Reviewerly/exHarmony huggingface dataset:
LoRa-Based Smart IoT Application for Smart City: An Example of Human Posture Detection

Scientists have explored the human body for hundreds of years, and yet more relationships between the behaviors and health are still to be discovered. With the development of data mining, artificial intelligence technology, and human posture detection, it is much more possible to figure out how behaviors and movements influence people’s health and life and how to adjust the relationship between work and rest, which is needed urgently for modern people against this high-speed lifestyle. Using smart technology and daily behaviors to supervise or predict people’s health is a key part of a smart city. In a smart city, these applications involve large groups and high-frequency use, so the system must have low energy consumption, a portable system, and a low cost for long-term detection. To meet these requirements, this paper proposes a posture recognition method based on multisensor and using LoRa technology to build a long-term posture detection system. LoRa WAN technology has the advantages of low cost and long transmission distances. Combining the LoRa transmitting module and sensors, this paper designs wearable clothing to make people comfortable in any given posture. Aiming at LoRa’s low transmitting frequency and small size of data transmission, this paper proposes a multiprocessing method, including data denoising, data enlarging based on sliding windows, feature extraction, and feature selection using Random Forest, to make 4 values retain the most information about 125 data from 9 axes of sensors. The result shows an accuracy of 99.38% of extracted features and 95.06% of selected features with the training of 3239 groups of datasets. To verify the performance of the proposed algorithm, three testers created 500 groups of datasets and the results showed good performance. Hence, due to the energy sustainability of LoRa and the accuracy of recognition, this proposed posture recognition using multisensor and LoRa can work well when facing long-term detection and LoRa fits smart city well when facing long-distance transmission.
Combinatorial Methods for Explainable AI

This short paper introduces an approach to producing explanations or justifications of decisions made by artificial intelligence and machine learning (AI/ML) systems, using methods derived from fault location in combinatorial testing. We use a conceptually simple scheme to make it easy to justify classification decisions: identifying combinations of features that are present in members of the identified class and absent or rare in non-members. The method has been implemented in a prototype tool, and examples of its application are given.
SIMBA: An Efficient Simulator for Blockchain Applications

Predicting the performance of a blockchain application during the design phase is difficult and evaluation after it is built could be expensive. The ability to simulate a blockchain network during the design stage in order to evaluate it is therefore a necessity. In this paper, we present a simulator for blockchain applications, called SIMBA (SIMulator for Blockchain Applications). SIMBA extends an existing simulator by adding the Merkle tree feature to blockchain nodes to improve efficiency and allowing more realistic evaluations not possible with the base tool to be performed. Results of our experiments show that the inclusion of Merkle trees has a high impact of up to 30 times reduction in the verification time of block transactions without an impact on block propagation delay. Since block verification is a critical part of the computational load of nodes on the network, this performance improvement significantly affects the overall performance of each node and consequently the entire network.
Is Artificial Intelligence Ready for Standardization?

Many standards development organizations worldwide work on norms for Artificial Intelligence (AI) technologies and AI related processes. At the same time, many governments and companies massively invest in research on AI. It may be asked if AI research has already produced mature technologies and if this field is ready for standardization. This article looks at today’s situation of AI in the context of needs for standardization. The International Organization for Standardization (ISO) runs a standardization project on AI since 2018. We give an up-to-date overview of the status of this work. While a fully comprehensive survey is not the objective, we describe a number of important aspects of the standardization work in AI. In addition, concrete examples for possible items of AI standards are described and discussed. From a scientific point of view, there are many open research questions that make AI standardization appear to be premature. However, our analysis shows that there is a sound basis for starting to work on AI standardization as being undertaken by ISO and other organizations.
Discrete-Time Advanced Zeroing Neurodynamic Algorithm Applied to Future Equality-Constrained Nonlinear Optimization With Various Noises

This research first proposes the general expression of Zhang et al. discretization (ZeaD) formulas to provide an effective general framework for finding various ZeaD formulas by the idea of high-order derivative simultaneous elimination. Then, to solve the problem of future equality-constrained nonlinear optimization (ECNO) with various noises, a specific ZeaD formula originating from the general ZeaD formula is further studied for the discretization of a noise-perturbed continuous-time advanced zeroing neurodynamic model. Subsequently, the resulting noise-perturbed discrete-time advanced zeroing neurodynamic (NP-DTAZN) algorithm is proposed for the real-time solution to the future ECNO problem with various noises suppressed simultaneously. Moreover, theoretical and numerical results are presented to show the convergence and precision of the proposed NP-DTAZN algorithm in the perturbation of various noises. Finally, comparative numerical and physical experiments based on a Kinova JACO2 robot manipulator are conducted to further substantiate the efficacy, superiority, and practicability of the proposed NP-DTAZN algorithm for solving the future ECNO problem with various noises.
A dictionary indexing approach for EBSD

Abstract Dictionary indexing is a new approach to the indexing of electron backscatter diffraction (EBSD) patterns. In this contribution, we will describe the underlying forward model as well as the indexing approach that uses a similarity metric between experimental and simulated patterns instead of feature extraction. Then we provide a number of example applications drawn from the materials and geological communities.
The Art of CPU-Pinning: Evaluating and Improving the Performance of Virtualization and Containerization Platforms

Cloud providers offer a variety of execution platforms in form of bare-metal, VM, and containers. However, due to the pros and cons of each execution platform, choosing the appropriate platform for a specific cloud-based application has become a challenge for solution architects. The possibility to combine these platforms (e.g., deploying containers within VMs) offers new capacities that makes the challenge even further complicated. However, there is a little study in the literature on the pros and cons of deploying different application types on various execution platforms. In particular, evaluation of diverse hardware configurations and different CPU provisioning methods, such as CPU pinning, have not been sufficiently studied in the literature. In this work, the performance overhead of container, VM, and bare-metal execution platforms are measured and analyzed for four categories of real-world applications, namely video processing, parallel processing (MPI), web processing, and No-SQL, respectively representing CPU intensive, parallel processing, and two IO intensive processes. Our analyses reveal a set of interesting and sometimes counterintuitive findings that can be used as best practices by the solution architects to efficiently deploy cloud-based applications. Here are some notable mentions: (A) Under specific circumstances, containers can impose a higher overhead than VMs; (B) Containers on top of VMs can mitigate the overhead of VMs for certain applications; (C) Containers with a large number of cores impose a lower overhead than those with a few cores.
Predictive Models in Software Engineering: Challenges and Opportunities

Predictive models are one of the most important techniques that are widely applied in many areas of software engineering. There have been a large number of primary studies that apply predictive models and that present well-performed studies in various research domains, including software requirements, software design and development, testing and debugging, and software maintenance. This article is a first attempt to systematically organize knowledge in this area by surveying a body of 421 papers on predictive models published between 2009 and 2020. We describe the key models and approaches used, classify the different models, summarize the range of key application areas, and analyze research results. Based on our findings, we also propose a set of current challenges that still need to be addressed in future work and provide a proposed research road map for these opportunities.
A kernel extreme learning machine-based neural network to forecast very short-term power output of an on-grid photovoltaic power plant

It is well-known that solar power depends on many complex parameters such as humidity, radiation, temperature, dust and wind speed. In order to cope with these complex structures and to provide an accurate forecast, the development of reliable and effective forecasting methodology is very significant. In this study, an Artificial Neural Network (ANN)-based system has been developed to forecast very short-term (2 to 4-h) power output of a grid tied Photovoltaic Power Plant (PVPP). An algorithm called Extreme Learning Machine (ELM) has gained increasing interest through its extremely fast learning and good generalization capability. The Kernel Extreme Learning Machine (KELM) which is the improved version of the ELM is proposed to develop a very short-term PVPP power forecast system. The most important feature of KELM is that it has less adjustable parameters and better generalization ability when compared with classical ELM. Experimental studies have been carried out on a grid tied PVPP that has 1 (MW) installed power capacity. The inputs of the KELM-based forecast system are selected as solar power, humidity, radiation and temperature. All data are divided into four parts to analyze the effect of seasons on the performance of the proposed forecast system. The comparison studies are carried out to clearly observe the forecast ability and performance of the KELM. From the experimental studies of the winter season which has not symmetric diurnal mean power curve, Correlation Coefficients (R) values of KELM are calculated as 0.896 for 2-h ahead, 0.865 for 3-h ahead, 0.833 for 4-h ahead. Those of ELM and (Levenberg Marquardt) LM are 0.866–0.812 for 2-h ahead, 0.831–0.669 for 3-h ahead, 0.799–0.502 for 4-h ahead, respectively. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values of KELM for winter season are 12.08%-6.14% for 2-h ahead, 14.06%-7.43% for 3-h ahead, 15.33%-7.74% for 4-h ahead, respectively. While these values of ELM are obtained as 13.82%-7.05% for 2-h ahead, 15.52%-8.77% for 3-h ahead, 17.63%-9.33% for 4-h ahead. The RMSE and MAE values of LM are 17.90%-9.14% for 2-h ahead, 20.04%-9.85% for 3-h ahead, 21.22%-10.68% for 4-h ahead. According to the obtained results, it is clearly seen that KELM provides a more powerful and reliable forecasting performance.
Automatic Detection of Usability Problem Encounters in Think-aloud Sessions

Think-aloud protocols are a highly valued usability testing method for identifying usability problems. Despite the value of conducting think-aloud usability test sessions, analyzing think-aloud sessions is often time-consuming and labor-intensive. Consequently, previous research has urged the community to develop techniques to support fast-paced analysis. In this work, we took the first step to design and evaluate machine learning (ML) models to automatically detect usability problem encounters based on users’ verbalization and speech features in think-aloud sessions. Inspired by recent research that shows subtle patterns in users’ verbalizations and speech features tend to occur when they encounter problems, we examined whether these patterns can be utilized to improve the automatic detection of usability problems. We first conducted and recorded think-aloud sessions and then examined the effect of different input features, ML models, test products, and users on usability problem encounters detection. Our work uncovers several technical and user interface design challenges and sets a baseline for automating usability problem detection and integrating such automation into UX practitioners’ workflow.
Industrial Case Studies for Digital Transformation of Engineering Processes using the Virtual Reality Technology

The virtual reality technology (VR) has undergone a rapid development in recent years. The VR software and hardware have become powerful and affordable. Therefore, the VR counts to the key technologies for enabling the digital transformation. However, this significant technology has not achieved the expected breakthrough in the industrial engineering area yet. The potential of the VR to digitize and to improve the engineering process of industrial companies especially for SMEs is still undiscovered. This has led to a poor implementation and use of the VR applications with an engineering scope compared with other business sectors e.g. gaming, entertainments. This paper presents and discusses two case studies achieved within cooperation project between the University of Applied Sciences Karlsruhe and German manufacturers for fire trucks and systems for firefighting and disaster protection. The aim of the case studies is the implementation of VR applications and their evaluation in terms of using in the business processes of the involved industrial partner. Based on the experiences gathered during this cooperation project a CAD Data transformation process for an industrial VR application as best-practice approach will be outlined and discussed in this paper. © 2020 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). Peer-review under responsibility of the scientific committee of the 27th CIRP Life Cycle Engineering (LCE) Conference.
<i>ε</i>-Constrained Differential Evolution Using an Adaptive <i>ε</i>-Level Control Method

Evolutionary algorithms and swarm intelligence algorithms have been widely used for constrained optimization problems for decades and numerous techniques for constraint handling have been proposed. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula> -constrained method is a very effective one. In the literature, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula> value was usually controlled via an exponential function, which is not competent for solving certain types of constrained optimization problems, e.g., whose global optima are located near the boundary of the feasible and infeasible regions. To solve this problem, this article proposes a new adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula> control method and incorporate it into a basic differential evolution (DE) algorithm: (DE/rand/1/exp). Based on the information of constraint violation in the current population, the adaptive method controls the value of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula> through a simple heuristic rule. Compared with the traditional exponential function-based control methods, the proposed adaptive method can prevent the algorithm from being trapped into local optima while retaining the obtained near-optimal candidate solutions in the infeasible region for generating promising searching paths. Besides, we set the crossover rate (CR) as a more reasonable value for DE/rand/1/exp, which can enhance the efficiency significantly. The well-known 2006 IEEE Congress on Evolutionary Computation (CEC 2006) competition on real-parameter single-objective constrained optimization benchmark is adopted to evaluate the effectiveness of the proposed adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula> -constrained DE. Fifteen constrained engineering optimization problems are collected from the literature to test the proposed algorithm. Moreover, the adaptive <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\varepsilon }$ </tex-math></inline-formula> control method is extended to an adaptive algorithm to solve the benchmark problems from CEC 2017. The comparison results confirm the superiority of the proposed method.
Deep learning for 3D imaging and image analysis in biomineralization research

Biomineralization research examines structure-function relations in all types of exo- and endo-skeletons and other hard tissues of living organisms, and it relies heavily on 3D imaging. Segmentation of 3D renderings of biomineralized structures has long been a bottleneck because of human limitations such as our available time, attention span, eye-hand coordination, cognitive biases, and attainable precision, amongst other limitations. Since recently, some of these routine limitations appear to be surmountable thanks to the development of deep-learning algorithms for biological imagery in general, and for 3D image segmentation in particular. Many components of deep learning often appear too abstract for a life scientist. Despite this, the basic principles underlying deep learning have many easy-to-grasp commonalities with human learning and universal logic. This primer presents these basic principles in what we feel is an intuitive manner, without relying on prerequisite knowledge of informatics and computer science, and with the aim of improving the reader's general literacy in artificial intelligence and deep learning. Here, biomineralization case studies are presented to illustrate the application of deep learning for solving segmentation and analysis problems of 3D images ridden by various artifacts, and/or which are plainly difficult to interpret. The presented portfolio of case studies includes three examples of imaging using micro-computed tomography (µCT), and three examples using focused-ion beam scanning electron microscopy (FIB-SEM), all on mineralized tissues. We believe this primer will expand the circle of users of deep learning amongst biomineralization researchers and other life scientists involved with 3D imaging, and will encourage incorporation of this powerful tool into their professional skillsets and to explore it further.
Adaptive Distributed Convolutional Neural Network Inference at the Network Edge with ADCNN

The emergence of the Internet of Things (IoT) has led to a remarkable increase in the volume of data generated at the network edge. In order to support real-time smart IoT applications, massive amounts of data generated from edge devices need to be processed using methods such as deep neural networks (DNNs) with low latency. To improve application performance and minimize resource cost, enterprises have begun to adopt Edge computing, a computation paradigm that advocates processing input data locally at the network edge. However, as edge nodes are often resource-constrained, running data-intensive DNN inference tasks on each individual edge node often incurs high latency, which seriously limits the practicality and effectiveness of this model.
Multi-Subspace Echo Hiding Based on Time-Frequency Similarities of Audio Signals

Audio watermarking plays an important role in copyright protection. Echo hiding, one of the most effective techniques for audio watermarking, has been studied for decades. However, the conventional echo hiding has been criticized for its weak security, as watermarks can be easily extracted by means of cepstrum analysis even without any prior knowledge. This article explores the time-frequency (T-F) characteristics of the repetition structures in an audio signal to improve the security of conventional echo hiding. In our approach, the original audio signal is first converted into a high-dimensional T-F representation, and then by clustering the time frames that have similar T-F characteristics into the same subspace, the original audio is decomposed into a union of subspaces with each corresponding to one time-domain subsignal. Paired and opposite echo kernels are applied to energy-balanced subsignals for watermark embedding, which significantly improves the security. In the watermark extraction process, the subspaces are recovered on the basis of the T-F similarities and cepstrum analysis is utilized to extract watermarks. The proposed embedding and extraction schemes thus offer a new approach for echo hiding. The results of experiments demonstrate the effectiveness of our approach with respect to inaudibility, security, and robustness.
Research Techniques Made Simple: Latent Class Analysis

Latent class analysis (LCA) is a statistical technique that allows for identification, in a population characterized by a set of predefined features, of hidden clusters or classes, that is, subgroups that have a given probability of occurrence and are characterized by a specific and predictable combination of the analyzed features. Compared with other methods of so called data segmentation, such as hierarchical clustering, LCA derives clusters using a formal probabilistic approach and can be used in conjunction with multivariate methods to estimate parameters. The optimal number of classes is the one that minimizes the degree of relationship among cases belonging to different classes, and it is decided by relying on methods such as the Bayesian Information Criterion that capitalize on the value of the negative log-likelihood function, a well-established measure of the goodness of fit of a statistical model. LCA has not been extensively used in dermatology. The areas of application are manifold, from the phenotype classification to the analysis of behavior in relation with risk factors to the performance of diagnostic tests.