Reviewerly/exHarmony huggingface dataset:
GBDT-Based Fall Detection with Comprehensive Data from Posture Sensor and Human Skeleton Extraction

Since fall is happening with increasing frequency, it has been a major public health problem in an aging society. There are considerable demands to distinguish fall down events of seniors with the characteristics of accurate detection and real-time alarm. However, some daily activities are erroneously signaled as falls and there are too many false alarms in actual application. In order to resolve this problem, this paper designs and implements a comprehensive fall detection framework on the basis of inertial posture sensors and surveillance cameras. In the proposed system framework, data sources representing behavior characteristics to indicate potential fall are derived from wearable triaxial accelerometers and monitoring videos of surveillance cameras. Moreover, the NB-IoT based communication mode is adopted to transmit wearable sensory data to the Internet for subsequent analysis. Furthermore, a Gradient Boosting Decision Tree (GBDT) classifier-based fall detection algorithm (GBDT-FD in short) with comprehensive data fusion of posture sensor and human video skeleton is proposed to improve detection accuracy. Experimental results verify the good performance of the proposed GBDT-FD algorithm compared to six kinds of existing fall detection algorithms, including SVM-based fall detection, NN-based fall detection, etc. Finally, we implement the proposed integrated systems including wearable posture sensors and monitoring software on the Cloud Server.
Review—Airborne Molecular Contamination: Recent Developments in the Understanding and Minimization for Advanced Semiconductor Device Manufacturing

This review paper focuses on the recent knowledge about airborne molecular contamination (AMC) and its impacts on 300-mm wafer fabrication processes. The adverse impacts on process materials by both organic and inorganic micro-contaminants, and evidence of cross-contamination between processed wafers and the mainstream wafer handling tools, are two areas of discussion for the study. The review also aims to re-frame the industrial guideline for AMC from chemical-based to risk-based approach, which substantiates the potential impacts of individual families of AMCs that have been well-documented. This approach resonates well with the new AMC classification method recently proposed by the International Roadmap of Devices and Systems (IRDS), building on new knowledge of AMC emerged over time with the advances of analytical and testing capability. The review also brings forth the impact of moisture, the mere presence of which can compromise the integrity of device structures. However, moisture is especially detrimental in the presence of other inorganic ions to trigger unwanted reactions with surface materials. Methods developed to minimize the micro-contaminants and moisture during wafer handling and storage are also reviewed.
Speaker recognition based on characteristic spectrograms and an improved self-organizing feature map neural network

Abstract To obtain a speaker’s pronunciation characteristics, a method is proposed based on an idea from bionics, which uses spectrogram statistics to achieve a characteristic spectrogram to give a stable representation of the speaker’s pronunciation from a linear superposition of short-time spectrograms. To deal with the issue of slow network training and recognition speed for speaker recognition systems on resource-constrained devices, based on a traditional SOM neural network, an adaptive clustering self-organizing feature map SOM (AC-SOM) algorithm is proposed. This algorithm automatically adjusts the number of neurons in the competition layer based on the number of speakers to be recognized until the number of clusters matches the number of speakers. A 100-speaker database of characteristic spectrogram samples was built and applied to the proposed AC-SOM model, yielding a maximum training time of only 304 s, with a maximum sample recognition time of less than 28 ms. Comparing to other approaches, the proposed method offers greatly improved training and recognition speed without sacrificing too much recognition accuracy. The promising results suggest that the proposed method satisfies real-time data processing and execution requirements for edge intelligence systems better than other speaker recognition methods.
Concept for the comparison of intralogistics designs with real factory layout using augmented reality, SLAM and marker-based tracking

In the automotive industry, the intralogistics planning faces the problem of matching the planning data with the current conditions in the assembly hall. The large variety of parts leads to a constantly changing production. Based on this, we establish an approach for the comparison using augmented reality (AR) and simultaneous localization and mapping (SLAM). The use of SLAM enables the consistent application of AR in an assembly hall. Based on this, the objective of this article is to visualize 3D objects from the corresponding CAD planning tool in the real factory and thus the comparison of the intralogistics design with the real factory is possible due to AR. Nevertheless, there was a lack of practical implementations in intralogistics and therefore the concept is evaluated by two prototypical solutions. The first one is implemented on an iPhone 7 using SLAM. The second prototype is developed on a HoloLens 2 and is based on a hybrid tracking solution, SLAM and marker tracking.
Generalized Mixtures of Finite Mixtures and Telescoping Sampling

Within a Bayesian framework, a comprehensive investigation of mixtures of finite mixtures (MFMs), i.e., finite mixtures with a prior on the number of components, is performed. This model class has applications in model-based clustering as well as for semi-parametric density estimation and requires suitable prior specifications and inference methods to exploit its full potential. We contribute by considering a generalized class of MFMs where the hyperparameter γK of a symmetric Dirichlet prior on the weight distribution depends on the number of components. We show that this model class may be regarded as a Bayesian non-parametric mixture outside the class of Gibbs-type priors. We emphasize the distinction between the number of components K of a mixture and the number of clusters K+, i.e., the number of filled components given the data. In the MFM model, K+ is a random variable and its prior depends on the prior on K and on the hyperparameter γK. We employ a flexible prior distribution for the number of components K and derive the corresponding prior on the number of clusters K+ for generalized MFMs. For posterior inference we propose the novel telescoping sampler which allows Bayesian inference for mixtures with arbitrary component distributions without resorting to reversible jump Markov chain Monte Carlo (MCMC) methods. The telescoping sampler explicitly samples the number of components, but otherwise requires only the usual MCMC steps of a finite mixture model. The ease of its application using different component distributions is demonstrated on several data sets.
High-quality 3D shape measurement by kernel-optimized high sinusoidal similarity dither patterns

The error-diffusion dithering technique is widely used in three-dimensional (3D) measurement. The sinusoid similarity of dithering fringe patterns greatly affects measurement accuracy, while the traditional dithering methods lack this quality. In this paper, we present a kernel-optimized dithering technique based on the Floyd–Steinberg dithering method and establish an optimization strategy considering both intensity and phase aspects to generate high-quality sinusoidal fringes for 3D measurement by utilizing the binary defocusing technique. Both simulations and experiments demonstrate that the proposed method could improve the performance of 3D measurement effectively under different defocusing amounts, especially in narrow strips.
ConvBERT: Improving BERT with Span-based Dynamic Convolution

Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers large memory footprint and computation cost. Although all its attention heads query on the whole input sequence for generating the attention map from a global perspective, we observe some heads only need to learn local dependencies, which means the existence of computation redundancy. We therefore propose a novel span-based dynamic convolution to replace these self-attention heads to directly model local dependencies. The novel convolution heads, together with the rest self-attention heads, form a new mixed attention block that is more efficient at both global and local context learning. We equip BERT with this mixed attention design and build a ConvBERT model. Experiments have shown that ConvBERT significantly outperforms BERT and its variants in various downstream tasks, with lower training cost and fewer model parameters. Remarkably, ConvBERTbase model achieves 86.4 GLUE score, 0.7 higher than ELECTRAbase, while using less than 1/4 training cost. Code and pre-trained models will be released.
Convergence Analysis of Inexact Randomized Iterative Methods

In this paper we present a convergence rate analysis of inexact variants of several randomized iterative methods for solving three closely related problems: a convex stochastic quadratic optimization problem, a best approximation problem, and its dual, a concave quadratic maximization problem. Among the methods studied are stochastic gradient descent, stochastic Newton, stochastic proximal point, and stochastic subspace ascent. A common feature of these methods is that in their update rule a certain subproblem needs to be solved exactly. We relax this requirement by allowing for the subproblem to be solved inexactly. We provide iteration complexity results under several assumptions on the inexactness error. Inexact variants of many popular and some more exotic methods, including randomized block Kaczmarz, Gaussian block Kaczmarz, and randomized block coordinate descent, can be cast as special cases. Numerical experiments demonstrate the benefits of allowing inexactness.
Comparative Analysis Spline Methods in Digital Processing of Signals

In this article, the Grebennikov cubic spline, the Ryabenky cubic spline, and we offer the local interpolation cubic spline models are selected.The construction details of the models were given and the processes of approximating the functions were performed using selected local cubic splines.We can also say that the local interpolation cubic spline models considered in this study provide high accuracy in digital processing of signals, which helps experts to make the right decision as a result of digital processing of signals.For instance, the initial values of the gastroentrological signal were calculated after that they digitally processed, and the error results were taken.Approximation of the considered models and digital processing of the gastroenterological signal by getting error results were specific analyzed comparatively based on numerical and graphical comparisons.
Instance Reduction for Avoiding Overfitting in Decision Trees

Abstract Decision trees learning is one of the most practical classification methods in machine learning, which is used for approximating discrete-valued target functions. However, they may overfit the training data, which limits their ability to generalize to unseen instances. In this study, we investigated the use of instance reduction techniques to smooth the decision boundaries before training the decision trees. Noise filters such as ENN, RENN, and ALLKNN remove noisy instances while DROP3 and DROP5 may remove genuine instances. Extensive empirical experiments were conducted on 13 benchmark datasets from UCI machine learning repository with and without intentionally introduced noise. Empirical results show that eliminating border instances improves the classification accuracy of decision trees and reduces the tree size, which reduces the training and classification times. In datasets without intentionally added noise, applying noise filters without the use of the built-in Reduced Error Pruning gave the best classification accuracy. ENN, RENN, and ALLKNN outperformed decision trees learning without pruning in 9, 9, and 8 out of 13 datasets, respectively. The datasets reduced using ENN and RENN without built-in pruning were more effective when noise was intentionally introduced in different ratios.
Construction of Mathematical Model of DC Servo Motor Mechanism with PID controller for Electric Wheel Chair Arrangement

Low-level and medium-level leg injury patients can be operated wheelchair independently in the clinical region. The construction of an electric wheelchair is one of the solutions to operate electric wheelchair by the patients themselves. The motor is an essential part of an electrical wheelchair for driving from one place to another place. The response of the system is very important for the optimization of the system. The existing methods fail in gradual sensitivity during motion and lack of response time to the user. This article is consist of a design for optimizing the existing DC motor transfer function for the smart wheelchair. The perfect angular tuning of the derivative controller provides a better executing time for our proposed model. The smoother responses from the smart wheelchair are obtaining by the dynamic response of closed-loop control. The design of DC motors is to drive smart wheelchair as per the needs. Besides, the construction of a mathematical model for proposing a system involves the DC motor drive to the smart wheelchair arrangement. The proposed model gives independent mobility of smart wheelchair with less response time and better sensitivity. Here, the mathematical model provides the details of immediate response to the user and less execution time of the complex system process. Finally, the authors discussed future improvements to the current DC motor design in the proposed system.
Cooperatively Improving Data Center Energy Efficiency Based on Multi-Agent Deep Reinforcement Learning

The problem of high power consumption in data centers is becoming more and more prominent. In order to improve the energy efficiency of data centers, cooperatively optimizing the energy of IT systems and cooling systems has become an effective way. In this paper, a model-free deep reinforcement learning (DRL)-based joint optimization method MAD3C is developed to overcome the high-dimensional state and action space problems of the data center energy optimization. A hybrid AC-DDPG cooperative multi-agent framework is devised for the improvement of the cooperation between the IT and cooling systems for further energy efficiency improvement. In the framework, a scheduling baseline comparison method is presented to enhance the stability of the framework. Meanwhile, an adaptive score is designed for the architecture in consideration of multi-dimensional resources and resource utilization improvement. Experiments show that our proposed approach can effectively reduce energy for data centers through the cooperative optimization while guaranteeing training stability and improving resource utilization.
A Semantic Approach for Big Data Exploration in Industry 4.0

The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.
Enriching Non-Autoregressive Transformer with Syntactic and Semantic Structures for Neural Machine Translation

The non-autoregressive models have boosted the efficiency of neural machine translation through parallelized decoding at the cost of effectiveness, when comparing with the autoregressive counterparts.In this paper, we claim that the syntactic and semantic structures among natural language are critical for non-autoregressive machine translation and can further improve the performance.However, these structures are rarely considered in existing non-autoregressive models.Inspired by this intuition, we propose to incorporate the explicit syntactic and semantic structures of languages into a non-autoregressive Transformer, for the task of neural machine translation.Moreover, we also consider the intermediate latent alignment within target sentences to better learn the long-term token dependencies.Experimental results on two real-world datasets (i.e., WMT14 En-De and WMT16 En-Ro) show that our model achieves a significantly faster speed, as well as keeps the translation quality when compared with several stateof-the-art non-autoregressive models.Table 1: A motivating example on WMT14 En→De dataset.English with POS|NER and its corresponding German translation with POS|NER.The Blue labels show the same tags, while the Red labels show the different tags in two languages. EN:A republican strategy to counter the rel-election of Obama .
Analyzing entropic uncertainty bound in two qubits coupled to a spin environment

Abstract By assuming a general pure state and a mixed Werner state as two initial states, we investigate the time evolution of entropic uncertainty bound in the presence of quantum memory for a two-qubit system interacting with an XY spin chain regarded as an environment constrained to carry the energy current. We obtain an explicit relationship between the entropic uncertainty bound and decoherence factor, and analytically study the dynamical process of entropic uncertainty bound for both weak and strong-coupling cases in two initial states. Our results reveal that the evolution of entropic uncertainty bound depends not only on the energy current, the system-environment couplings and the anisotropy parameter but also on the size of the environmental XY spin chain. Interestingly, we find that the entropic uncertainty bound can be suppressed by reducing the energy current on the environmental spin chain in a weak-coupling region, however, it has no sensitivity to the energy current in a strong-coupling region.
Researcher attitudes toward data sharing in public data repositories: a meta-evaluation of studies on researcher data sharing

Purpose The purpose of this paper is to report a study of how research literature addresses researchers' attitudes toward data repository use. In particular, the authors are interested in how the term data sharing is defined, how data repository use is reported and whether there is need for greater clarity and specificity of terminology. Design/methodology/approach To study how the literature addresses researcher data repository use, relevant studies were identified by searching Library Information Science and Technology Abstracts, Library and Information Science Source, Thomas Reuters' Web of Science Core Collection and Scopus. A total of 62 studies were identified for inclusion in this meta-evaluation. Findings The study shows a need for greater clarity and consistency in the use of the term data sharing in future studies to better understand the phenomenon and allow for cross-study comparisons. Furthermore, most studies did not address data repository use specifically. In most analyzed studies, it was not possible to segregate results relating to sharing via public data repositories from other types of sharing. When sharing in public repositories was mentioned, the prevalence of repository use varied significantly. Originality/value Researchers' data sharing is of great interest to library and information science research and practice to inform academic libraries that are implementing data services to support these researchers. This study explores how the literature approaches this issue, especially the use of data repositories, the use of which is strongly encouraged. This paper identifies the potential for additional study focused on this area.
An Attention Enhanced Spatial–Temporal Graph Convolutional LSTM Network for Action Recognition in Karate

With the increasing popularity of artificial intelligence applications, artificial intelligence technology has begun to be applied in competitive sports. These applications have promoted the improvement of athletes’ competitive ability, as well as the fitness of the masses. Human action recognition technology, based on deep learning, has gradually been applied to the analysis of the technical actions of competitive sports athletes, as well as the analysis of tactics. In this paper, a new graph convolution model is proposed. Delaunay’s partitioning algorithm was used to construct a new spatiotemporal topology which can effectively obtain the structural information and spatiotemporal features of athletes’ technical actions. At the same time, the attention mechanism was integrated into the model, and different weight coefficients were assigned to the joints, which significantly improved the accuracy of technical action recognition. First, a comparison between the current state-of-the-art methods was undertaken using the general datasets of Kinect and NTU-RGB + D. The performance of the new algorithm model was slightly improved in comparison to the general dataset. Then, the performance of our algorithm was compared with spatial temporal graph convolutional networks (ST-GCN) for the karate technique action dataset. We found that the accuracy of our algorithm was significantly improved.
EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search

Neural Architecture Search (NAS) has shown excellent results in designing architectures for computer vision problems. NAS alleviates the need for human-defined settings by automating architecture design and engineering. However, NAS methods tend to be slow, as they require large amounts of GPU computation. This bottleneck is mainly due to the performance estimation strategy, which requires the evaluation of the generated architectures, mainly through training, to update the sampler method. In this paper, we propose EPE-NAS, an efficient performance estimation strategy, that mitigates the problem of evaluating networks, by scoring untrained networks and correlating them with their trained performance. We perform this process by looking at intra and inter-class correlations of an untrained network. We show that EPE-NAS can produce a robust correlation and that by incorporating it into a simple random sampling strategy, we are able to search for competitive networks, without requiring any training, in a matter of seconds using a single GPU. Moreover, EPE-NAS is agnostic to the search method, as it focuses on evaluating untrained networks, making it easy to integrate into almost any NAS method.