Reviewerly/exHarmony huggingface dataset:
IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes

Aerobics is the fusion of gymnastics, dance, and music; it is a body of a sports project, along with the development of the society. The growing demand for aerobics inevitably increases the demand for aerobics coach and teacher and has opened elective aerobics class which is an effective way of cultivating professional talents relevant to aerobics. Aerobics has extended fixed teaching mode and cannot conform to the development of the times. The motion prediction of aerobics athletes is a new set of teaching aid. In this paper, a motion prediction model of aerobics athletes is built based on the wearable inertial sensor of the Internet of Things and the bidirectional long short term memory (BiLSTM) network. Firstly, a wireless sensor network based on ZigBee was designed and implemented to collect the posture data of aerobics athletes. The inertial sensors were used for data collection and transmission of the data to the cloud platform through Ethernet. Then, the movement of aerobics athletes is recognized and predicted by the BiLSTM network. Based on the BiLSTM network and the attention mechanism, this paper proposes to solve the problem of low classification accuracy caused by the traditional method of directly summing and averaging the updated output vectors corresponding to each moment of the BiLSTM layer. The simulation experiment is also carried out in this paper. The experimental results show that the proposed model can recognize aerobics effectively.
Specific Emitter Identification Based on Multi-Domain Feature Fusion and Integrated Learning

Specific Emitter Identification (SEI) is a key research problem in the field of information countermeasures. It is one of the key technologies required to be solved urgently in the target reconnaissance system. It has the ability to distinguish between different individual radiation sources according to the varying individual characteristics of the emitter hardware within the transmitted signals. In response to the lack of scarcity among labeled samples in specific emitter identification, this paper proposes a method combining multi-domain feature fusion and integrated learning (MDFFIL). First, the received signal is preprocessed to obtain segmented time domain signal samples. Then, the signal is converted to time–frequency distribution using wavelet transform. Afterwards, an integrated learning two-stage recognition classification method is designed to extract data features of 1D time domain signals and 2D time–frequency distribution signals using the symmetry network structures of CVResNet and ResNet. Finally, fused features are fed into the complex-valued residual network classifier to obtain the final classification results. We demonstrate through the analysis results of the measured data that the proposed method has a higher accuracy as compared with the classical feature extraction method, and that this can improve the identification of communication radiation sources with fewer labeled samples.
Blockchain-based green big data visualization: BGbV

Abstract The progression of Internet of Things (IoT) has resulted in generation of huge amount of data. Effective handling and analysis of such big volumes of data proposes a crucial challenge. Existing cloud-based frameworks of Big Data visualization are rising costs for servers, equipment, and energy consumption. There is a need for a green solution targeting lesser cost and energy consumption with tamper-proof record-keeping, storage, and interactive visualization with only demanded data. We have proposed a Blockchain-based Green big data Visualization (BGbV) solution using Hyperledger Sawtooth for optimum utilization of organization resources. BGbV will support current distributed data visualization platforms and guarantee benefits like security and data availability with lesser storage costs. It helps reduce costs by utilizing small resources that are already available and consume less energy, making it an environmentally friendly solution.
Advances in Mining Heterogeneous Healthcare Data

Thanks to the explosion of heterogeneous healthcare data and advanced machine learning and data mining techniques, specifically deep learning methods, we now have an opportunity to make difference in healthcare. In this tutorial, we will present state-of-the-art deep learning methods and their real-world applications, specifically focusing on exploring the unique characteristics of different types of healthcare data. The first half will be spent on introducing recent advances in mining structured healthcare data, including computational phenotyping, disease early detection/risk prediction and treatment recommendation. In the second half, we will focus on challenges specific to the unstructured healthcare data, and introduce advanced deep learning methods in automated ICD coding, understandable medical language translation, clinical trial mining, and medical report generation. This tutorial is intended for students, engineers and researchers who are interested in applying deep learning methods to healthcare, and prerequisite knowledge will be minimal. The tutorial will be concluded with open problems and a Q&A session.
Intelligent Vision-Enabled Detection of Water-Surface Targets for Video Surveillance in Maritime Transportation

The timely, automatic, and accurate detection of water-surface targets has received significant attention in intelligent vision-enabled maritime transportation systems. The reliable detection results are also beneficial for water quality monitoring in practical applications. However, the visual image quality is often inevitably degraded due to the poor weather conditions, potentially leading to unsatisfactory target detection results. The degraded images could be restored using state-of-the-art visibility enhancement methods. It is still difficult to generate high-quality detection performance due to the unavoidable loss of details in restored images. To alleviate these limitations, we first investigate the influences of visibility enhancement methods on detection results and then propose a neural network-empowered water-surface target detection framework. A data augmentation strategy, which synthetically simulates the degraded images under different weather conditions, is further presented to promote the generalization and feature representation abilities of our network. The proposed detection performance has the capacity of accurately detecting the water-surface targets under different adverse imaging conditions, e.g., haze, low-lightness, and rain. Experimental results on both synthetic and realistic scenarios have illustrated the effectiveness of the proposed framework in terms of detection accuracy and efficacy.
Analysis of a Mathematical Model for Drilling System With Reverse Air Circulation by Using the ANN-BHCS Technique

In the present article, mathematical analysis of drilling system with reverse air circulation is presented by a novel hybrid technique of feed forward artificial neural network (ANN) and biogeography based cuckoo search (BHCS) algorithm. A series solution is constructed with unknown weights for the differential equations representing the drilling problem. Five numerical cases are analysed to show the effectiveness of our method for solution of differential equations. From the experimental outcomes, it is investigated that our soft computing procedure has better rate of convergence to the best solution as compared to state-of-the-art techniques. From solution graphs, it is established that our results are in agreement with the reference solutions. It is noted that our technique is easy to implement and can be used for any mathematical model containing nonlinear differential equations. The graphical abstract of this article is given in Figure (1).
Determinants of Sustainable Open Innovations—A Firm-Level Capacity Analysis

Institutional constraints impede firms’ open innovation. They have been a challenge, obstructing growth and sustainable development. Research on open innovation has shown that the quality of institutions essentially affects innovation in firms. Hence, prior research has made efforts to incorporate the quality of institutions into open innovation analysis. We can use a series of analyses to examine the impact of corruption, the tax system, and other indicators on firm innovation performance. However, developing economies, such as countries in sub-Saharan Africa, represent a specific group of countries that have long been perceived as those mostly deficient in the rule of law, with poor regulatory quality and a great deal of corruption. In these countries, it is also possible to see a lower number of studies, as the inability to obtain quality data to perform empirical analyses can often limit researchers. Nevertheless, employing data from the World Bank’s 2019 Enterprise Survey, this research aimed at exploring the determinants of sustainable open innovation as well as the effect of institutional quality on firms’ capacity utilization and process innovation through a PLS structural equation model analysis. Our research showed interesting findings, such as the fact that the quality of institutions significantly affects firms’ use of OI instruments and capacity utilization. This research also provides for the novelty of the analysis of capacity utilization in an open innovation analysis. The results support the hypotheses that low institutional quality negatively affects firms’ implementation of inbound open innovation instruments, and that there is a strong and positive effect of low institutional quality on firms’ capacity utilization. In addition, we confirm the premise that firms’ implementation of inbound open innovation instruments has a positive and significant influence on firms’ process innovation.
BWM-SAW: A new hybrid MCDM technique for modeling of chromite potential in the Birjand district, east of Iran

The Birjand chromite district, eastern Iran, is a part of the Birjand ophiolite complex. Although there is no known chromite mineralization in this district, its favorable geological setting suggests the presence of chromite endowment. Therefore, this study adopted a novel hybrid multiple-criteria decision making (MCDM) technique called BWM-SAW to mineral potential modeling (MPM) of chromite deposits in the Birjand district. For this, the authors developed three exploration evidence maps, including (1) proximity to the host rock, (2) fault density, and (3) multi-element geochemical signature. In addition, a further AHP-TOPSIS potential model was merely generated to check the adequacy of the developed BWM-SAW model. Also in this study, for the purpose of comparison and further investigation, we applied the above hybrid MCDM techniques to generate mineral potential map in the Dolatabad district hosting numerous chromite deposits. For this, we utilized three evidential maps previously generated and described by Roshanravan (2020). The results of this study manifest that the BWM-SAW technique to MPM outperformed the AHP-TOPSIS potential model and the previous continuous (i.e., data-driven index overlay, geometric average and fuzzy gamma) models of Roshanravan (2020).
Integrating artificial intelligence and analytics in smart grids: a systematic literature review

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.,A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?,The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.,Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification

End-to-end intent classification using speech has numerous advantages compared to the conventional pipeline approach using automatic speech recognition (ASR), followed by natural language processing modules. It attempts to predict intent from speech without using an intermediate ASR module. However, such end-to-end framework suffers from the unavailability of large speech resources with higher acoustic variation in spoken language understanding. In this work, we exploit the scope of the transformer distillation method that is specifically designed for knowledge distillation from a transformer based language model to a transformer based speech model. In this regard, we leverage the reliable and widely used bidirectional encoder representations from transformers (BERT) model as a language model and transfer the knowledge to build an acoustic model for intent classification using the speech. In particular, a multilevel transformer based teacher-student model is designed, and knowledge distillation is performed across attention and hidden sub-layers of different transformer layers of the student and teacher models. We achieve an intent classification accuracy of 99.10% and 88.79% for Fluent speech corpus and ATIS database, respectively. Further, the proposed method demonstrates better performance and robustness in acoustically degraded condition compared to the baseline method.
Identifying Conflict Escalation and Primates by Using Ensemble X-Vectors and Fisher Vector Features

Computational paralinguistics is concerned with the automatic identification of non-verbal information in human speech.The Interspeech ComParE challenge features new paralinguistic tasks each year; this time, among others, a cross-corpus conflict escalation task and the identification of primates based solely on audio are the actual problems set.In our entry to ComParE 2021, we utilize x-vectors and Fisher vectors as features.To improve the robustness of the predictions, we also experiment with building an ensemble of classifiers from the x-vectors.Lastly, we exploit the fact that the Escalation Sub-Challenge is a conflict detection task, and incorporate the SSPNet Conflict Corpus in our training workflow.Using these approaches, at the time of writing, we had already surpassed the official Challenge baselines on both tasks, which demonstrates the efficiency of the employed techniques.
A Particle Swarm Algorithm Based on a Multi-Stage Search Strategy

Particle swarm optimization (PSO) has the disadvantages of easily getting trapped in local optima and a low search accuracy. Scores of approaches have been used to improve the diversity, search accuracy, and results of PSO, but the balance between exploration and exploitation remains sub-optimal. Many scholars have divided the population into multiple sub-populations with the aim of managing it in space. In this paper, a multi-stage search strategy that is dominated by mutual repulsion among particles and supplemented by attraction was proposed to control the traits of the population. From the angle of iteration time, the algorithm was able to adequately enhance the entropy of the population under the premise of satisfying the convergence, creating a more balanced search process. The study acquired satisfactory results from the CEC2017 test function by improving the standard PSO and improved PSO.
Constructing a Design Space from a Collection of Design Examples

Previous design cases and examples are important resources in design, most famously conceptualized by Donald Schön as the designer’s repertoire. We propose a way of approaching and gathering such examples, and introduce a tool for constructing and analyzing the design spaces these collections form. Each example is deconstructed in terms of aspects of concern and options for how these aspects can be implemented. The example is then represented within a design space schema comprising aspect-option sets. This makes it possible to conceptualize a design space of aspect-option sets reflecting the focal collection, and to position individual examples within that design space. We demonstrate our approach through an analysis of 54 media architecture installations listed on the Media Architecture Biennale award nomination lists from 2012, 2014, 2016, and 2018. We discuss how this approach compares to similar approaches, and detail the knowledge building process that it enables, which includes two main elements: 1) developing a language to describe the examples in terms of the aspect-option sets; and 2) exploring the design space through filtering the design space.
Meta-Attack: Class-agnostic and Model-agnostic Physical Adversarial Attack

Modern deep neural networks are often vulnerable to adversarial examples. Most exist attack methods focus on crafting adversarial examples in the digital domain, while only limited works study physical adversarial attack. However, it is more challenging to generate effective adversarial examples in the physical world due to many uncontrollable physical dynamics. Most current physical attack methods aim to generate robust physical adversarial examples by simulating all possible physical dynamics. When attacking new images or new DNN models, they require expensive manually efforts for simulating physical dynamics and considerable time for iteratively optimizing for each image. To tackle these issues, we propose a class-agnostic and model-agnostic physical adversarial attack model (Meta-Attack), which is able to not only generate robust physical adversarial examples by simulating color and shape distortions, but also generalize to attacking novel images and novel DNN models by accessing a few digital and physical images. To the best of our knowledge, this is the first work to formulate the physical attack as a few-shot learning problem. Here, the training task is redefined as the composition of a support set, a query set, and a target DNN model. Under the few-shot setting, we design a novel class-agnostic and model-agnostic meta-learning algorithm to enhance the generalization ability of our method. Extensive experimental results on two benchmark datasets with four challenging experimental settings verify the superior robustness and generalization of our method by comparing to state-of-the-art physical attack methods.
Attention Based Context Boosted Cyberbullying Detection in Social Media

Cyberbullying detection is a rising research topic due to its paramount impact on social media users, especially youngsters and adolescents. While there has been an enormous amount of progress in utilising efficient machine learning and NLP techniques for tackling this task, recent methods have not fully addressed contextualizing the textual content to the highest possible extent. The textual content of social media posts and comments is normally long, noisy and mixed with lots of irrelevant tokens and characters, and therefore utilizing an attention-based approach that can focus on more relevant parts of the text can be quite pertinent. Moreover, social media information is normally multi-modal in nature and may contain various metadata and contextual information that can contribute to enhancing the Cyberbullying prediction system. In this research, we propose a novel machine learning method that, (i) fine tunes a variant of BERT, a deep attention-based language model, which is capable of detecting patterns in long and noisy bodies of text; (ii)~extracts contextual information from multiple sources including metadata information, images and even external knowledge sources and uses these features to complement the learner model; and (iii) efficiently combines textual and contextual features using boosting and a wide-and-deep architecture. We compare our proposed method with state-of-the-art methods and highlight how our approach significantly outperforming the quality of results compared to those methods in most cases.
Ontologies as Knowledge Organization Systems

This contribution presents the principal features of ontologies, drawing special attention to the comparison between ontologies and the different kinds of know­ledge organization systems (KOS). The focus is on the semantic richness exhibited by ontologies, which allows the creation of a great number of relationships between terms. That establishes ontologies as the most evolved type of KOS. The concepts of “conceptualization” and “formalization” and the key components of ontologies are described and discussed, along with upper and domain ontologies and special typologies, such as bibliographical ontologies and biomedical ontologies. The use of ontologies in the digital libraries environment, where they have replaced thesauri for query expansion in searching, and the role they are playing in the Semantic Web, especially for semantic interoperability, are sketched.
A Systematic Review of Privacy Preserving Healthcare Data Sharing on Blockchain

Sharing the electronic health data helps to increase the accuracy of the diagnoses and to improve the quality of health services. This shared data can also be used in medical research and can reduce medical costs. However, health data are fragmented across decentralized hospitals, this prevents data sharing and puts patients’ privacy at risks. In recent years, blockchain has revealed solutions that make life easier in many areas thanks to its distributed, safe and immutable structure. There are many blockchain-based studies in the literature on providing data privacy and sharing in different areas. In some studies, blockchain has been used with technologies such as cloud computing and cryptology. In the field of healthcare blockchain-based solutions are offered for the management and sharing of Electronic health records. In these solutions, private and consortium blockchain types are generally preferred and Public Key Infrastructure (PKI) and encryption are used for data privacy. Within the scope of this study, blockchain-based studies on the privacy preserving data sharing of health data were examined. In this paper, information about the studies in the literature and potential issues that can be studied in the future were discussed. In addition, information about current blockchain technologies such as smart contracts and PKI is also given.