Reviewerly/exHarmony huggingface dataset:
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique

Automating the research for the best neural network model is a task that has gained more and more relevance in the last few years. In this context, Neural Architecture Search (NAS) represents the most effective technique whose results rival the state of the art hand-crafted architectures. However, this approach requires a lot of computational capabilities as well as research time, which makes prohibitive its usage in many real-world scenarios. With its sequential model-based optimization strategy, Progressive Neural Architecture Search (PNAS) represents a possible step forward to face this resources issue. Despite the quality of the found network architectures, this technique is still limited in research time. A significant step in this direction has been done by Pareto-Optimal Progressive Neural Architecture Search (POPNAS), which expands PNAS with a time predictor to enable a trade-off between search time and accuracy, considering a multi-objective optimization problem. This paper proposes a new version of the Pareto-Optimal Progressive Neural Architecture Search, called POPNASv2. Our approach enhances its first version and improves its performance. We expanded the search space by adding new operators and improved the quality of both predictors to build more accurate Pareto fronts. Moreover, we introduced cell equivalence checks and enriched the search strategy with an adaptive greedy exploration step. Our efforts allow POPNASv2 to achieve PNAS-like performance with an average 4x factor search time speed-up.
Self secured model for cloud based IOT systems

A difficult problem to solve concerns the secure installation and startup of devices connected to the Internet of Things (IoT) via the Internet.To provide additional value-added services, this article deals with the verified configuration of IoT devices in a secure manner using the Internet. Following a review of the safe self-configuration limitations imposed on IoT and Cloud technologies; offer a Cloud-based architecture that enables the communication between IoT devices and several federated Cloud services. Specifically discuss two situations, one cloud environment and federated cloud infrastructure interact with IoT devices, and handle unique issues.In addition, it provides many operational design features that take into account the truly open hardware and software products already on the market.
Documenting the fun: Studying artistic information-creating behavior using research diaries

Research diaries as a qualitative method are found practical in information science. In information behavior literature, they are effective for capturing human experience and thought in situ in emic view. Although previous scholars undertook research diaries to explore information needs, seeking, use, and more, what is left relatively unknown is how this method may garner diverse forms of data beyond text when studying inherently dynamic information creation. To address this gap, this study applied research diaries inspired by cultural probes and maker documentation to investigate the making process of 25 arts and crafts hobbyists. Findings present a wide variety of entries generated from participants, illuminating the benefits of using research diaries to facilitate making and encourage reflection. Design implications are shared to unlock the potential of research diaries for examining information behavior, demonstrating their value in gathering rich empirical data and bringing participants engaging research experiences.
The Technology-Oriented Pathway for Auxiliary Diagnosis in the Digital Health Age: A Self-Adaptive Disease Prediction Model

The advent of the digital age has accelerated the transformation and upgrading of the traditional medical diagnosis pattern. With the rise of the concept of digital health, the emerging information technologies, such as machine learning (ML) and data mining (DM), have been extensively applied in the medical and health field, where the construction of disease prediction models is an especially effective method to realize auxiliary medical diagnosis. However, the existing related studies mostly focus on the prediction analysis for a certain disease, using models with which it might be challenging to predict other diseases effectively. To address the issues existing in the aforementioned studies, this paper constructs four novel strategies to achieve a self-adaptive disease prediction process, i.e., the hunger-state foraging strategy of producers (PHFS), the parallel strategy for exploration and exploitation (EEPS), the perturbation-exploration strategy (PES), and the parameter self-adaptive strategy (PSAS), and eventually proposes a self-adaptive disease prediction model with applied universality, strong generalization ability, and strong robustness, i.e., multi-strategies optimization-based kernel extreme learning machine (MsO-KELM). Meanwhile, this paper selects six different real-world disease datasets as the experimental samples, which include the Breast Cancer dataset (cancer), the Parkinson dataset (Parkinson's disease), the Autistic Spectrum Disorder Screening Data for Children dataset (Autism Spectrum Disorder), the Heart Disease dataset (heart disease), the Cleveland dataset (heart disease), and the Bupa dataset (liver disease). In terms of the prediction accuracy, the proposed MsO-KELM can obtain ACC values in analyzing these six diseases of 94.124%, 84.167%, 91.079%, 72.222%, 70.184%, and 70.476%, respectively. These ACC values have all been increased by nearly 2-7% compared with those obtained by the other models mentioned in this paper. This study deepens the connection between information technology and medical health by exploring the self-adaptive disease prediction model, which is an intuitive representation of digital health and could provide a scientific and reliable diagnostic basis for medical workers.
Improvement of an Adaptive Robot Control by Particle Swarm Optimization-Based Model Identification

Model-based controllers suffer from the effects of modeling imprecisions. The analytical form of the available model often contains only approximate parameters and can be physically incomplete. The consequences of these effects can be compensated by adaptive techniques and by the improvement of the available model. Lyapunov function-based classic methods, which assume exact analytical model forms, guarantee asymptotic stability by cautious and slow parameter tuning. Fixed point iteration-based adaptive controllers can work without the exact model form but immediately yield precise trajectory tracking. They neither identify nor improve the parameters of the available model. However, any amendment of the model can improve the controller’s operation by affecting its range and speed of convergence. It is shown that even very primitive, fast, and simple versions of evolutionary computation-based methods can produce considerable improvement in their operation. Particle swarm optimization (PSO) is an attractive, efficient, and simple tool for model improvement. In this paper, a PSO-based model approximation technique was investigated for use in the control of a three degrees of freedom PUMA-type robot arm via numerical simulations. A fixed point iteration (FPI)-based adaptive controller was used for tracking a nominal trajectory while the PSO attempted to refine the model. It was found that the refined model still had few errors, the effects of which could not be completely neglected in the model-based control. The best practical solution seems to be the application of the same adaptive control with the use of the more precise, PSO-improved model. Apart from a preliminary study, the first attempt to combine PSO with FPI is presented here.
Teaching Probabilistic Graphical Models with OpenMarkov

OpenMarkov is an open-source software tool for probabilistic graphical models. It has been developed especially for medicine, but has also been used to build applications in other fields and for tuition, in more than 30 countries. In this paper we explain how to use it as a pedagogical tool to teach the main concepts of Bayesian networks and influence diagrams, such as conditional dependence and independence, d-separation, Markov blankets, explaining away, optimal policies, expected utilities, etc., and some inference algorithms: logic sampling, likelihood weighting, and arc reversal. The facilities for learning Bayesian networks interactively can be used to illustrate step by step the performance of the two basic algorithms: search-and-score and PC.
Using Domain Adaptation for Incremental SVM Classification of Drift Data

A common assumption in machine learning is that training data is complete, and the data distribution is fixed. However, in many practical applications, this assumption does not hold. Incremental learning was proposed to compensate for this problem. Common approaches include retraining models and incremental learning to compensate for the shortage of training data. Retraining models is time-consuming and computationally expensive, while incremental learning can save time and computational costs. However, the concept drift may affect the performance. Two crucial issues should be considered to address concept drift in incremental learning: gaining new knowledge without forgetting previously acquired knowledge and forgetting obsolete information without corrupting valid information. This paper proposes an incremental support vector machine learning approach with domain adaptation, considering both crucial issues. Firstly, a small amount of new data is used to fine-tune the previous model to generate a model that is sensitive to the new data but retains the previous data information by transferring parameters. Secondly, an ensemble and model selection mechanism based on Bayesian theory is proposed to keep the valid information. The computational experiments indicate that the performance of the proposed model improved as new data was acquired. In addition, the influence of the degree of data drift on the algorithm is also explored. A gain in performance on four out of five industrial datasets and four synthetic datasets has been demonstrated over the support vector machine and incremental support vector machine algorithms.
Techno-Economic Assessment of a 100 kWp Solar Rooftop PV System for Five Hospitals in Central Southern Thailand

This paper presents a techno-economic assessment of a 100 kWp solar rooftop photovoltaic (PV) system at five hospitals in central southern Thailand. The system encompasses 100 kWp PV panels, 100 kW grid-tied inverters and balance of system (BOS) under the grid code of the Provincial Electricity Authority (PEA). The latest PV technology of bifacial mono-crystalline solar panels, inverters and BOS were simulated along with the Meteonorm 7.3 database using the PVsyst simulation toolkit with different tilt angles, orientations, solar radiations and ambient temperature. The technical aspects of solar rooftop PV power generation systems include the annual energy output and the performance ratio (PR) under IEC standard. Further, an economic analysis of the model was examined using a cost benefit analysis (CBA) and various assumptions. Four main financial criteria, i.e., benefit cost ratio (BCR), net present value (NPV), internal rate of return (IRR), and payback period (PBP) were evaluated under three different scenarios: (1) self-consumption scheme, (2) feed-in tariff (FiT) scheme, and (3) private power purchase agreement (PPA) scheme. Finally, the levelized cost of energy (LCOE) was also calculated. The results reveal that the Takua Thung hospital is characterized by the maximum average global horizontal irradiation (GHI) and the maximum annual produced energy of 199 kWh/m2 and 164.8 MWh/year, respectively. The PR calculated for all hospital sites is above 85%. The outcomes of the financial analysis show that the optimum scenarios are PPA and FiT schemes. The LCOE analysed in this study indicates that the Takua Thung hospital site has the lowest LCOE at 2.47 THB/kWh (0.07 USD/kWh). This research confirms the potential for hospitals and stakeholders in central southern Thailand for investments in solar rooftop PV systems
Does the Personality of Consumers Influence the Assessment of the Experience of Interaction with Social Robots?

Abstract In recent years, in response to the effects of Covid-19, there has been an increase in the use of social robots in service organisations, as well as in the number of interactions between consumers and robots. However, it is not clear how consumers are valuing these experiences or what the main drivers that shape them are. Furthermore, it is an open research question whether these experiences undergone by consumers can be affected by their own personality. This study attempts to shed some light on these questions and, to do so, an experiment is proposed in which a sample of 378 participants evaluate a simulated front-office service experience delivered by a social robot. The authors investigate the underlying process that explains the experience and find that cognitive-functional factors, emphasising efficiency, have practically the same relevance as emotional factors, emphasising stimulation. In addition, this research identifies the personality traits of the participants and explores their moderating role in the evaluation of the experience. The results reveal that each personality trait, estimated between high and low poles, generates different responses in the evaluation of the experience.
A Non-Local Tensor Completion Algorithm Based on Weighted Tensor Nuclear Norm

In this paper, we proposed an image inpainting algorithm, including an interpolation step and a non-local tensor completion step based on a weighted tensor nuclear norm. Specifically, the proposed algorithm adopts the triangular based linear interpolation algorithm firstly to preform the observation image. Second, we extract the non-local similar patches in the image using the patch match algorithm and rearrange them to a similar tensor. Then, we use the tensor completion algorithm based on the weighted tensor nuclear norm to recover the similar tensors. Finally, we regroup all these recovered tensors to obtain the final recovered image. From the image inpainting experiments on color RGB images, we can see that the performance of the proposed algorithm on the peak signal-to-noise ratio (PSNR) and the relative standard error (RSE) are significantly better than other image inpainting methods.
Renewable-Aware Geographical Load Balancing Using Option Pricing for Energy Cost Minimization in Data Centers

It is becoming increasingly difficult to properly control the power consumption of widely dispersed data centers. Energy consumption is high because of the need to run these data centers (DCs) that handle incoming user requests. The rising cost of electricity at the data center is a contemporary problem for cloud service providers (CSPs). Recent studies show that geo-distributed data centers may share the load and save money using variable power prices and pricing derivatives in the wholesale electricity market. In this study, we evaluate the problem of reducing energy expenditures in geographically dispersed data centers while accounting for variable system dynamics, power price fluctuations, and renewable energy sources. We present a renewable energy-based load balancing employing an option pricing (RLB-Option) online algorithm based on a greedy approach for interactive task allocation to reduce energy costs. The basic idea of RLB-Option is to process incoming user requests using available renewable energy sources. In contrast, in the case of unprocessed user requests, the workload will be processed using brown energy or call option contract at each timeslot. We formulate the energy cost minimization in geo-distributed DCs as an optimization problem considering geographical load balancing, renewable energy, and an option pricing contract from the derivative market while satisfying the set of constraints. We prove that the RLB-Option can reduce the energy cost of the DCs close to that of the optimal offline algorithm with future information. Compared to standard workload allocation methods, RLB-Option shows considerable cost savings in experimental evaluations based on real-world data.
Global–Local Self-Attention Based Transformer for Speaker Verification

Transformer models are now widely used for speech processing tasks due to their powerful sequence modeling capabilities. Previous work determined an efficient way to model speaker embeddings using the Transformer model by combining transformers with convolutional networks. However, traditional global self-attention mechanisms lack the ability to capture local information. To alleviate these problems, we proposed a novel global–local self-attention mechanism. Instead of using local or global multi-head attention alone, this method performs local and global attention in parallel in two parallel groups to enhance local modeling and reduce computational cost. To better handle local location information, we introduced locally enhanced location encoding in the speaker verification task. The experimental results of the VoxCeleb1 test set and the VoxCeleb2 dev set demonstrated the improved effect of our proposed global–local self-attention mechanism. Compared with the Transformer-based Robust Embedding Extractor Baseline System, the proposed speaker Transformer network exhibited better performance in the speaker verification task.
Gender Dynamics in Human-AI Role-Taking

Purpose Role-taking is a basic social process underpinning much of the structural social psychology paradigm – a paradigm built on empirical studies of human interaction. Yet today, our social worlds are occupied by bots, voice assistants, decision aids, and other machinic entities collectively referred to as artificial intelligence (AI). The integration of AI into daily life presents both challenges and opportunities for social psychologists. Through a vignette study, the authors investigate role-taking and gender in human-AI relations.
Automatic Modulation Recognition Based on the Optimized Linear Combination of Higher-Order Cumulants

Automatic modulation recognition (AMR) is used in various domains-from general-purpose communication to many military applications-thanks to the growing popularity of the Internet of Things (IoT) and related communication technologies. In this research article, we propose an innovative idea of combining the classical mathematical technique of computing linear combinations (LCs) of cumulants with a genetic algorithm (GA) to create super-cumulants. These super-cumulants are further used to classify five digital modulation schemes on fading channels using the K-nearest neighbor (KNN). Our proposed classifier significantly improves the percentage recognition accuracy at lower SNRs when using smaller sample sizes. A comparison with existing techniques manifests the supremacy of our proposed classifier.
Detection and Relative Quantification of Neodymium in Sillai Patti Carbonatite Using Decision Tree Classification of the Hyperspectral Data

A recent increase in the importance of Rare Earth Elements (REEs), proportional to advancements in modern technology, green energy, and defense, has urged researchers to look for more sophisticated and efficient exploration methods for their host rocks, such as carbonatites. Hyperspectral remote sensing has long been recognized as having great potential to identify the REEs based on their sharp and distinctive absorption features in the visible near-infrared (VNIR) and shortwave infrared (SWIR) electromagnetic spectral profiles. For instance, neodymium (Nd), one of the most abundant Light Rare Earth Elements (LREEs), has among the most distinctive absorption features of REEs in the VNIR part of the electromagnetic spectrum. Centered at ~580, ~745, ~810, and ~870 nm in the VNIR, the positions of these absorption features have been proved to be independent of the mineralogy that hosts Nd, and the features can be observed in samples as low in Nd as 1000 ppm. In this study, a neodymium index (NI) is proposed based on the 810 nm absorption feature and tested on the hyperspectral images of the Sillai Patai carbonatite samples to identify Nd pixels and to decipher the relative concentration of Nd in the samples based on the depth of the absorption feature. A preliminary spectral study of the carbonatite samples was carried out using a spectroradiometer to determine the presence of Nd in the samples. Only two of the absorption features of Nd, centered at ~745 and ~810 nm, are prominent in the Nd-rich samples. The other absorption features are either weak or suppressed by the featureless spectra of the associated minerals. Similar absorption features are found in the VNIR and SWIR images of the rock samples captured by the laboratory-based hyperspectral cameras that are processed through Minimum Noise Fraction (MNF) and Fast Fourier Transform (FFT) to filter the signal and noise from the reflectance data. An RGB false-color composite of continuum-removed VNIR reflectance bands covering wavelengths of 587.5, 747.91, and 810.25 nm efficiently displayed the spatial distribution of Nd-rich hotspots in the hyperspectral image. The depth of the 810 nm absorption feature, which corresponds to the concentration of Nd in a pixel, is comparatively greater in these zones and is quantified using the proposed NI such that the deeper the absorption feature, the higher the NI. To quantify the Nd-rich pixels in the continuum-removed VNIR images, different threshold values of NI are introduced into a decision tree classifier which generates the number of pixels in each class. The strength of the proposed NI coupled with the decision tree classifier is further supported by the accuracy assessment of the classified images generating the Kappa coefficient of 0.82. Comparing the results of the remote sensing data obtained in this study with some of the previously published studies suggests that the Sillai Patti carbonatite is rich in Nd and associated REEs, with some parts of the samples as high in Nd concentration as 1000 ppm.
Multi-Objective Routing Optimization for 6G Communication Networks Using a Quantum Approximate Optimization Algorithm

Sixth-generation wireless (6G) technology has been focused on in the wireless research community. Global coverage, massive spectrum usage, complex new applications, and strong security are among the new paradigms introduced by 6G. However, realizing such features may require computation capabilities transcending those of present (classical) computers. Large technology companies are already exploring quantum computers, which could be adopted as potential technological enablers for 6G. This is a promising avenue to explore because quantum computers exploit the properties of quantum states to perform certain computations significantly faster than classical computers. This paper focuses on routing optimization in wireless mesh networks using quantum computers, explicitly applying the quantum approximate optimization algorithm (QAOA). Single-objective and multi-objective examples are presented as robust candidates for the application of quantum machine learning. Moreover, a discussion about quantum supremacy estimation for this problem is provided.
Efficient tripartite scheme of remotely sharing single-qubit operation with five-qubit Brown state

A tripartite operation sharing scheme is put forward by employing a five-qubit entangled state [proposed by Brown et al., J. Phys. A 38, 1119 (2005)] as quantum channel. Its sharer asymmetry and its success determinacy are both identified. Moreover, its security is ensured via analyses. In terms of current techniques, its experimental feasibility is discussed and thus confirmed. Furthermore, its comparison with Ye et al.’s scheme [Chin. Phys. Lett. 30, 020301 (2013)] utilizing the same quantum channel is made and shows its distinct advantage of degrading the intensity and difficulty of necessary operations.