Reviewerly/exHarmony huggingface dataset:
Impact of Source Panning on a Global Metronome in Rhythmic Networked Music Performance

Achieving synchronicity and tempo stability during a Network Music Performance (NMP) is not straightforward under heavy network latency conditions. Previous work on Global Metronomes has shown that it is possible to provide a universal time-reference signal to the connected nodes of an NMP. This paper illustrates the effects of using a global metronome and signal source-panning on a rhythmic performance. An experiment was conducted to evaluate the objective musical outcome and the subjective impressions of using these elements, applied to several pairs of Djembe percussion duets, under different tempo and latency conditions. The objective analysis in terms of tempo stability and synchrony, conducted from the perspective of an hypothetical audience, suggest that the use of a metronome achieves stability improvements at medium and higher latency levels, while the use of signal panning has been effective in improving the metronome efficiency. Subjective analysis data shows that the use of the metronome becomes challenging for higher delays while it was perceptually non-intrusive for the musicians for lower and medium delays.
Research on Timeliness Evaluation Model of Online Teaching Based on Intelligent Learning

To improve the effect of online teaching, evaluate the timeliness of online teaching, and provide a more effective analysis method for the current teaching system, we proposed an online teaching timeliness evaluation model based on intelligent learning. Empirical analysis is carried out on statistical data, and a timeliness evaluation model for online teaching based on intelligent learning is proposed. In this model, a statistical information analysis model of online teaching timeliness evaluation is constructed. Then, mining method of association rules is used for segmentation fusion and autocorrelation matching detection of teaching timeliness. Finally, using analysis method for statistical characteristic to conduct statistical analysis and robustness test to the timeliness of online teaching. The simulation results show that this method has a high level of confidence in the timeliness evaluation of online teaching, which improves the quantitative analysis ability of online teaching timeliness.
Ascertaining the Play Outcome using the Single Conspiracy Number in GoBang

Analyzing the effect of value change in the MIN/-MAX search algorithm towards gameplay can be challenging. The single conspiracy number (SCN) is an indicator that can reflect such information. In this paper, SCN is utilized to identify different factors and analyze patterns that influenced a 5-in-a-row game called GoBang. SCN values from various GoBang games were calculated and analyzed. A specific GoBang dataset called the "chess manuals" is used as the simulation test-bed to reflect real game situations. Results show that such a method is proven to indicate different game situations, leading to the ability to ascertain the outcome of the play, given the right threshold, relative to the MIN/MAX evaluation value.
Hierarchical Motion Planning Framework for Manipulators in Human-Centered Dynamic Environments

Collaborating robots face rising challenges with respect to autonomy and safety as they are deployed in flexible automation applications. The ability to perform the required tasks in the presence of humans and obstacles is key for the integration of these machines in industry. In this work we introduce a framework for motion planning of manipulators that builds upon the most promising existing approaches by combining them in an advantageous way. It includes a new Obstacle-related Sampling Rejection Probabilistic Roadmap planner (ORSR-PRM) that represents the free workspace in an efficient way. Using this representation, dynamic obstacles can be avoided in real-time using an attractor-based online trajectory generation. The resulting motions satisfy kinematic and dynamic joint limits, ensuring a safe human-robot interaction. We validate the functionality and performance of the presented framework in simulations and experiments.
Structuring Human-Robot Interactions via Interaction Conventions

Interaction conventions (e.g., using pinch gestures to zoom in and out) are designed to structure how users effectively work with an interactive technology. We contend in this paper that successful human-robot interactions may be achieved through an appropriate use of interaction conventions. We present a simple, natural interaction convention-"Put That Here"-for instructing a robot partner to perform pick-and-place tasks. This convention allows people to use common gestures and verbal commands to select objects of interest and to specify their intended location of placement. We implement an autonomous robot system capable of parsing and operating through this convention. Through a user study, we show that participants were easily able to adopt and use the convention to provide task specifications. Our results show that participants using this convention were able to complete tasks faster and experienced significantly lower cognitive load than when using only verbal commands to give instructions. Furthermore, when asked to give natural pick-and-place instructions to a human collaborator, the participants intuitively used task specification methods that paralleled our convention, incorporating both gestures and verbal commands to provide precise task-relevant information. We discuss the potential of interaction conventions in enabling productive human-robot interactions.
An Improved Vision Method for Robust Monitoring of Multi-Point Dynamic Displacements with Smartphones in an Interference Environment

Current research on dynamic displacement measurement based on computer vision mostly requires professional high-speed cameras and an ideal shooting environment to ensure the performance and accuracy of the analysis. However, the high cost of the camera and strict requirements of sharp image contrast and stable environment during the shooting process limit the broad application of the technology. This paper proposes an improved vision method to implement multi-point dynamic displacement measurements with smartphones in an interference environment. A motion-enhanced spatio-temporal context (MSTC) algorithm is developed and applied together with the optical flow (OF) algorithm to realize a simultaneous tracking and dynamic displacement extraction of multiple points on a vibrating structure in the interference environment. Finally, a sine-sweep vibration experiment on a cantilever sphere model is presented to validate the feasibility of the proposed method in a wide-band frequency range. In the test, a smartphone was used to shoot the vibration process of the sine-sweep-excited sphere, and illumination change, fog interference, and camera jitter were artificially simulated to represent the interference environment. The results of the proposed method are compared to conventional displacement sensor data and current vision method results. It is demonstrated that, in an interference environment, (1) the OF method is prone to mismatch the feature points and leads to data deviated or lost; (2) the conventional STC method is sensitive to target selection and can effectively track those targets having a large proportion of pixels in the context with motion tendency similar to the target center; (3) the proposed MSTC method, however, can ease the sensitivity to target selection through in-depth processing of the information in the context and finally enhance the robustness of the target tracking. In addition, the MSTC method takes less than one second to track each target between adjacent frame images, implying a potential for online measurement.
Event-Driven Network for Cross-Modal Retrieval

Despite extensive research on cross-modal retrieval, existing methods focus on the matching between image objects and text words. However, for the large amount of social media, such as news reports and online posts with images, previous methods are insufficient to model the associations between long text and image. As long text contains multiple entities and relationships between them, as well as complex events sharing a common scenario of the text, it poses unique research challenge to cross-modal retrieval. To tackle the challenge, in this paper, we focus on the retrieval task on long text and image, and propose an event-driven network for cross-modal retrieval. Our approach consists of two modules, namely the contextual neural tensor network (CNTN) and cross-modal matching network (CMMN). The CNTN module captures both event-level and text-level semantics of the sequential events extracted from a long text. The CMMN module learns a common representation space to compute the similarity of image and text modalities. We construct a multimodal dataset based on the news reports in People's Daily. The experimental results demonstrate that our model outperforms the existing state-of-the-art methods and can provide semantic richer text representations to enhance the effectiveness in cross-modal retrieval.
Evolving Initial Heuristic Functions for Agent-Centered Heuristic Search

Heuristic functions guide search algorithms and have a profound impact on their performance. In the context of agent-centered real-time heuristic search (RTHS), a heuristic represents the agent's initial domain knowledge which the agent then updates as it explores the search graph. An ideal initial heuristic should capture some specific domain knowledge to guide the agent effectively yet be general enough for a broad class of problems. It should also be computationally efficient, compact in its representation and human-interpretable. Traditionally initial heuristics in RTHS have been designed by humans (e.g., Manhattan distance). In this paper we explore the alternative of building initial heuristics by machines. To keep them portable and human-interpretable we represent each heuristic as a closed-form algebraic formula. Yet to make the heuristics capture problem specifics and thus be more effective in guiding the search, we automatically build a heuristic tailored to a class of problems. To achieve both objectives, we propose and evaluate automatically searching the space of heuristic functions. As a preliminary demonstration, we find closed-form heuristics that outperform Manhattan distance in grid-based pathfinding. We then develop an insight on how such formula-based heuristics are able to exploit characteristics of certain pathfinding maps.
Delay Mitigation for Backchannel Prediction in Spoken Dialog System

To provide natural dialogues between spoken dialog systems and users, backchannel feedback can be used to make the interaction more sophisticated. Many related studies have combined acoustic and lexical features into a model to achieve better prediction. However, extracting lexical features leads to a delay caused by the automatic speech recognition (ASR) process. The systems should respond with no delay, since delays reduce the naturalness of the conversation and make the user feel dissatisfied. In this work, we present a prior prediction model for reducing response delay in backchannel prediction. We first train both acoustic- and lexical-based backchannel prediction models independently. In the lexical-based model, prior prediction is necessary to consider the ASR delay. The prior prediction model is trained with a weighting value that gradually increases when a sequence is closer to a suitable response timing. The backchannel probability is calculated based on the outputs from both acoustic- and lexical-based models. Evaluation results show that the prior prediction model can predict backchannel with an improvement rate on the F1 score 8% better than the current state-of-the-art algorithm under a 2.0-s delay condition.
Telepresence Robot Blended With a Real Landscape and Its Impact on User Experiences

Telepresence robots using traditional video chats do not yet fully represent the presence of a remote user. To enhance that presence, we propose a novel approach that replaces the background of the remote user with a real-time captured image of the area behind the robot. We assumed that the background of the remote user, which was visible on the telepresence robot's display, was one reason for the reduction in presence because it might remind local viewers that the remote user is in another place. We found that our approach effectively enhanced the presence of remote user and reduced the negative impression of a delay. In this paper, we describe the new approach, its implementation, and an evaluation of the system. We evaluated the subjective impressions of communication through the proposed system. This approach also protects the privacy of the remote user and makes it more acceptable to people using telepresence robots from private places, e.g., a sickroom in a hospital.
Topological Convolutional Neural Networks for Transient Stability Assessment on Massive Historical Online Power Grid Data

The power system is a highly nonlinear complex dynamic system with chaos. Based on the historical online data generated by dynamic stability analysis (DSA) system, transient stability assessment (TSA) approach enables rapid screening of transient stability fault sets, and thus improving the operation stability of the power grid. We propose a novel deep learning framework, called Topological Convolutional Networks (TCNN), to specifically model the topological structures of power grids and efficiently extract features. Based on the adjacent matrix, topological convolution updates the node’s representation using the representation of its neighbors, and topological pooling operation summarizes the topological local features. The results show that the proposed model outperforms other strong baselines by a large margin on both measurements of compressing the candidate fault set and reducing the misclassification of unstable samples.
Visualization in Medical System Interfaces: UX Guidelines

Visualization is an important element in user experience (UX). Thus, it should be considered in designing an interface of a system. Visualization can help users to accomplish their duties in an intuitive manner. This is essential especially in the medical field. To the best of our knowledge none of existing UX guidelines, for designing an interface have included visualization factor. The aim of this study is to propose a set of UX guidelines that include visualization elements for designing an interface of a medical system. This paper presents a survey on visualization elements that contribute to UX and proposes a nine UX guidelines for building interfaces of medical systems. The proposed guidelines consist of visualizations elements including graphs, symbols, icons, shapes, pictographic, infographic, pattern, layout, colors, and terminologies used.
A Descriptive Analysis of US Initial Coin Offerings

Initial Coin Offerings (ICOs) are blockchain-based crowdfunding ventures to facilitate access to capital. Blockchain technology promises to solve many present-day problems, however, in the past three years, a diminished crypto-currency activity and fraudulent undertakings have raised some concerns. Using a novel, hand-collected dataset, we study a range of characteristics of US-based ICO tokens, starting with the first token launched in 2015 till December 2019. The price of ICO tokens was at its highest in 2017, followed by a sharp decline in 2018 and a slow recovery since. Presence of Know-Your-Customer (KYC) and White-list due diligence procedures can be associated with an ICO receiving a higher rating from experts and a higher level of social media followers. On the other hand, an ICO’s utilization of an incentive program, such as offering bonuses and bounties, is associated with higher rating by experts, but a lower level of social media following. Following the 2018 cryptocurrency slump, teams have increased the percentage of tokens kept in lock-up to prevent large market supplies that result in a slump as before and to signal their long-term commitment to the project. ICOs’ ratings and prices show contradictory characteristics, where blockchain infrastructure, healthcare, and marketplace domain tokens attracted lowest prices while being rated the highest, whereas the converse was true for energy tokens.
DTV radio spectrum anomaly detection based on an improved GAN

Detecting anomalous behavior in radio spectrum is an urgent task due to the popularity of wireless applications. Radio spectrum anomalies include both the anomalies in authorized and unauthorized frequency band. In this paper, we focus on unsupervised radio spectrum Anomaly detection of Digital Television (DTV) frequency band (474MHz). Firstly, an improved GAN model was proposed and its efficacy was demonstrated on a MNIST dataset. Then, the model is trained by collecting the spectrum data sets at DTV band. The performance of the model was tested by simulating different interference signals. Results show that when the width and power of the interference pulse is 1.5MHz and 5dB greater than the signal, the detection accuracy achieves 97.86%. Besides, for a real FM interference signal with power of -15dBm, the detection rate is nearly 90%.
Inferring Non-Stationary Human Preferences for Human-Agent Teams

One main challenge to robot decision making in human-robot teams involves predicting the intents of a human team member through observations of the human's behavior. Inverse Reinforcement Learning (IRL) is one approach to predicting human intent, however, such approaches typically assume that the human's intent is stationary. Furthermore, there are few approaches that identify when the human's intent changes during observations. Modeling human decision making as a Markov decision process, we address these two limitations by maintaining a belief over the reward parameters of the model (representing the human's preference for tasks or goals), and updating the parameters using IRL estimates from short windows of observations. We posit that a human's preferences can change with time, due to gradual drift of preference and/or discrete, step-wise changes of intent. Our approach maintains an estimate of the human's preferences under such conditions, and is able to identify changes of intent based on the divergence between subsequent belief updates. We demonstrate that our approach can effectively track dynamic reward parameters and identify changes of intent in a simulated environment, and that this approach can be leveraged by a robot team member to improve team performance.
Distributed Ledger Technology for Securing IoT

Computing and communication are getting increasingly ubiquitous with the inclusion of sophisticated devices like electric vehicles, smart phones and other house hold appliances. Due to the constant evolution in Internet of Things (IoT), the process of collaboration of these devices at a mass scale in order to provide improved and better services to the society. Traditional mechanisms which are used to sustain privacy and security become incapable from achieving the same for IoT systems having distributed or decentralized topology. Distributed Ledger Technologies (DLT), an emerging digital technology, consists of different kinds of decentralized data structures to ensure immutability by linking blocks using cryptographic measures. DLT has the ability to ensure privacy, security and distributed or decentralized computations with adhering to the constraints of IoT nodes. This study is motivated due to the lack of an in-depth analysis on how the characteristics of DLT can be exploited to secure IoT systems. So, an in depth overview of DLT along with some of the existing solutions to meet security requirements of IoT systems employing DLT have been provided in this paper. With respect to integrating DLT with IoT, this article also highlights the different challenges.
Review of the Application of Deep Learning in Image Memorability Prediction

With the rapid development of artificial intelligence and image processing technology, image memorability prediction has been studied more and more widely. Image memorability is applied to many fields such as education, advertising and medicine, which has important research value. Through an inductive analysis of related research, the concept of image memorability is introduced and the factors affecting image memorability are summarized. According to the different structures, the image memorability prediction methods are divided into 5 categories, and the performance of each method is introduced and compared. Finally, the method of increasing image memorability is briefly introduced, and the future research trend of image memorability is prospected.
The Maximum Entropy on the Mean Method for Image Deblurring

Image deblurring is a notoriously challenging ill-posed inverse problem. In recent years, a wide variety of approaches have been proposed based upon regularization at the level of the image or on techniques from machine learning. We propose an alternative approach, shifting the paradigm towards regularization at the level of the probability distribution on the space of images. Our method is based upon the idea of maximum entropy on the mean wherein we work at the level of the probability density function of the image whose expectation is our estimate of the ground truth. Using techniques from convex analysis and probability theory, we show that the method is computationally feasible and amenable to very large blurs. Moreover, when images are imbedded with symbology (a known pattern), we show how our method can be applied to approximate the unknown blur kernel with remarkable effects. While our method is stable with respect to small amounts of noise, it does not actively denoise. However, for moderate to large amounts of noise, it performs well by preconditioned denoising with a state of the art method.
Tissue Differentiation Based on Classification of Morphometric Features of Nuclei

The aim of the article is to analyze the shape of nuclei of various tissues and to assess the tumor differentiation based on morphometric measurements. For this purpose, an experiment was conducted, the results of which determine whether it is possible to determine a tissue’s type based on the mentioned features. The measurements were performed on a publicly available data set containing 1,356 hematoxylin- and eosin-stained images with nucleus segmentations for 14 different human tissues. Morphometric analysis of cell nuclei using ImageJ software took 17 parameters into account. Classification of the obtained results was performed in Matlab R2018b software using the SVM and t-SNE algorithms, which showed that some cancers can be distinguished with an accuracy close to 90% (lung squamous cell cancer vs others; breast cancer vs cervical cancer).