Reviewerly/exHarmony huggingface dataset:
Setup for musical reed instrument testing using constant phase stroboscopic imaging

Reed woodwind instruments pose great challenges in modeling because their functioning is based on a combination of hydrodynamics, acoustics, and mechanics. Reproducible measurements therefore remain crucial for further development of reeds and instrument mouthpieces. We present a method to measure reed behavior and acoustic response under realistic, well-defined playing conditions. A measurement setup based on constant phase stroboscopic digital imaging correlation (DIC) is proposed. A computer-controlled timer delay triggering system combined with high-power LEDs allows one to obtain clear images at different phases of the vibrational period. From these high-quality images, digital image correlation DIC allows one to analyze both displacement and strain over the full visible surface of the reed. The presented technique can also be applied to other nonfrequency-imposed vibrating structures. The method is demonstrated on a saxophone reed, and we show how it can be used to determine playability range of a mouthpiece and vibrational behavior of the reed.
Image Encryption Using RK-RSA Algorithm in Aadhaar Card

Cryptography is used for secretly sending information. The information or given data is protected by cryptographic technique. The technique is used in Text and images. The technique is supported by a lot of algorithms. RSA is a better encryption technique for smart cards. In this paper, an image in the Aadhaar card is encrypted using the RK-RSA algorithm for better protection and confidentiality. The proposed RK-RSA algorithm is very secure for smart cards and Aadhaar cards. The better performance of the RK-RSA is evaluated based on the Avalanche Effect, Speed, Throughput, and Power Consumption. The improved performance of the RK-RSA algorithm’s experimental results is reported. The mathematical justification supporting the RK-RSA algorithm is also detailed.
The Univariate Marginal Distribution Algorithm Copes Well with Deception and Epistasis

In their recent work, Lehre and Nguyen (2019) show that the univariate marginal distribution algorithm (UMDA) needs time exponential in the parent populations size to optimize the DeceptiveLeadingBlocks (DLB) problem. They conclude from this result that univariate EDAs have difficulties with deception and epistasis. In this work, we show that this negative finding is caused by the choice of the parameters of the UMDA. When the population sizes are chosen large enough to prevent genetic drift, then the UMDA optimizes the DLB problem with high probability with at most λ(n2+2elnn) fitness evaluations. Since an offspring population size λ of order nlogn can prevent genetic drift, the UMDA can solve the DLB problem with O(n2logn) fitness evaluations. In contrast, for classic evolutionary algorithms no better runtime guarantee than O(n3) is known (which we prove to be tight for the (1+1) EA), so our result rather suggests that the UMDA can cope well with deception and epistatis. From a broader perspective, our result shows that the UMDA can cope better with local optima than many classic evolutionary algorithms; such a result was previously known only for the compact genetic algorithm. Together with the lower bound of Lehre and Nguyen, our result for the first time rigorously proves that running EDAs in the regime with genetic drift can lead to drastic performance losses.
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation

This paper addresses the problem of simultaneous machine translation (SiMT) by exploring two main concepts: (a) adaptive policies to learn a good trade-off between high translation quality and low latency; and (b) visual information to support this process by providing additional (visual) contextual information which may be available before the textual input is produced.For that, we propose a multimodal approach to simultaneous machine translation using reinforcement learning, with strategies to integrate visual and textual information in both the agent and the environment.We provide an exploration on how different types of visual information and integration strategies affect the quality and latency of simultaneous translation models, and demonstrate that visual cues lead to higher quality while keeping the latency low.
InterpreT: An Interactive Visualization Tool for Interpreting Transformers

With the increasingly widespread use of Transformer-based models for NLU/NLP tasks, there is growing interest in understanding the inner workings of these models, why they are so effective at a wide range of tasks, and how they can be further tuned and improved.To contribute towards this goal of enhanced explainability and comprehension, we present InterpreT, an interactive visualization tool for interpreting Transformer-based models.In addition to providing various mechanisms for investigating general model behaviours, novel contributions made in Inter-preT include the ability to track and visualize token embeddings through each layer of a Transformer, highlight distances between certain token embeddings through illustrative plots, and identify task-related functions of attention heads by using new metrics.Inter-preT is a task agnostic tool, and its functionalities are demonstrated through the analysis of model behaviours for two disparate tasks: Aspect Based Sentiment Analysis (ABSA) and the Winograd Schema Challenge (WSC).
Efficient Digital Signature Scheme for Internet of Things

Apparently enthusiasm for the Internet of things (IoT) has as of late arrived at its top, with a lot of center from both the private and public parts. IoT, an innovation that empowers the trading of information through linkage among all items encompassing the client, can make new administrations. Information correspondence among objects isn't restricted to individual data, yet can likewise convey diverse information types, for example, detecting data gathered from the general condition. At the point when such information is gathered and utilized malevolently by an assailant, it is more defenseless against dangers than in traditional organization situations. In this article we proposed for digital signature scheme for Internet of Things.
Usability Evaluation of Online Educational Applications in COVID-19

COVID-19 is a pandemic faced by almost every country in the world, this has resulted in health crisis. Due to COVID-19, all the countries around the world have decided to close all educational institutes to prevent this pandemic. Educational institutes have taken every possible measure to minimize the impact of the closure of schools and introduce the concept of an online education system which is not only a massive shock for parents but it also affects the children's learning process and social life. The educational applications (Apps) are very important, because they offer more opportunities for development and growth to society. In this pandemic situation, educational Apps like Zoom, HEC LMS, Google Classroom, and Skype, etc. are the need of the hour when everything goes online. In this paper, the usability features of online educational Apps are thoroughly discussed including the effectiveness and usability for students. Using the results obtained from the survey, this paper observes the student's perspective of usefulness of online educational Apps in student’s learning process of different age groups. It also analyzes the easiness for students to understand, interact and use these Apps.
An effective Parallel Integrated Neural Network System for industrial data prediction

At present, General Regression Neural Network (GRNN) have been more and more used for data prediction in industry, however, because its smoothing factor is difficult to determine, it is easy to obtain poor prediction accuracy when using it to predict complex problems in reality. To tackle these problems, an effective Parallel Integrated Neural Network System (PINN) is proposed in this paper. The model is a combination of GRNN and Adaptive Dynamic Grey Wolf Optimizer (ADGWO), in this model, the smoothing factor and calculation result of GRNN are taken as the individual position information and individual fitness of ADGWO, respectively, and the training of the model is completed through the optimization of ADGWO. Different from Grey Wolf Optimizer (GWO), ADGWO introduces the nonlinear cosine decreasing convergence factor, the weighted position update method and the central disturbance criterion, aiming to balance the exploitation and exploration. Applying PINN to the soil heavy metal datasets from Yinchuan of Ningxia and Wuhan, China for data prediction, the experimental results show that PINN has higher average prediction accuracy than several comparative models, especially an increase of 8.05% compared with Wavelet Neural Network, which proves that PINN can be effectively applied to industrial data prediction.
Optimizing the deployment of quantum key distribution switch-based networks

Quantum key distribution (QKD) networks provide an infrastructure for establishing information-theoretic secure keys between legitimate parties via quantum and authentic classical channels. The deployment of QKD networks in real-world conditions faces several challenges, which are related in particular to the high costs of QKD devices and the condition to provide reasonable secret key rates. In this work, we present a QKD network architecture that provides a significant reduction in the cost of deploying QKD networks by using optical switches and reducing the number of QKD receiver devices, which use single-photon detectors. We describe the corresponding modification of the QKD network protocol. We also provide estimations for a network link of a total of 670 km length consisting of 8 nodes and demonstrate that the switch-based architecture achieves significant resource savings of up to 28%, while the throughput is reduced by 8% only.
A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS

Intelligent transport systems (ITS) are a promising area of studies. One implementation of ITS are advanced driver assistance systems (ADAS), involving the problem of obstacle detection in traffic. This study evaluated the YOLOv4 model as a state-of-the-art CNN-based one-stage detector to recognize traffic obstacles. A new dataset is proposed containing traffic obstacles on Indonesian roads for ADAS to detect traffic obstacles that are unique to Indonesia, such as pedicabs, street vendors, and bus shelters, and are not included in existing datasets. This study established a traffic obstacle dataset containing eleven object classes: cars, buses, trucks, bicycles, motorcycles, pedestrians, pedicabs, trees, bus shelters, traffic signs, and street vendors, with 26,016 labeled instances in 7,789 images. A performance analysis of traffic obstacle detection on Indonesian roads using the dataset created in this study was conducted using the YOLOv4 method.
Do Multi-Hop Question Answering Systems Know How to Answer the Single-Hop Sub-Questions?

Multi-hop question answering (QA) requires a model to retrieve and integrate information from multiple passages to answer a question.Rapid progress has been made on multi-hop QA systems with regard to standard evaluation metrics, including EM and F1.However, by simply evaluating the correctness of the answers, it is unclear to what extent these systems have learned the ability to perform multihop reasoning.In this paper, we propose an additional sub-question evaluation for the multihop QA dataset HotpotQA, in order to shed some light on explaining the reasoning process of QA systems in answering complex questions.We adopt a neural decomposition model to generate sub-questions for a multi-hop question, followed by extracting the corresponding sub-answers.Contrary to our expectation, multiple state-of-the-art multi-hop QA models fail to answer a large portion of sub-questions, although the corresponding multi-hop questions are correctly answered.Our work takes a step forward towards building a more explainable multi-hop QA system.
Mapping of Aluminum Concentration in Bauxite Mining Residues Using Sentinel-2 Imagery

There is a growing interest in the characterization of mining residues, both for environmental assessments and critical raw materials recovery. The lack of sufficient in situ samples hampers an effective geostatistical modelling of material concentrations variability. This paper proposes a method to characterize the aluminum spatial variability in a mine residue from remote sensing data and imprecise information from daily dumping procedures. The method is proposed for the mapping of aluminum within a Greek bauxite residue, using Sentinel-2 imagery. The spatial correlation between metal concentrations and remote sensing indicators (e.g., spectral band ratios) is the premise for mapping aluminum varieties. The proposed method is based on Conditional Gaussian Co-Simulation, where Sentinel-2 images can be used as auxiliary variables. Simulation results are compared with the Co-kriging estimation method. To perform the Co-kriging estimation, the same conditions as simulation are used (same inputs, models, and neighborhoods). Simulation results quantified the metals variability in mining residues, presenting the metal concentration of piled materials in two time periods. For results validation and selecting the best map, fourteen validation samples were used. For the best representative maps of aluminum concentration, a correlation coefficient of about 0.7 between the validation data and obtained aluminum concentration map was obtained.
StructSum: Summarization via Structured Representations

Abstractive text summarization aims at compressing the information of a long source document into a rephrased, condensed summary.Despite advances in modeling techniques, abstractive summarization models still suffer from several key challenges: (i) layout bias: they overfit to the style of training corpora; (ii) limited abstractiveness: they are optimized to copying n-grams from the source rather than generating novel abstractive summaries; (iii) lack of transparency: they are not interpretable.In this work, we propose a framework based on document-level structure induction for summarization to address these challenges.To this end, we propose incorporating latent and explicit dependencies across sentences in the source document into end-to-end single-document summarization models.Our framework complements standard encoderdecoder summarization models by augmenting them with rich structure-aware document representations based on implicitly learned (latent) structures and externally-derived linguistic (explicit) structures.We show that our summarization framework, trained on the CNN/DM dataset, improves the coverage of content in the source documents, generates more abstractive summaries by generating more novel n-grams, and incorporates interpretable sentence-level structures, while performing on par with standard baselines. 1
Distributed NLI: Learning to Predict Human Opinion Distributions for Language Reasoning

We introduce distributed NLI, a new NLU task with a goal to predict the distribution of human judgements for natural language inference.We show that by applying additional distribution estimation methods, namely, Monte Carlo (MC) Dropout, Deep Ensemble, Re-Calibration, and Distribution Distillation, models can capture human judgement distribution more effectively than the softmax baseline.We show that MC Dropout is able to achieve decent performance without any distribution annotations while Re-Calibration can give further improvements with extra distribution annotations, suggesting the value of multiple annotations for one example in modeling the distribution of human judgements.Despite these improvements, the best results are still far below the estimated human upper-bound, indicating that predicting the distribution of human judgements is still an open, challenging problem with a large room for improvements.We showcase the common errors for MC Dropout and Re-Calibration.Finally, we give guidelines on the usage of these methods with different levels of data availability and encourage future work on modeling the human opinion distribution for language reasoning.
Karakteristik Histori Bencana Indonesia Periode 1815 – 2019 Berdasarkan Jumlah Bencana, Kematian, Keterpaparan dan Kerusakan Rumah Akibat Bencana

[ Historical Characteristics of Disasters in Indonesia for the Period 1815 - 2019 Based on the Number of Disasters, Deaths, Exposure and Damage to Houses Due to Disasters ] Indonesia is a country with high potential for disasters in the world. Natural, non-natural and social disasters in Indonesia must be able to be used as laboratories and disaster experts as a form of state protection for its people. This paper aims to describe the historical characteristics of disasters based on indicators of the number of disaster events, deaths, exposure, and damage to houses due to disasters. We found that the characteristics differed in each of the indicators studied. Meanwhile, the increasing graph is shown by each indicator in this study. Analysis of the historical characteristics of disasters is an important tool needed to support sustainable disaster management programs.
XRSISE: An XR Training System for Interactive Simulation and Ergonomics Assessment

The use of virtual reality (VR) techniques for industrial training provides a safe and cost effective solution that contributes to increased engagement and knowledge retention levels. However, the process of experiential learning in a virtual world without biophysical constraints might contribute to muscle strain and discomfort, if ergonomic risk factors are not considered in advance. Under this scope, we have developed a digital platform which employs extended reality (XR) technologies for the creation and delivery of industrial training programs, by taking into account the users and workplace specificities through the adaptation of the 3D virtual world to the real environment. Our conceptual framework is composed of several inter-related modules: 1) the XR tutorial creation module , for automatic recognition of the sequence of actions composing a complex scenario while this is demonstrated by the educator in VR, 2) the XR tutorial execution module , for the delivery of visually guided and personalized XR training experiences, 3) the digital human model (DHM) based simulation module for creation and demonstration of job task simulations avoiding the need of an actual user and 4) the biophysics assessment module for ergonomics analysis given the input received from the other modules. Three-dimensional reconstruction and aligned projection of the objects situated in the real scene facilitated the imposition of inherent physical constraints, thereby allowed to seamlessly blend the virtual with the real world without losing the sense of presence.
Generating Weather Comments from Meteorological Simulations

The task of generating weather-forecast comments from meteorological simulations has the following requirements: (i) the changes in numerical values for various physical quantities need to be considered, (ii) the weather comments should be dependent on delivery time and area information, and (iii) the comments should provide useful information for users.To meet these requirements, we propose a datato-text model that incorporates three types of encoders for numerical forecast maps, observation data, and meta-data.We also introduce weather labels representing weather information, such as sunny and rain, for our model to explicitly describe useful information.We conducted automatic and human evaluations.The results indicate that our model performed best against baselines in terms of informativeness.We make our code and data publicly available 1 . Cloud cover Air pressure Air temperature PrecipitationDelivery time: 05:51 a.m. on 06
Chaotic antiferromagnetic nano-oscillator driven by spin torque

We theoretically describe the behavior of a terahertz nano-oscillator based on an anisotropic antiferromagnetic dynamical element driven by spin torque. We consider the situation when the polarization of the spin current is perpendicular to the external magnetic field applied along the anisotropy easy axis. We determine the domain of the parametric space (field, current) where the oscillator demonstrates chaotic dynamics. Characteristics of the chaotic regimes are analyzed using conventional techniques such as spectra of the Lyapunov exponents. We show that the threshold current of the chaos appearance is particularly low in the vicinity of the spin-flop transition. In this regime, we consider the mechanism of the chaos appearance in detail when the field is fixed and the current density increases. We show that the appearance of chaos is preceded by a regime of quasiperiodic dynamics on the surface of a two-frequency torus arising in phase space as a result of the Neimark-Sacker bifurcation.
Exploiting Position and Contextual Word Embeddings for Keyphrase Extraction from Scientific Papers

Keyphrases associated with research papers provide an effective way to find useful information in the large and growing scholarly digital collections.In this paper, we present KPRank, an unsupervised graph-based algorithm for keyphrase extraction that exploits both positional information and contextual word embeddings into a biased PageRank.Our experimental results on five benchmark datasets show that KPRank that uses contextual word embeddings with additional position signal outperforms previous approaches and strong baselines for this task.
Dynamic Graph Partitioning Scheme for Supporting Load Balancing in Distributed Graph Environments

As dynamic graph data have been actively used, incremental graph partition schemes have been studied to efficiently store and manage large graphs. In this paper, we propose a vertex-cut based novel incremental graph partitioning scheme that supports load balancing in a distributed environment. The proposed scheme chooses the load of each node that considers its storage utilization and throughput as the partitioning criterion. The proposed scheme defines hot data that means a particular vertex frequently searched among graphs requested by queries. We manage and utilize hot data for graph partitioning. Finally, we perform vertex-cut based dynamic graph partitioning by using a vertex replication index, the load each node, and hot data to distribute the load evenly in a distributed environment. In order to verify the superiority of the proposed partitioning scheme, we compare it with the existing partitioning schemes through a variety of performance evaluations.
A Novel Attribute-Based Encryption Approach with Integrity Verification for CAD Assembly Models

Cloud manufacturing is one of the three key technologies that enable intelligent manufacturing. This paper presents a novel attribute-based encryption (ABE) approach for computer-aided design (CAD) assembly models to effectively support hierarchical access control, integrity verification, and deformation protection for co-design scenarios in cloud manufacturing. An assembly hierarchy access tree (AHAT) is designed as the hierarchical access structure. Attribute-related ciphertext elements, which are contained in an assembly ciphertext (ACT) file, are adapted for content keys decryption instead of CAD component files. We modify the original Merkle tree (MT) and reconstruct an assembly MT. The proposed ABE framework has the ability to combine the deformation protection method with a content privacy of CAD models. The proposed encryption scheme is demonstrated to be secure under the standard assumption. Experimental simulation on typical CAD assembly models demonstrates that the proposed approach is feasible in applications.