Reviewerly/exHarmony huggingface dataset:
Tuning of bending and torsional modes of bars used in mallet percussion instruments

The present paper proposes a generic design-optimization procedure for the concomitant tuning of bending and torsional modes of bars used in mallet percussion instruments. The undercut model uses a series of discontinuous cuts aimed to facilitate the manufacturing process. Compared to one-dimensional beam models, the use of three-dimensional (3-D) finite element modeling not only allows for the calculation of torsional modes but also provides an increased accuracy in the prediction of modal frequencies, an important aspect when dealing with the demanding tuning tolerances required in a musical context. A global optimization problem is formulated and solved using a surrogate function algorithm, which enables fast computations even with the expensive function evaluations associated with 3-D finite element models. Modal identification of experimental bars demonstrates the potential of the proposed procedure, leading to bars with demanding tuning ratios (six target frequencies) at absolute tuning deviations typically below 15 cents. Measurements of the radiated sound from the experimental bars illustrate the benefits of the improved designs compared to those without torsional tuning. The proposed framework, aside from dealing with the comprehensive tuning of percussive musical bars, also accounts for important practical considerations regarding efficient optimization, modeling accuracy, and manufacturing complexity.
Learning an Optimal Bipartite Graph for Subspace Clustering via Constrained Laplacian Rank

In this article, we focus on utilizing the idea of co-clustering algorithms to address the subspace clustering problem. In recent years, co-clustering methods have been developed greatly with many important applications, such as document clustering and gene expression analysis. Different from the traditional graph-based methods, co-clustering can utilize the bipartite graph to extract the duality relationship between samples and features. It means that the bipartite graph can obtain more information than other traditional graph methods. Therefore, we proposed a novel method to handle the subspace clustering problem by combining dictionary learning with a bipartite graph under the constraint of the (normalized) Laplacian rank. Besides, to avoid the effect of redundant information hiding in the data, the original data matrix is not used as the static dictionary in our model. By updating the dictionary matrix under the sparse constraint, we can obtain a better coefficient matrix to construct the bipartite graph. Based on <xref ref-type="theorem" rid="theorem2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Theorem 2</xref> and <xref ref-type="lemma" rid="lemma1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lemma 1</xref> , we further speed up our algorithm. Experimental results on both synthetic and benchmark datasets demonstrate the superior effectiveness and stability of our model.
Reflexivity of Account, Professional Vision, and Computer-Supported Cooperative Work: Working in the Maritime Domain

Computer-supported cooperative work (CSCW) researchers have plenty to say about designing through texts. However, the relationships between implementation and design are often misunderstood by engineering project systems developers who design collaborative systems, among others. This problem is not new; how to utilize CSCW insights effectively and correctly in engineering projects has long been a concern in the CSCW community. By reviewing year-long, multiple-site ethnographic studies in the maritime domain conducted since 2015, this paper reflects on the "reflexivity of account" and "professional vision" as complementary concepts for ensuring that actors' actions and reasonings are designed in such a way that actors-in this case, maritime operators, systems developers, educators, policymakers, and shipowners-are accountable in and through their member groups. Rather than generalizing my findings and my role in the maritime domain as explicit knowledge to help other CSCW researchers in studying engineering projects, the goal of this paper is to seek intersubjective knowledge of my fieldwork to trigger the utilization of CSCW insights as a fundamental basis for facilitating systems design. The aim is to shape and reshape systems in line with what those actors do in reality.
An Enhanced Memetic Algorithm for Feature Selection in Big Data Analytics with MapReduce

Recently, various research fields have begun dealing with massive datasets forseveral functions.The main aim of a feature selection (FS) model is to eliminate noise, repetitive, and unnecessary featuresthat reduce the efficiency of classification.In a limited period, traditional FS models cannot manage massive datasets and filterunnecessary features.It has been discovered from the state-ofthe-art literature that metaheuristic algorithms perform better compared to other FS wrapper-based techniques.Common techniques such as the Genetic Algorithm (GA) andParticle Swarm Optimization (PSO) algorithm, however, suffer from slow convergence and local optima problems.Even with new generation algorithms such as Firefly heuristic and Fish Swarm Heuristic, these questions have been shown to overcome.This paper introduces an improved memetic optimization (EMO) algorithm for FS in this perspective by using conditional criteria in large datasets.The proposed EMO algorithm divides the entire dataset into sample blocksandconducts the task of learning in the map steps.The partial result obtained is combined into a final vector of feature weights in the reductionprocess which defines the appropriate collection of characteristics.Finally, the method of grouping based on the support vector machine (SVM) takes place.Within the Spark system, the proposed EMO algorithm is applied and the experimental results claim that it is superior to other approaches.The simulation results show that the maximum AUC values of 0.79 and 0.74 respectively are obtained by the EMO-FS model.
GGRNet: Global Graph Reasoning Network for Salient Object Detection in Optical Remote Sensing Images

The task of salient object detection (SOD) in optical remote sensing images (RSIs) is more challenging than the SOD in natural sensing images (NSIs) because of the unique characteristics of remote sensing images such as various object scales and background context redundancy. However, the existing methods ignore the global relationship modeling between different salient objects or different parts in one salient object. To this end, we design a Global Graph Reasoning Module (GGRM) in a lightweight and effective form, and propose a novel Global Graph Reasoning Network (GGRNet) for SOD in optical RSIs. During the graph reasoning, the GGRM considers the role of the global information. Specifically, we explore two ways to utilize the global information, including the global features and global nodes, which are ingeniously added to the interaction of graph nodes and fully integrated through iteration. Besides, we stabilize the projection channel between coordinate space and interactive space through an attention mechanism. The GGRNet outperforms the existing state-of-the-art SOD algorithms on two publicly available datasets, and the number of parameters is only 25.01 Mb.
A Novel Crow Search Algorithm Based on Improved Flower Pollination

Crow search algorithm (CSA) is a new type of swarm intelligence optimization algorithm proposed by simulating the crows’ intelligent behavior of hiding and retrieving food. The algorithm has the characteristics of simple structure, few control parameters, and easy implementation. Like most optimization algorithms, the crow search algorithm also has the disadvantage of slow convergence and easy fall into local optimum. Therefore, a crow search algorithm based on improved flower pollination algorithm (IFCSA) is proposed to solve these problems. First, the search ability of the algorithm is balanced by the reasonable change of awareness probability, and then the convergence speed of the algorithm is improved. Second, when the leader finds himself followed, the cross-pollination strategy with Cauchy mutation is introduced to avoid the blindness of individual location update, thus improving the accuracy of the algorithm. Experiments on twenty benchmark problems and speed reducer design were conducted to compare the performance of IFCSA with that of other algorithms. The results show that IFCSA has better performance in function optimization and speed reducer design problem.
Class imbalance problem in short-term solar flare prediction

Using data-driven algorithms to accurately forecast solar flares requires reliable data sets. The solar flare dataset is composed of many non-flaring samples with a small percentage of flaring samples. This is called the class imbalance problem in data mining tasks. The prediction model is sensitive to most classes of the original data set during training. Therefore, the class imbalance problem for building up the flare prediction model from observational data should be systematically discussed. Aiming at the problem of class imbalance, three strategies are proposed corresponding to the data set, loss function, and training process: Type I resamples the training samples, including oversampling for the minority class, undersampling, or mixed sampling for the majority class. Type II usually changes the decision-making boundary, assigning the majority and minority categories of prediction loss to different weights. Type III assigns different weights to the training samples, the majority categories are assigned smaller weights, and the minority categories are assigned larger weights to improve the training process of the prediction model. The main work of this paper compares these imbalance processing methods when building a flare prediction model and tries to find the optimal strategy. Our results show that among these strategies, the performance of oversampling and sample weighting is better than other strategies in most parameters, and the generality of resampling and changing the decision boundary is better.
Efficient congruencing in ellipsephic sets: the quadratic case

We bound the number of solutions to a quadratic Vinogradov system of equations in which the variables are required to satisfy digital restrictions in a given base. Certain sets of permitted digits, namely those giving rise to few representations of natura
Deep Learning for Obstacle Avoidance in Autonomous Driving

This chapter discusses various real-time challenges in the context of obstacle detection for autonomous driving such as small objects, occlusion, shadows, object size variations, and loss of depth information while projecting a three-dimensional scene on a two-dimensional plane. It is deep learning for obstacle detection, which concerns the selection of models and algorithms that can have their parameters tuned to improve the recommended performance criterion. The chapter considers the algorithms for obstacle avoidance using deep learning, different deep learning architectures that involve CNN and its variants, and the detailed YOLO algorithm for obstacle avoidance. It presents a huge range of research work on detecting and avoiding obstacles in the path of autonomous vehicles using deep learning frameworks. Datasets are important for scientists and designers, as a large number of the methods and apparatuses must be tried and tested before the autonomous vehicle is developed.
LAE : Long-Tailed Age Estimation

Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification. The effectiveness of our approach has been demonstrated on the dataset provided by organizers of Guess The Age Contest 2021.
Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment

Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple's Siri and Amazon's Alexa for obtaining medical information.The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants.Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversational assistant's response was randomly varied to include either a correct or incorrect paraphrase of the query or a disclaimer message-or not-telling the participants that they should not act on the advice without first talking to a physician. The participants were then asked what actions they would take based on their interaction, along with the likelihood of taking the action. The reported actions were recorded and analyzed, and the participants were interviewed at the end of each interaction.A total of 32 participants completed the study, each interacting with 4 conversational assistants. The participants were on average aged 42.44 (SD 14.08) years, 53% (17/32) were women, and 66% (21/32) were college educated. Those participants who heard a correct paraphrase of their query were significantly more likely to state that they would follow the medical advice provided by the conversational assistant (χ21=3.1; P=.04). Those participants who heard a disclaimer message were significantly more likely to say that they would contact a physician or health professional before acting on the medical advice received (χ21=43.5; P=.001).Designers of conversational systems should consider incorporating both disclaimers and feedback on query understanding in response to user queries for medical advice. Unconstrained natural language input should not be used in systems designed specifically to provide medical advice.
Machine Learning based Hybrid Approach for Email Spam Detection

Today, everyone uses official data via email. Spam is the number one subject on the Internet. Emails containing spam from spammers are easy to send. Spam fills up our inboxes with inappropriate emails. Spammers steal our confidential information from our devices, such as Files, contacts. Even if we have the latest technology, it is difficult to do it. This article aims to propose a Term Frequency inverse document frequency (TFIDF) method by implementing an artificial neural network. The effects are as compared in phrases of the confusion matrix, accuracy, and precision. To assess the suitability of ANN we got a tendency to use a Kaggle data set that has a less proportion of spammed emails and real emails. The Outputs show that the positive yield of ANN appears at the ratio of 97.5 8% by using TFIDF based ANN
An Immersive Visual Analytics Platform for Multidimensional Dataset

Virtual Reality is a budding new medium of interaction, but ways in which to use it for data analytics are not immediately obvious. Through our research, we investigated new and useful ways to view multidimensional data in a virtual environment. In this paper, we present a virtual laboratory with collaborative visualizations in different spaces. We focused on geographic view in the center of the room by utilizing spatial freedom in Virtual Reality, which informed both the geographic visualization and time series data. Through this laboratory and the collaborative visualizations housed within, we believe we've found a novel way to interpret data in Virtual Reality.
Adversarial Attack against Cross-lingual Knowledge Graph Alignment

Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks.However, there is still a paucity of vulnerability analyses of cross-lingual entity alignment under adversarial attacks.This paper proposes an adversarial attack model with two novel attack techniques to perturb the KG structure and degrade the quality of deep cross-lingual entity alignment.First, an entity density maximization method is employed to hide the attacked entities in dense regions in two KGs, such that the derived perturbations are unnoticeable.Second, an attack signal amplification method is developed to reduce the gradient vanishing issues in the process of adversarial attacks for further improving the attack effectiveness.
On the (In)Tractability of Reinforcement Learning for LTL Objectives

In recent years, researchers have made significant progress in devising reinforcement-learning algorithms for optimizing linear temporal logic (LTL) objectives and LTL-like objectives. Despite these advancements, there are fundamental limitations to how well this problem can be solved. Previous studies have alluded to this fact but have not examined it in depth. In this paper, we address the tractability of reinforcement learning for general LTL objectives from a theoretical perspective. We formalize the problem under the probably approximately correct learning in Markov decision processes (PAC-MDP) framework, a standard framework for measuring sample complexity in reinforcement learning. In this formalization, we prove that the optimal policy for any LTL formula is PAC-MDP-learnable if and only if the formula is in the most limited class in the LTL hierarchy, consisting of formulas that are decidable within a finite horizon. Practically, our result implies that it is impossible for a reinforcement-learning algorithm to obtain a PAC-MDP guarantee on the performance of its learned policy after finitely many interactions with an unconstrained environment for LTL objectives that are not decidable within a finite horizon.
Risk-Averse Bayes-Adaptive Reinforcement Learning

In this work, we address risk-averse Bayesadaptive reinforcement learning. We pose the problem of optimising the conditional value at risk (CVaR) of the total return in Bayes-adaptive Markov decision processes (MDPs). We show that a policy optimising CVaR in this setting is risk-averse to both the parametric uncertainty due to the prior distribution over MDPs, and the internal uncertainty due to the inherent stochasticity of MDPs. We reformulate the problem as a two-player stochastic game and propose an approximate algorithm based on Monte Carlo tree search and Bayesian optimisation. Our experiments demonstrate that our approach significantly outperforms baseline approaches for this problem.
Effects of Head-locked Augmented Reality on User’s Performance and Perceived Workload

An augmented reality (AR) environment includes a set of digital elements with which the users interact while performing certain tasks. Recent AR head-mounted displays allow users to select how these elements are presented. However, few studies have been conducted to examine the effect of presenting augmented content on user performance and workload. This study aims to evaluate two methods of presenting augmented content — world-locked and head-locked modes in a data entry task. A total of eighteen participants performed the data entry task in this study. The effectiveness of each mode is evaluated in terms of task performance, muscle activity, perceived workload, and usability. The results show that the task completion time is shorter, and the typing speed is significantly faster in the head-locked mode while the world-locked mode achieved higher scores in terms of preference. The findings of this study can be applied to AR user interfaces to improve content presentation and enhance the user experience.
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events

In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.NEREL is significantly larger than existing Russian datasets: to date it contains 56K annotated named entities and 39K annotated relations.Its important difference from previous datasets is annotation of nested named entities, as well as relations within nested entities and at the discourse level.NEREL can facilitate development of novel models that can extract relations between nested named entities, as well as relations on both sentence and document levels.NEREL also contains the annotation of events involving named entities and their roles in the events.The NEREL collection is available via https://github.com/nerel-ds/NEREL.
Blockchain breeding grounds: Asia's advance over the USA and Europe

Blockchain is argued to disrupt almost every industry and research field. Among other technologies, it contributes to the next fourth industrial revolution. While prominent technologies, such as AI and IoT, have already proven their potential and value in many applications, Blockchain applications have gathered only limited appeal. We ask, which countries drive inventive activity in Blockchain and to which magnitude and type. Using worldwide Blockchain patent applications from 2009 to 2020 as inventive outcome indicators the National Breeding Ground (NBG) index and the International Breeding Ground (IBG) index are calculated. The NBG index is related to the interest of a country to file Blockchain patents in its domestic market. The IBG index is related to the interest of a country to file and exploit Blockchain patents in foreign markets. We observe China and the USA as strong national and international breeding grounds. In particular, Asian countries drive Blockchain inventions. European countries contribute marginally to the Blockchain patent landscape and, together with the USA, are losing ground to Asia. Comparisons to prior work on Blockchain patenting and research patterns partially reflect our results. However, comparisons to AI suggest different patenting and exploitation strategies for Blockchain and AI in China.
Review of Joint Encoding and Encryption for Image Transmission using Chaotic Map, LDPC and AES Encryption

In today’s era of communication, sharing of information is unavoidable. Analysis of security and reliability while communicating is essential. Information may get altered, copied and copyright can be infringed. Previously, digital watermarking and different encryption techniques like AES, DES, HASH and MAC were used as a primary security technique for information hiding. But, in all these methods, there are some vulnerabilities, because of which information may be hacked or changed while transmission. In addition to the security of the information, there should also be an error-free transmission of data. This review paper discusses various image encryption techniques which are using joint error correction and encryption methods. Based on the literature review, a novel method for joint encryption and encoding is suggested. Low-Density Parity-Check (LDPC) code, a highly recommended technique in 5G, is used for reliability. Chaotic sequence based on a logistic map is used for security. Proposed technique also uses the S-Box of AES algorithm for enhancing the security obtained by combining LDPC and Chaotic sequence. This paper aims to provide a general overview of image encryption techniques. Based on the study, a new system for joint encryption and encoding using LDPC, Chaotic map and AES S-Box is suggested.