Reviewerly/exHarmony huggingface dataset:
Sound tuning in asymmetrically braced guitars

Guitar bracings generally follow a symmetrical design in contrast to the asymmetrical assembly of the sound post and bass bar in violins. However, one of Conde's progressive constructions to combine the classical Spanish guitar with the flamenco guitar is based on an asymmetrical bracing. Mobility measurements on a well-preserved exemplar reveal four strong resonances between air modes A0 and A1, whereas other guitars reveal only one or two resonances in the same region. These are equivalent to the intensively discussed signature modes in violins, which are believed to prominently determine sound quality. The paper develops a model to explain the existence of the four resonances and how these can be tuned in terms of frequency and intensity. The tuning parameters for three of these resonances comprise (i) a measure of asymmetry, (ii) a measure of how wide the bridge overhangs relevant braces, (iii) a measure of coupling between the top and back plates, and finally (iv) the tuning between the top and back plates. Data from modeling are referenced against a lumped experimental setup and measured data from the guitar. Asymmetrical bracing constructions leverage mutual mode coupling and increase radiation efficiency, but they are more difficult to tune than symmetrical constructions.
Object Modifier Generation for Image Captioning

The recently proposed Encoder-Decoder based models have made great progress in image captioning. However, we find that the captions generated by them ignore some objects that humans care about and mostly lack modifiers for objects. To solve these problems, we proposal an Object Modifier Generation Mechanism (OMGM), which combines modifier vocabulary building and Multi-Task Learning (MTL). By building a modifier vocabulary, OMGM-model emphasizes the described objects, which are more concerned by humans. MTL jointly trains the text generation and the modifier generation, where the auxiliary task makes it easier for the main task to learn more features of modifiers. We analysis qualitatively and quantitatively on Microsoft COCO dataset. Our OMGM-model achieves the best performance on most metrics compared to other models. These results can also quantitatively prove that our OMGM is good for generating better captions with more objects and modifiers.
Explicit-duration Hidden Markov Models for quantum state estimation

An explicit-duration Hidden Markov Model with a nonparametric kernel estimator of the state duration distribution is specified. The motivation comes from the physical problem of extracting the maximum information from an open quantum system subject to an external perturbation, which induces a change in the dynamics of the system. A nonparametric kernel estimator for discrete data is introduced, which is consistent and improves the estimates accuracy in presence of sparse data. To reconstruct the hidden dynamics, a Viterbi algorithm is used, which is robust against the underflow problem. Finite sample properties are investigated through an extensive Monte Carlo study showing that our formulation outperforms the original one both in small and in large samples.
Learning how to drive using DDPG Algorithm with Double Experience Buffer Priority Sampling

The driving policy based on the DDPG algorithm has the defects of convergence difficulty and poor anti-interference ability. An important reason for these defects is that the random experience replay mechanism cannot reasonably utilize experience collected by agents. It can neither use different experiences for targeted training in different training stages, nor can it pick out high-quality experience to speed up policy convergence. This paper proposes a double experience buffer priority sampling mechanism, which can distinguish positive experience and negative experience, and replay these experiences according to the needs of the current training stage, and can also select high-quality experience for priority replay. The results of simulation experiments show that our method not only increases the convergence speed of the policy, but also improves the driving performance in the new environment.
Laboratory-Scale Implementation of Ethereum Based Decentralized Application for Solar Energy Trading

The decentralized application (DApp) is an application that has a backend operation on the distributed computing nodes system. DApp has been built on decentralized technology such as blockchain. The advantages of DApp are security, transparency, and reliability. There are several use cases of DApp for many aspects such as prediction potential trading gains on Augur, sharing economy of computing power by Golem and browsing, chatting, and payment on Status. However, these DApps are utilized without any details about how to implement it. This paper address this issue by presenting the implementation of solar energy trading. Ethereum Blockchain – an open-source platform for DApp has been proposed and applied for solar energy trading. The token is created by using the ERC20 token for trading. The wallet is deployed by Metamask. Transactions, assets, and participants are made by Ganache and tested by Truffle. Moreover, the trading algorithm has been shown to check the correction between seller and buyer with the smart contract on Solidity. Lastly, React- a javascript library for building user interfaces has been deployed as a front- end to make users interactive in solar energy trading.
Adaptive Evolution Mechanism of Blockchain Community Based on Token-based Halving Event

When Nakamoto proposed bitcoin, he also designed a half reduction mechanism. In previous studies, there are few studies on the Bitcoin halving event from the perspective of public opinion mining. This article mainly conducts public opinion mining on the content of Weibo within one month before and after each Bitcoin halving events, and studies the public opinion on Weibo of the Bitcoin halving event and its disturbing impact on the Bitcoin price market. The experimental results show that with the repeated occurrence of the halving event, the public pays more and more attention to it, and the specific content of its attention is constantly changing, which reflects the trend of Bitcoin market price changes to a certain extent. This research is of great value for studying the Bitcoin halving mechanism from the perspective of Weibo public opinion.
Developing an Intelligent System for Recommending Products

When it comes to making decisions on which product to buy, knowing the overall reviews from other users becomes very helpful. Evaluating this task from user ratings is so simple. Although a machine can be used for evaluating the recommendations, simply by calculating its user ratings, sometimes it becomes difficult to provide accurate and efficient results. As therefore, evaluating users’ comments usually leads to assigning humans to read all the comments one by one and then let them decide on how useful the product seems. This is a tedious process which wastes our valuable time and resources due to no way of automating the process. On the other hand, selecting the most valuable product from an enormous number of reviews becomes a hectic task for the consumers. Considering all of the above, we have developed a machine learning based intelligent system which not only evaluates the ratings from users’ reviews but also provides a reflection about the products which are popular simply by analyzing those reviews.
4x4 Census Transform

This paper proposes a novel 4x4 census transform (4x4CT) to encourage further research in computer vision and visual computing. Unlike the traditional census transform (3x3CT) which only uses a nine pixels kernel or 3x3 window size, the proposed 4x4CT uses a sixteen pixels kernel or 4x4 window size with four overlapped groups of 3x3 kernel size. In each overlapping group, a reference input pixel profits from its nearest eight pixels to produce an eight bits binary string convertible to a grayscale decimal integer corresponding to the 4x4CT's output pixel. Preliminary subjective assessments were more promising with the 4x4CT transformed images than 3x3CT transformed images.
Blocksequences of k-local Words

The locality of words is a relatively young structural complexity measure, introduced by Day et al. in 2017 in order to define classes of patterns with variables which can be matched in polynomial time. The main tool used to compute the locality of a word is called marking sequence: an ordering of the distinct letters occurring in the respective order. Once a marking sequence is defined, the letters of the word are marked in steps: in the i\(^{\text {th}}\) marking step, all occurrences of the i\(^{\text {th}}\) letter of the marking sequence are marked. As such, after each marking step, the word can be seen as a sequence of blocks of marked letters separated by blocks of non-marked letters. By keeping track of the evolution of the marked blocks of the word through the marking defined by a marking sequence, one defines the blocksequence of the respective marking sequence. We first show that the words sharing the same blocksequence are only loosely connected, so we consider the stronger notion of extended blocksequence, which stores additional information on the form of each single marked block. In this context, we present a series of combinatorial results for words sharing the extended blocksequence.
Hierarchical Multiobjective Shortest Path Problems

We consider the shortest path problem on graphs with weights taking values in Cartesian products of cost monoids. Such cost structures appear in multiobjective planning including, for instance, the minimum-violation planning framework. It is known that these products often do not satisfy the conditions of a cost monoid. Classical dynamic programming graph search algorithms may therefore fail to find an optimal solution. We isolate the concept of a regular cost monoid and propose an iterative search algorithm that finds an optimal path in graphs weighted by products of such costs. Our algorithm allows this class of multiobjective planning problems to be solved in polynomial time.
Representation-guided generative adversarial network for unpaired photo-to-caricature translation

Imitating the painting style of one caricature source is an interesting and important application. It requires to capture the caricature style from one reference image, and generate target caricature image with similar style representation based on one source photo. Recently, image-to-image translation is a proven and potential framework for photo-to-caricature task. However, it still suffers from three drawbacks: (1) annotating aligned photo-to-caricature pairs is expensive and time-consuming; (2) the photo-to-caricature requires to capture and exaggerate the high-level semantic representations; and (3) the multiple painting styles increase the translation difficulty. To tackle these issues, we propose an innovative representation-guided photo-to-caricature translation framework based on unpaired images. The representation-guided scheme is designed to transfer the selected caricature style. To improve image synthesis quality, we introduce one feature-pyramid adversarial network (FPAN) to provide multiple feature-level constrains. The comprehensive experiments on various caricature datasets show excellent imitation capabilities of the proposed method.
Power-Efficient Layer Mapping for CNNs on Integrated CPU and GPU Platforms

Heterogeneous MPSoCs consisting of integrated CPUs and GPUs are suitable platforms for embedded applications running on handheld devices such as smart phones. As the handheld devices are mostly powered by battery, the integrated CPU and GPU MPSoC is usually designed with an emphasis on low-power rather than performance. In this paper, we are interested in exploring a power-efficient layer mapping of convolution neural networks (CNNs) deployed on integrated CPU and GPU platforms. Specifically, we investigate the impact of layer mapping of YoloV3-Tiny (i.e., a widely-used CNN in both industry and academia) on system power consumption through numerous experiments on NVIDIA board Jetson TX2. The experimental results indicate that 1) almost all of the convolution layers are not suitable for mapping to CPU, 2) the pooling layer can be mapped to CPU for reducing power consumption, but the mapping may lead to a decrease in inference speed when the layer's output tensor size is large, 3) the detection layer can be mapped to CPU as long as its floating-point operation scale is not too large, and 4) the channel and upsampling layers are both suitable for mapping to CPU. These observations obtained in this study can be further utilized to guide the design of power-efficient layer mapping strategies for integrated CPU and GPU platforms.
Soft Computing Based Evolutionary Multi-Label Classification

Machine Learning (ML) has revolutionized intelligent systems that range from self-driving automobiles, search engines, business/market analysis, fraud detection, network intrusion investigation, and medical diagnosis. Classif... | Find, read and cite all the research you need on Tech Science Press
A Framework to Decide Adaptive Functionalities by Considering User Emotions and the Context

A framework that can adjust and blend its activities or setups to satisfy users' needs continuously in real-time by considering the status of the user and the status of the system based on the present properties can be defined as an adaptive framework. Proposing an adaptive framework which can suggest functions by considering user emotions and context parameters such as location, time and current user activity can be used to improve the User Experience (UX) to enhance the usability of a mobile device. This paper introduces a research approach and an architectural framework to design an adaptive system. A prototype named "Emotional Keyboard" was developed as a proof of concept. It can suggest adaptive functions by detecting emotions from facial expressions and text data while considering the context parameters in real-time. Two user surveys have been conducted to strengthen the background of this research and five prototype evaluations have been carried out.
Advances of Quantum Machine Learning

The basic idea of artificial intelligence and machine learning is that machines have the talent to learn from data, previous experience, and perform the work in future consequences. In the era of the digitalized world which holds big data has long-established machine learning methods consistently with requisite high-quality computational resources in numerous useful and realistic tasks. At the same time, quantum machine learning methods work exponentially faster than their counterparts by making use of quantum mechanics. Through taking advantage of quantum effects such as interference or entanglement, quantum computers can proficiently explain selected issues that are supposed to be tough for traditional machines. Quantum computing is unexpectedly related to that of kernel methods in machine learning. Hence, this chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work of QML.
Dynamic Similarity Search on Integer Sketches

Similarity-preserving hashing is a core technique for fast similarity searches, and it randomly maps data points in a metric space to strings of discrete symbols (i.e., sketches) in the Hamming space. While traditional hashing techniques produce binary sketches, recent ones produce integer sketches for preserving various similarity measures. However, most similarity search methods are designed for binary sketches and inefficient for integer sketches. Moreover, most methods are either inapplicable or inefficient for dynamic datasets, although modern real-world datasets are updated over time. We propose dynamic filter trie (DyFT), a dynamic similarity search method for both binary and integer sketches. An extensive experimental analysis using large real-world datasets shows that DyFT performs superiorly with respect to scalability, time performance, and memory efficiency. For example, on a huge dataset of 216 million data points, DyFT performs a similarity search 6,000 times faster than a state-of-the-art method while reducing to one-thirteenth in memory.
Basic Methodologies Used in NLP Area

For a better understanding of how the neural network is used in natural language processing (NLP), this article lists several basic language models and some latest improvements in order to explain the development of NLP more clearly. In these models, the continue bag of words model (CBOW) and skip-gram model are two simple models with one layer. The CBOW model is used to generate or guess a target word by providing context, while the skip-gram model is used for generated other words by providing one word. For more complex models, the convolution neural network (CNN) and recurrent neural network (RNN) are widely used in the NLP area, and CNN is used for classification tasks, while RNN is more used in the translation area of NLP. Based on the RNN model, a new structure encoder and decoder is designed to solve sequence to sequence problem, and several latest improvements are based on this structure.
Clustering-Guided Incremental Learning of Tasks

Incremental deep learning aims to learn a sequence of tasks while avoiding forgetting their knowledge. One naïve approach using a deep architecture is to increase the capacity of the architecture as the number of tasks increases. However, this is followed by heavy memory consumption and makes the approach not practical. If we attempt to avoid such an issue with a fixed capacity, we encounter another challenging problem called catastrophic forgetting, which leads to a notable degradation of performance on previously learned tasks. To overcome these problems, we propose a clustering-guided incremental learning approach that can mitigate catastrophic forgetting while not increasing the capacity of an architecture. The proposed approach adopts a parameter-splitting strategy to assign a subset of parameters in an architecture for each task to prevent forgetting. It uses a clustering approach to discover the relationship between tasks by storing a few samples per task. When we learn a new task, we utilize the knowledge of the relevant tasks together with the current task to improve performance. This approach could maximize the efficiency of the approach realized in a single fixed architecture. Experimental results with a number of fine-grained datasets show that our method outperforms existing competitors.
Handwritten Digital Recognition Optimization Method based on Deep Learning

At present, human handwritten digital recognition has become a research hotspot. For the MNIST dataset widely used in the field of handwriting recognition, based on the TensorFlow framework in deep learning, the Softmax model and the Convolutional Neural Network (CNN) model are introduced, and the activation function of the CNN model is analyzed. The ReLU function is selected to replace the traditional Sigmoid function and Tanh hyperbolic tangent function to improve the convergence speed in the recognition process. In order to prevent too many parameters from overfitting, Dropout regularization is added. In addition, the above two models are implemented to compare the recognition accuracy of the models, and the optimized CNN model has higher accuracy.
Gesture-Based Model of Mixed Reality Human-Computer Interface

Presently, the quickly evolving concept is of smart city development with the self-reliant capability to face challenges or problems of societies. The expansion or growth of any organization is possible only by improving existing systems and expanding their services to meet all human needs. Technologies, like virtual reality, augmented reality, mixed reality, and human-computer interaction, have grown manifolds. These technologies find space in applications related to manufacturing, education, entertainment, media, IT industry, and the real-estate sector. Out of these technologies, mixed reality and human-computer interaction are evolving at a higher pace. This paper presents the conceptual framework of the gesture-based mixed-reality human-computer interface system for future application development in the above areas. The interaction between people and computers, specifically digital devices, is easily visible. Technology has contributed a lot in making these interactions more and more natural with the integration of new technologies altogether. The presented model employs computer-linked hand gestures and shows the corresponding operation on the head-mounted display (HMD) device.
Probing Gordon Mumma's Studio Heuristic through a Digital Recreation of <i>Mesa</i> (1966)

Gordon Mumma (b. 1935) composed and performed music with live electronics alone or in collaboration with such central figures as John Cage, David Tudor, or Robert Ashley. This activity, closely linked to the American experimentalist tradition, grew out of his first youthful electronic essays on the University of Michigan campus, his work in Milton Cohen’s Space Theater beginning in 1957, and then at the ONCE festival from 1961. It was in Ann Arbor, Michigan that Mumma set up his first electronic music home studio, a pioneering effort that was so innovative that Mumma would publish an article on home studio building in the Journal of the Audio Engineering Society in 1964. This paper traces Mumma’s studio heuristic by reporting on the results of a digital recreation project by the author, in collaboration with Francis Lecavalier (programming) and Ofer Pelz (electronic performance), of one of Mumma’s iconic live electronic works, Mesa (1966), for bandoneon and live electronics. By synthesising information culled from an electronics schematic diagram, two recordings of the work, the composer’s contemporaneous writings and published interviews, but most importantly, an email correspondence stretching over the two years that preceded the performance of Mesa at the Montreal New Musics Festival on 27 February 2017, the ‘DIY’ practices of this early live electronic creation come into focus. Mesa’s apparatus involves the following essential elements: 1) ring modulation 2) a gate matrix that only sends signals beyond a given threshold to the effects modules 3) four input channels consisting of two piezo microphones on either side of the bandoneon as well as 4) a corresponding quadraphonic output to four speakers. The author contends that in an open-ended creation like Mesa, the work’s ontology is coeval with the scope of its technical apparatus.
Video2Report: A Video Database for Automatic Reporting of Medical Consultancy Sessions

The regulation of medical consultations for some countries, such as the Netherlands, dictates the general practitioners to prepare a detailed report for each consultation, for accountability purposes. Automatic report generation during medical consultations can simplify this time-consuming procedure. Action recognition for automatic reporting of medical actions is not a well-researched area, and there are no publicly available medical video databases. We present in this paper Video2Report, the first publicly available medical consultancy video database involving interactions between a general practitioner and one patient. After reviewing the standard medical procedures for general practitioners, we select the most important actions to record, and have an actual medical professional perform the actions and train further actors to create a resource. The actions, as well as the area of investigation during the actions are annotated separately. In this paper, we describe the collection setup, provide several action recognition baselines with OpenPose feature extraction, and make the database, evaluation protocol and all annotations publicly available. The database contains 192 sessions recorded with up to three cameras, with 332 single action clips and 119 multiple action sequences. While the dataset size is too small for end-to-end deep learning, we believe it will be useful for developing approaches to investigate doctor-patient interactions and for medical action recognition.