational direction of the robot face and the traveling direction of the robot are determined based on the Dynamic Window Approach. In the proposed method, passing-by behaviors against pedestrians and obstacles are conducted differently under the consideration of personal space, positions and velocities of pedestrians. Two kinds of experiments were conducted in which the robot traveled with and without the previous announcement. Results of two experiments indicated that the proposed method was effective to announce the robots' upcoming operation.
A Spatial-Temporal Weighted Method for Asymmetrically Distorted Stereo Video Quality Assessment

We propose a 2D-TO-3D video quality prediction model for assessing the perceptual quality of asymmetrically compressed stereoscopic 3D video. In the case that the distortions between left- and right-views are significantly different, directly averaging the qualities of single-view videos to estimate 3D video perceptual quality may lead a strong prediction bias. In order to eliminate or reduce the prediction bias, we design a two-stage approach for 3D video quality prediction. Firstly, we evaluate the perceptual quality of single-view videos with the state-of-the-art 2D image/video quality assessment approaches. Secondly, we design a binocular rivalry inspired model considering both spatial and temporal perceptual information to integrate 2D video perceptual quality of both views into the assessment of 3D video quality. We validate the highly competitive performance of the proposed approach on the Waterloo-IVC 3D video quality database.
PaxVis

Peace is a universal concern involving a complex process of negotiations between select groups (i.e. policy makers, mediators, scholars and civil society groups). In this paper we present PaxVis, a platform of two interactive data visualizations for a large database of peace agreements (PA-X). We developed PaxVis to support comparative analysis of peace processes and to improve understandings of the complex dynamics behind the establishment of peace.
Airlight Estimation Based on Distant Region Segmentation

Natural images suffer from bad weather conditions, such as haze or fog, which decreases the contrast and degrades the color of observed images. Haze removal aims to recover haze-free images by the image degradation model. The global atmospheric light (airlight) estimation is an essential step for haze removal. With an assumption that the airlight exists in the infinite distance, we propose a novel learning-based framework for airlight estimation. Our framework is mainly composed of two steps: i) the airlight is initially determined by distant region segmentation based on U-Net; ii) the final airlight can be obtained by the weighted sum of the pixel values inside the distant region. Owing to lack of ground-truth airlight, we present a method to synthesize outdoor training examples. The proposed framework not only perform well on synthetic images but also has a good generalization ability for natural images. Experimental results demonstrate that our proposed approach can achieve more accurate estimate of airlight than state-of-the-art methods on both synthetic and natural images.
ARRoW

[<u>Context/Motivation</u>] Decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) and the costs-benefits analysis of the alternative solutions. Usually, it is required the specification of the weights (a.k.a. preferences) associated with the NFRs and decision-making strategies. These preferences are traditionally defined at design-time. [<u>Questions/Problems</u>] A big challenge is the need to deal with unsuitable preferences, based on empirical evidence available at runtime, and which may not agree anymore with previous assumptions. Therefore, new techniques are needed to systematically reassess the current preferences according to empirical evidence collected at runtime. [<u>Principal ideas/ results</u>] We present ARRoW (<u>A</u>utomatic <u>R</u>untime <u>R</u>eappraisal <u>o</u>f <u>W</u>eights) to support the dynamic update of preferences/weights associated with the NFRs and decision-making strategies in SAS, while taking into account the current levels of satisficement that NFRs can reach during the system's operation. [<u>Contribution</u>] To developed ARRoW, we have extended the Primitive Cognitive Network Process (P-CNP), a version of the Analytical Hierarchy Process (AHP), to enable the handling and update of weights during runtime. Specifically, in this paper, we show a formalization for the specification of the decision-making of a SAS in terms of NFRs, the design decisions and their corresponding weights as a P-CNP problem. We also report on how the P-CNP has been extended to be used at runtime. We show how the propagation of elements of P-CNP matrices is performed in such a way that the weights are updated to therefore, improve the levels of satisficement of the NFRs to better match the current environment during runtime. ARRoW leverages the Bayesian learning process underneath, which on the other hand, provides the mechanism to get access to evidence about the levels of satisficement of the NFRs. The experiments have been applied to a case study of the networking application domain where the decision-making has been improved.
MatHealthXB

The proliferation of digital technologies has facilitated the adoption of innovative approaches to addressing global maternal health challenges. Worldwide, HCI researchers - from both resource-constrained and resource-rich countries are actively engaged in developing novel responses to an ever-evolving maternal health landscape. However, opportunities for these researchers to interact and engage in sustained dialogue and collaboration are limited. The purpose of this Special Interest Group (SIG) is to bring these professionals together to support an active global network of maternal health researchers and facilitate collaboration across borders.
Multiple stable states and Dicke phase transition for two atoms in an optical cavity

We study in the present paper the multiple stable states of two different atoms in a single-mode quantum cavity by means of variational method. Eigenenergies and eigenstates are obtained analytically in the optical coherent state of cavity field. The ground state displays the known Dicke phase transition from the normal to superradiant phases at the critical value of atom–field coupling. The rich phase diagrams including the stable excited states are presented respectively for two atoms with different level spaces and atom–field couplings. By adjusting the ratios of two-atom couplings or level spaces, one atom can be in the normally populated state, while the other is in the inversely populated state. We also observe the stimulated radiation from the inversely populated state. Particularly, the normal and stimulated radiations are interchangeable respectively for two atoms. Moreover, the maximally entangled state can be generated with two identical atoms.
PALS

Amyotrophic Lateral Sclerosis (ALS) is a serious and poorly understood disease, impacting 50,000 people a year globally. Our research found that people with ALS express a lack of connection with other people with the disease, and that the general public lacks awareness about ALS. We also identified an engagement problem with the currently available resources to connect and support people with ALS. To address these issues, we introduce 'PALS' - an accessible crowdsourcing and connection quilt, first hung like a tapestry in the ALS clinic, then later used as an interactive public display. The quilt offers the opportunity to access crowdsourced information concerning individual experiences of ALS. Our work offers three primary contributions: 1) adding to limited HCI research concerning the ALS community by establishing the needs, 2) applying the 'PALS' quilt design solution to these needs, and 3) combining three modalities: crowdsourcing, tangible tapestry displays, and interactive waiting education in a unique way.