Reviewerly/exHarmony huggingface dataset:
Quantum dynamics and relaxation in comb turbulent diffusion

Continuous time quantum walks in the form of quantum counterparts of turbulent diffusion in comb geometry are considered. The interplay between the backbone inhomogeneous advection $\delta(y)x\partial_x$ along the $x$ axis, which takes place only at the $y=0$, and normal diffusion inside fingers $\partial_y^2$ along the $y$ axis leads to turbulent diffusion. This geometrical constraint of transport coefficients due to comb geometry and properties of a dilatation operator lead to consideration of two possible scenarios of quantum mechanics. These two variants of continuous time quantum walks are described by non-Hermitian operators of the form $\hat{\cal H}=\hat{A}+i\hat{B}$. Operator $\hat{A}$ is responsible for the unitary transformation, while operator $i\hat{B}$ is responsible for quantum/classical relaxation. At the first quantum scenario, the initial wave packet can move against the classical streaming. This quantum swimming upstream is due to the dilatation operator, which is responsible for the quantum (not unitary) dynamics along the backbone, while the classical relaxation takes place in fingers. In the second scenario, the dilatation operator is responsible for the quantum relaxation in the form of an imaginary optical potential, while the quantum unitary dynamics takes place in fingers. Rigorous analytical analysis is performed for both wave and Green's functions.
LRCN-RetailNet: A recurrent neural network architecture for accurate people counting

Measuring and analyzing the flow of customers in retail stores is essential for a retailer to better comprehend customers’ behavior and support decision-making. Nevertheless, not much attention has been given to the development of novel technologies for automatic people counting. We introduce LRCN-RetailNet: a recurrent neural network architecture capable of learning a non-linear regression model and accurately predicting the people count from videos captured by low-cost surveillance cameras. The input video format follows the recently proposed RGBP image format, which is comprised of color and people (foreground) information. Our architecture is capable of considering two relevant aspects: spatial features extracted through convolutional layers from the RGBP images; and the temporal coherence of the problem, which is exploited by recurrent layers. We show that, through a supervised learning approach, the trained models are capable of predicting the people count with high accuracy. Additionally, we present and demonstrate that a straightforward modification of the methodology is effective to exclude salespeople from the people count. Comprehensive experiments were conducted to validate, evaluate and compare the proposed architecture. Results corroborated that LRCN-RetailNet remarkably outperforms both the previous RetailNet architecture, which was limited to evaluating a single image per iteration; and two state-of-the-art neural networks for object detection. Finally, computational performance experiments confirmed that the entire methodology is effective to estimate people count in real-time.
Malicious Bot Detection in Online Social Networks: Arming Handcrafted Features with Deep Learning

Online social networks (OSNs) have long been suffering from various types of malicious bots (e.g., spammers, fake followers, social bots, and content polluters). Recent studies show that they have also been actively involved in delivering hate speeches and disseminating misinformation. Over several years, researchers have proposed multiple approaches to identify some types of them to lower their impact on the OSNs. However, their strategies mostly focused on handcrafted features to capture characteristics of malicious users, or their deep learning approaches may only work under certain situations (e.g., under the dense retweets/sharing behavior). To overcome the limitation of the prior work, in this paper, we propose a novel framework that incorporates handcrafted features and automatically learned features by deep learning methods from various perspectives. It automatically makes the balance between them to make the final prediction toward detecting malicious bots. In particular, we (i) combine publicly available 15 Twitter user datasets and categorize these accounts into two groups (i.e., legitimate accounts and malicious bot accounts); and (ii) propose a deep learning framework that jointly learns various features and detects malicious accounts. Our experimental results show that our proposed model outperforms 7 state-of-the-art methods, achieving 0.901 accuracy. Our ablation study shows that all types of our features positively contribute to enhancing the model performance.
Non-Extra Pixel Interpolation

A non-extra pixel interpolation NPI is introduced for efficient image upscaling purposes. The NPI algorithm uses extended-triangular and linear scaling functions to match the pixel coordinates. The triangular function uses a modulo-operator with only two variables representing image pixels and scaling ratio. Every two variables of the linear scaling function represent the source/destination image pixels and scaling ratio. The traditional ceil function is used to round off non-integer pixel coordinates. The circshift and padarray functions are used to circularly shift the elements in array output by [Formula: see text]-amount in each dimension and pad elements of the [Formula: see text]th columns/rows by g-padsize in the shifted array, respectively. The [Formula: see text], [Formula: see text] and [Formula: see text] values are determined with respect to integer scaling ratios by a vector of [Formula: see text]-elements. The Exactness, Peak Signal-to-Noise Ratio, Signal-to-Noise Ratio and Discrete Fourier Transform techniques were used for objective evaluation purposes. Experiments demonstrated comparable results as well as the need for further researches.
CNN Based Aerial Image processing model for Women Security and Smart Surveillance

Usage of drones has become very popular but its fullest potential can only be realized when collaborated with other customized sensors, subsystem performing functionalities like image processing, navigation control, robotic actuation. This project helps in utilizing drone technology in an effective way to solve different issues of society by proposing a CNN based image processing model which is effective for aerial surveillance with various smart autonomous modes. The paper also discusses the requirement for employing such faster, simpler, and effective working methods in aerial surveillance by streamlining, and enhancing its operations by overcoming the various challenges of aerial imaging utilizing pose estimation. Apart from detecting pedestrian activities by using pose estimation, it is even trained to classify gender, detect weapons, fire accidents, and sparkling of roadside electric wires. It can be installed on a drone by following technical details and working states provided in later sections of this paper using master-slave protocol or it can also function independently as a Smart CCTV camera when not setup on the drone. The prototype is implemented using Raspberry Pi, Arduino connected with PI-camera, GPS Neo 6m, HC-SR04 ultrasonic sensor and the model uses the Tensor flow and OpenCV. Later the model is tested for being controlled by specially designed LABVIEW GUI and then Python GUI is programmed for controlling modes and display purposes. The model discussed in this paper also intended to deliver enhanced general protection but seriously encompasses other dimensions of crime like VAW-Violence against women and SGBV- Sexual and Gender-based Violence.
Bidirectional quantum teleportation and cyclic quantum teleportation of multi-qubit entangled states via G-state

We propose a novel scheme for faithful bidirectional quantum teleportation (BQT) in which Alice can transmit an unknown N-qubit entangled state to Bob and at the same time Bob can transmit an unknown M-qubit entangled state to Alice by using a four-qubit entangled G-state as a quantum channel. We also propose a new scheme for cyclic QT of multi-qubit entangled states by using two G-states as a quantum channel. The advantage of our schemes is that it seems to be much simpler and requires reduced number of qubits in quantum channel as compared with the other proposed schemes.
A Visual Analytics Approach for Ecosystem Dynamics based on Empirical Dynamic Modeling

An important approach for scientific inquiry across many disciplines involves using observational time series data to understand the relationships between key variables to gain mechanistic insights into the underlying rules that govern the given system. In real systems, such as those found in ecology, the relationships between time series variables are generally not static; instead, these relationships are dynamical and change in a nonlinear or state-dependent manner. To further understand such systems, we investigate integrating methods that appropriately characterize these dynamics (i.e., methods that measure interactions as they change with time-varying system states) with visualization techniques that can help analyze the behavior of the system. Here, we focus on empirical dynamic modeling (EDM) as a state-of-the-art method that specifically identifies causal variables and measures changing state-dependent relationships between time series variables. Instead of using approaches centered on parametric equations, EDM is an equation-free approach that studies systems based on their dynamic attractors. We propose a visual analytics system to support the identification and mechanistic interpretation of system states using an EDM-constructed dynamic graph. This work, as detailed in four analysis tasks and demonstrated with a GUI, provides a novel synthesis of EDM and visualization techniques such as brush-link visualization and visual summarization to interpret dynamic graphs representing ecosystem dynamics. We applied our proposed system to ecological simulation data and real data from a marine mesocosm study as two key use cases. Our case studies show that our visual analytics tools support the identification and interpretation of the system state by the user, and enable us to discover both confirmatory and new findings in ecosystem dynamics. Overall, we demonstrated that our system can facilitate an understanding of how systems function beyond the intuitive analysis of high-dimensional information based on specific domain knowledge.
Deep Relationship Analysis in Video with Multimodal Feature Fusion

In this paper, we propose a novel multimodal feature fusion method based on scene segmentation to detect the relationships between entities in a long duration video. Specifically, a long video is split into some scenes and entities in the scenes are tracked. Text, audio and visual features in a scene are extracted to predict relationships between different entities in the scene. The relationships between entities construct a knowledge graph of the video and can be used to answer some queries about the video. The experimental results show that our method performs well for deep video understanding on the HLVU dataset.
What if it speaks like it was from the village? Effects of a Robot speaking in Regional Language Variations on Users’ Evaluations

The present contribution investigates the effects of spoken language varieties, in particular non-standard / regional language compared to standard language (in our study: High German), in social robotics. Based on (media) psychological and sociolinguistic research, we assumed that a robot speaking in regional language (i.e., dialect and regional accent) would be considered less competent compared to the same robot speaking in standard language (H1). Contrarily, we assumed that regional language might enhance perceived social skills and likability of a robot, at least so when taking into account whether and how much the human observers making the evaluations talk in regional language themselves. More precisely, it was assumed that the more the study participants spoke in regional language, the better their ratings of the dialect-speaking robot on social skills and likeability would be (H2). We also investigated whether the robot's gender (male vs. female voice) would have an effect on the ratings (RQ). H1 received full, H2 limited empirical support by the data, while the robot's gender (RQ) turned out to be a mostly negligible factor. Based on our results, practical implications for robots speaking in regional language varieties are suggested.
Adaptive Multi-Feature Reliability Re-Determinative Correlation Filter for Visual Tracking

Model drift is a challenging issue for visual object tracking. Most available approaches aim to address this issue by constructing of a stronger discriminative prediction model with the integration of multiple features. In this article, we also address the issue of model drift using multiple features, but we consider the fusion problem from a different point of view, namely, strengthening the features that are suitable for the current scenario while weakening the remaining ones. Therefore, an advanced tracker that adaptively redetermines the reliability for each feature during the tracking process is proposed. Furthermore, to correctly evaluate and redetermine these reliabilities, two different solutions, called model evaluation and numerical optimization, are proposed, and two independent trackers corresponding to these two solutions are implemented. Extensive experiments have been designed on five large datasets to validate the following: 1) The proposed tracking framework is superior for making the tracking model more robust, and 2) the two solutions proposed for redetermining the reliability for each feature are effective. As expected, the two implemented trackers do indeed improve the accuracy and robustness compared to state-of-the-art trackers. Especially on VOT2016, the proposed trackers based on model evaluation and numerical optimization achieve outstanding EAO scores (i.e., <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.453</b> and <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0.428</b> , respectively), outperforming the recently developed top trackers by a large margin.
Assisting Maritime Search and Rescue (SAR) Personnel with AI-Based Speech Recognition and Smart Direction Finding

Communication for processing relevant information plays a paramount role in developing a comprehensive understanding of Search and Rescue (SAR) situations and conducting operations in a successful and reliable manner. Nevertheless, communication systems have not changed considerably in the context of simplifying very high frequency (VHF) maritime communication and enhancing the value of SAR practices. The Automated Transcription of Maritime VHF Radio Communication for SAR Mission Coordination (ARTUS) project approaches this problem with the development of an assistance system which employs AI-based speech recognition and smart direction finding. First, ideas and specified needs of end users for designing the user interface are presented in this paper. Further, preliminary accomplishments of domain specific language training for maritime speech recognition, and the direction-finding algorithms for localizing senders are sketched out. While the preliminary results build a solid ground, additional field experiments will be conducted in order to enhance the accuracy and reliability of speech recognition and direction finding. The identified end user requirements across different personnel groups show commonalities, but call for a differentiated approach in order to meet the challenges and peculiar needs of the various working contexts.
Exploring Fantasy Play in MathMythos AR

Fostering fantasy play for young children through augmented reality has the potential to support 21st century learning activities by stimulating creativity, communication, and flexibility. We present a design exploration of MathMythos AR, an augmented reality card-based math addition game that enables children to engage in basic math tasks embedded in fantastical narratives. We provide insights into our design process, the effects of fantasy and everyday narratives, and discuss implications for AR card games that show potential to foster motivation for learning math. The presented insights are the foundation for the the next iteration of MathMythos AR, where we aim to create compelling and immersive AR narratives for learning.
An effective, secure and efficient tagging method for integrity protection of outsourced data in a public cloud storage

Data Integrity Auditing (DIA) is a security service for checking the integrity of data stored in a PCS (Public Cloud Storage), a third-party based storage service. A DIA service is provided by using integrity tags (hereafter referred to tags). This paper proposes a novel tagging method, called Tagging of Outsourced Data (TOD), for generating and verifying tags of files. TOD has a number of unique properties: (i) it supports both public and private verifiability, and achieves this property with a low level of overhead at the user end, making it particularly attractive to mobile users with resource-constrained devices, (ii) it protects data confidentiality, supports dynamic tags and is resilient against tag forgery and tag tampering (i.e. by authorised insiders) at the same time in more secure and efficient, making the method more suited to the PCS environment, (iii) it supports tags deduplication, making it more efficient, particularly for the user who has many files with data redundancy. Comprehensive security analysis and performance evaluation have been conducted to demonstrate the efficacy and efficiency of the approach taken in the design.
Blockchain in Education – Track ability and Traceability

Block-chain is one of the latest trends of tech world. The technology was introduced with Bitcoin and is being highly experimented for various cryptocurrencies ever since. However, with recent advancements in the technology, the research community and industries have started focusing on its applicability in other various areas such as healthcare, business, logistics, education and many more. Consequently, the research carried out in this paper investigates the use of Block-chain technology in education sector. Over the years, the use of technology in education have helped in improving the accessibility and quality of education using touch screens, Artificial Intelligence, cloud computing, e-classrooms etc. However, there are certain areas in education that need further consideration such as keeping track of students and student related records and transcripts, reduction in manual and paperwork and more. This is where Block-chain is predicted to play a significant role. In this paper, the potential applications of Block-chain for tracing and tracking in education sector are discussed. The discussion is done by first presenting the existing issues in education sector. Further, using the tracking and tracing ability of the Block-chain, few solutions to the existing problems have been proposed. The proposal of solutions is followed by the challenges of integration of Block-chain technology in education sector. Finally, the paper concludes stating that Block-chain has a huge potential in revolutionizing the education sector if the existing challenges are overcome through research and experimentation.
Open-Source Morphology for Endangered Mordvinic Languages

This document describes shared development of finite-state description of two closely related but endangered minority languages, Erzya and Moksha.It touches upon morpholexical unity and diversity of the two languages and how this provides a motivation for shared open-source FST development.We describe how we have designed the transducers so that they can benefit from existing opensource infrastructures and are as reusable as possible.
Certifying Confidence via Randomized Smoothing

Randomized smoothing has been shown to provide good certified-robustness guarantees for high-dimensional classification problems. It uses the probabilities of predicting the top two most-likely classes around an input point under a smoothing distribution to generate a certified radius for a classifier's prediction. However, most smoothing methods do not give us any information about the confidence with which the underlying classifier (e.g., deep neural network) makes a prediction. In this work, we propose a method to generate certified radii for the prediction confidence of the smoothed classifier. We consider two notions for quantifying confidence: average prediction score of a class and the margin by which the average prediction score of one class exceeds that of another. We modify the Neyman-Pearson lemma (a key theorem in randomized smoothing) to design a procedure for computing the certified radius where the confidence is guaranteed to stay above a certain threshold. Our experimental results on CIFAR-10 and ImageNet datasets show that using information about the distribution of the confidence scores allows us to achieve a significantly better certified radius than ignoring it. Thus, we demonstrate that extra information about the base classifier at the input point can help improve certified guarantees for the smoothed classifier. Code for the experiments is available at this https URL.
Alternatives to Bicubic Interpolation Considering FPGA Hardware Resource Consumption

Bicubic interpolation is widely used in real-time image processing systems because of its quality. The real-time implementation of bicubic interpolation requires a lot of hardware resources, especially the number of multipliers because it represents high computational complexity. In this article, a set of algorithms that approximate the bicubic interpolation and reduce the hardware resource consumption are proposed. The proposed algorithms are based on combining linear and cubic interpolations. These algorithms are surveyed and compared in terms of interpolation quality, number of adders, number of multipliers, adaptive logic modules, lookup tables (LUTs), registers, and maximum operating frequency. These algorithms are implemented and tested on an Intel Cyclone V target. This article provides various choices of interpolation algorithms to cater to different application requirements, including accuracy, hardware resource consumption, and throughput performance. The implementation codes are available at github.com/DreamIP/Interpolation.
Fast Communication-Efficient Spectral Clustering over Distributed Data

The last decades have seen a surge of interests in distributed computing thanks to advances in clustered computing and big data technology. Existing distributed algorithms typically assume {\it all the data are already in one place}, and divide the data and conquer on multiple machines. However, it is increasingly often that the data are located at a number of distributed sites, and one wishes to compute over all the data with low communication overhead. For spectral clustering, we propose a novel framework that enables its computation over such distributed data, with "minimal" communications while a major speedup in computation. The loss in accuracy is negligible compared to the non-distributed setting. Our approach allows local parallel computing at where the data are located, thus turns the distributed nature of the data into a blessing; the speedup is most substantial when the data are evenly distributed across sites. Experiments on synthetic and large UC Irvine datasets show almost no loss in accuracy with our approach while about 2x speedup under various settings with two distributed sites. As the transmitted data need not be in their original form, our framework readily addresses the privacy concern for data sharing in distributed computing.