Reviewerly/exHarmony huggingface dataset:
Maximum number of generations as a stopping criterion considered harmful

Evolutionary algorithms have been shown to be very effective in solving complex optimization problems. This has driven the research community in the development of novel, even more efficient evolutionary algorithms. The newly proposed algorithms need to be evaluated and compared with existing state-of-the-art algorithms, usually by employing benchmarks. However, comparing evolutionary algorithms is a complicated task, which involves many factors that must be considered to ensure a fair and unbiased comparison. In this paper, we focus on the impact of stopping criteria in the comparison process. Their job is to stop the algorithms in such a way that each algorithm has a fair opportunity to solve the problem. Although they are not given much attention, they play a vital role in the comparison process. In the paper, we compared different stopping criteria with different settings, to show their impact on the comparison results. The results show that stopping criteria play a vital role in the comparison, as they can produce statistically significant differences in the rankings of evolutionary algorithms. The experiments have shown that in one case an algorithm consumed 50 times more evaluations in a single generation, giving it a considerable advantage when max gen was used as the stopping criterion, which puts the validity of most published work in question.
Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction

As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.
Efficient Combination of CNN and Transformer for Dual-Teacher Uncertainty-guided Semi-supervised Medical Image Segmentation

Deep learning-based methods for fast target segmentation of magnetic resonance imaging (MRI) have become increasingly popular in recent years. Generally, the success of deep learning methods in medical image segmentation tasks relies on a large amount of labeled data. The time-consuming and labor-intensive problem of data annotation is a major challenge in medical image segmentation tasks. The aim of this work is to enhance the segmentation of MR images using a semi-supervised learning-based method using a small amount of labeled data and a large amount of unlabeled data.To utilize the effective information of the unlabeled data, we designed the method of guiding the Student segmentation model simultaneously by the Dual-Teacher structure of CNN and transformer forming the subject network. Both Teacher A and Student models are CNNs, and the TA-S module they form is a mean teacher structure with added data noise. In the TB-S module formed by the combination of Student and Teacher B models, their backbone networks CNN and transformer capture the local and global information of the image at the same time, respectively, to create pseudo labels for each other and perform cross-supervision. The Dual-Teacher guides the Student through synchronous training and performs knowledge rectification and communication with each other through consistent regular constraints, which better utilizes the valid information in the unlabeled data. In addition, the segmentation predictions of Teacher A and Student and Teacher A and Teacher B are screened for uncertainty assessment during the training process to enhance the prediction accuracy and generalization of the model. This method uses the mechanism of simultaneous training of the synthetic structure composed of TA-S and TB-S modules to jointly guide the optimization of the Student model to obtain better segmentation ability.We evaluated the proposed method on a publicly available MRI dataset from a cardiac segmentation competition organized by MICCAI in 2017. Compared with several existing state-of-the-art semi-supervised segmentation methods, the method achieves better segmentation results in terms of Dice coefficient and HD distance evaluation metrics of 0.878 and 4.9 mm and 0.886 and 5.0 mm, respectively, using a training set containing only 10% and 20% of labeled data.This method fuses CNN and transformer to design a new Teacher-Student semi-supervised learning optimization strategy, which greatly improves the utilization of a large number of unlabeled medical images and the effectiveness of model segmentation results.
Cryptopricing: Whence comes the value for cryptocurrencies and NFTs?

This article provides a critical review of pricing issues related to cryptocurrency and NFTs from a marketing perspective and tries to answer the question of whence comes the value for cryptocurrencies and NFTs. It shows that the value for a cryptocurrency like Bitcoin hinges on the collective belief in the marketplace in its long-term survival as a store of value and a unit of exchange. As such, the belief can give rise to a plethora of pricing equilibria for Bitcoin, and it can also be swayed by many demand factors. Consequently, Bitcoin prices will beset with uncertainties and speculations for the foreseeable future and can swing widely and wildly at times. However, the plethora of pricing equilibria also suggests that it is unlikely that Bitcoin may suddenly disappear after its long, successful run so far. In contrast, NFT prices find their value bases not so much in the collective belief in NFTs as a viable product category, although there are many reasons for optimism in that regard, but in the willingness to pay by individuals that depends on many familiar and well-studied factors. This article proposes many pricing-related research questions.
Plant-scale biogas production prediction based on multiple hybrid machine learning technique

The parameters from full-scale biogas plants are highly nonlinear and imbalanced, resulting in low prediction accuracy when using traditional machine learning algorithms. In this study, a hybrid extreme learning machine (ELM) model was proposed to improve prediction accuracy by solving imbalanced data. The results showed that the best ELM model had a good prediction for validation data (R2 = 0.972), and the model was developed into the software (prediction error of 2.15 %). Furthermore, two parameters within a certain range (feed volume (FV) = 23-45 m3 and total volatile fatty acids of anaerobic digestion (TVFAAD) = 1750-3000 mg/L) were identified as the most important characteristics that positively affected biogas production. This study combines machine learning with data-balancing techniques and optimization algorithms to achieve accurate predictions of plant biogas production at various loads.
HCCANet: histopathological image grading of colorectal cancer using CNN based on multichannel fusion attention mechanism

Histopathological image analysis is the gold standard for pathologists to grade colorectal cancers of different differentiation types. However, the diagnosis by pathologists is highly subjective and prone to misdiagnosis. In this study, we constructed a new attention mechanism named MCCBAM based on channel attention mechanism and spatial attention mechanism, and developed a computer-aided diagnosis (CAD) method based on CNN and MCCBAM, called HCCANet. In this study, 630 histopathology images processed with Gaussian filtering denoising were included and gradient-weighted class activation map (Grad-CAM) was used to visualize regions of interest in HCCANet to improve its interpretability. The experimental results show that the proposed HCCANet model outperforms four advanced deep learning (ResNet50, MobileNetV2, Xception, and DenseNet121) and four classical machine learning (KNN, NB, RF, and SVM) techniques, achieved 90.2%, 85%, and 86.7% classification accuracy for colorectal cancers with high, medium, and low differentiation levels, respectively, with an overall accuracy of 87.3% and an average AUC value of 0.9.In addition, the MCCBAM constructed in this study outperforms several commonly used attention mechanisms SAM, SENet, SKNet, Non_Local, CBAM, and BAM on the backbone network. In conclusion, the HCCANet model proposed in this study is feasible for postoperative adjuvant diagnosis and grading of colorectal cancer.
Bitcoin unchained: Determinants of cryptocurrency exchange liquidity

We study bitcoin to US dollar (BTCUSD) liquidity and liquidity determinants using order book data from three large cryptocurrency exchanges. The BTCUSD market is more liquid than US equity markets with bid–ask spreads often below 1 basis point. We find that BTCUSD liquidity is largely explained by same-exchange past liquidity, past cryptocurrency market-wide liquidity and volatility, and fees charged on the blockchain for bitcoin transfers. Surprisingly, we find that BTCUSD liquidity is unrelated to broader financial markets and financial market liquidity.
Sensing with Earables

Earables have emerged as a unique platform for ubiquitous computing by augmenting ear-worn devices with state-of-the-art sensing. This new platform has spurred a wealth of new research exploring what can be detected on a wearable, small form factor. As a sensing platform, the ears are less susceptible to motion artifacts and are located in close proximity to a number of important anatomical structures including the brain, blood vessels, and facial muscles which reveal a wealth of information. They can be easily reached by the hands and the ear canal itself is affected by mouth, face, and head movements. We have conducted a systematic literature review of 271 earable publications from the ACM and IEEE libraries. These were synthesized into an open-ended taxonomy of 47 different phenomena that can be sensed in, on, or around the ear. Through analysis, we identify 13 fundamental phenomena from which all other phenomena can be derived, and discuss the different sensors and sensing principles used to detect them. We comprehensively review the phenomena in four main areas of (i) physiological monitoring and health, (ii) movement and activity, (iii) interaction, and (iv) authentication and identification. This breadth highlights the potential that earables have to offer as a ubiquitous, general-purpose platform.
A Weakly Supervised Learning Framework for Salient Object Detection via Hybrid Labels

Fully-supervised salient object detection (SOD) methods have made great progress, but such methods often rely on a large number of pixel-level annotations, which are time-consuming and labour-intensive. In this paper, we focus on a new weakly-supervised SOD task under hybrid labels, where the supervision labels include a large number of coarse labels generated by the traditional unsupervised method and a small number of real labels. To address the issues of label noise and quantity imbalance in this task, we design a new pipeline framework with three sophisticated training strategies. In terms of model framework, we decouple the task into label refinement sub-task and salient object detection sub-task, which cooperate with each other and train alternately. Specifically, the R-Net is designed as a two-stream encoder-decoder model equipped with Blender with Guidance and Aggregation Mechanisms (BGA), aiming to rectify the coarse labels for more reliable pseudo-labels, while the S-Net is a replaceable SOD network supervised by the pseudo labels generated by the current R-Net. Note that, we only need to use the trained S-Net for testing. Moreover, in order to guarantee the effectiveness and efficiency of network training, we design three training strategies, including alternate iteration mechanism, group-wise incremental mechanism, and credibility verification mechanism. Experiments on five SOD benchmarks show that our method achieves competitive performance against weakly-supervised/unsupervised methods both qualitatively and quantitatively. The code and results can be found from the link of <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://rmcong.github.io/proj_Hybrid-Label-SOD.html</uri> .
CGRM: A Context-Based Graph Reasoning Model for Fitting Detection

Transmission lines’ status detection and fault diagnosis are based on the high-accuracy detection of typical fittings in aerial inspection photographs. Detecting tiny-size fittings in complicated sceneries is still an unresolved issue, nevertheless, because of the constraints of the small number of feature pixels. Considering the characteristics of fitting connection structures, we proposed a novel method named context-based graph reasoning model (CGRM), which is based on multiscale context, self-attention map, and graph learning. This proposed model includes one graph-based global context (GGC) subnetwork and one multiscale local context (MLC) subnetwork. The GGC effectively extracted the pixel-level global map based on multihead attention mechanism to capture global context and the graph convolutional neural network (GCN) was further used for reasoning. The MLC exploited the internal and external contextual information of multiscale for each unique proposal to capture its local context. The final detection result is then reached by fusing the global and local circumstances. Experimental results well demonstrate the superiority of the proposed model for fitting detection. In addition, compared with the original Faster R-CNN, the detection accuracy of CGRM is increased by 16.3%. It also provides a new idea for general object detection of tiny-size targets.
The mediator and moderator roles of perceived cost on the relationship between organizational readiness and the intention to adopt blockchain technology

Blockchain is a disruptive technology that is expected to revolutionize the world of trusted transactions. Through a combination of cryptographic techniques, a distributed, shared, and immutable ledger, consensus validation of transactions, and a financial audit trail, blockchain is poised to do for businesses what the Internet did for communication. With its promise of reducing cost, time, friction, and fraud in business transactions and ensuring trust, identity management, and reputation management, companies around the world are now contemplating their transition to blockchain versions of themselves. However, the cost of implementing such technology and its effect on the readiness of the organization to adopt blockchain has not been fully explored yet. This study examines the mediation and moderation roles of the perceived cost on the relationship between organization readiness and the intention to adopt blockchain. Data was collected from blockchain experts and Partial least squares structural equation modeling (PLS-SEM) was used as the analytical tool to test all of the framework hypotheses. To investigate the potential managerial implications of this topic, Importance-Performance Map Analysis (IPMA) was utilized. The findings reveal that perceived cost is not a mediator; rather, it moderates the relationship between the TRI (Technology Readiness Index) enablers and the TRI inhibitors with the intention to adopt blockchain. In terms of importance, the empirical data analysis through IPMA revealed that the enablers construct is the most important factor, while perceived cost has the highest performance on the intention to adopt blockchain. With this discovery, our findings are expected to aid decision-makers in prioritizing and improving the enabling aspects required for the successful adoption of blockchain technology.
Explainable Artificial Intelligence for Cybersecurity

Recently, numerous Machine Learning (ML) algorithms have been applied in many areas of cybersecurity. However, most of these systems can only be seen as a black box to users. To improve our understanding of such systems, adversarial machine learning approaches can be used. The main features are detected by analyzing the extent of such changes, which helps in identifying the main reasons for misclassification . In this paper, the presented approach has obtained satisfactory results that accurately explains the reasons for misclassifications. Some features of the presented method can be applied to any classifier with defined gradients without the need for modifications. The proposed model can be extended to perform more diagnoses and it can be used for a deeper analysis of systems, obtaining more than 95% accuracy classification on the used datasets in the experiments. • Explains misclassifications by data-driven AI models using an adversarial approach. • Compute the minimum number of changes to input features required. • Increased average classification accuracy by 2.5% post modification. • Designed a black-box attack to test the correctness and trustworthiness. • Used explanation maps to examine the effectiveness of attacks.
Hourly solar irradiance forecasting based on encoder–decoder model using series decomposition and dynamic error compensation

Accurate solar irradiance prediction is crucial for harnessing solar energy resources. However, the pattern of irradiance sequence is intricate due to its nonlinear and non-stationary characteristics. In this paper, a deep hybrid model based on encoder–decoder is proposed to cope with the complex pattern for hourly irradiance forecasting. The hybrid deep model integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), encoder–decoder module, and dynamic error compensation (DEC) architecture. The CEEMDAN is implemented to reduce the nonlinear and non-stationarity of the irradiance sequence. The encoder–decoder integrates temporal convolutional networks (TCN), long short-term memory networks (LSTM), and multi-layer perceptron (MLP) for temporal features extraction and multi-step prediction. The DEC architecture dynamically updates the model based on adjacent error information to mine the predictable components of error information. Furthermore, a new loss function is further proposed for multi-objective optimization to balance the performance of multi-step forecasting. In the hourly irradiance forecasting experiments on the three public datasets, the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (R) of the proposed model are observed to be in a range of 30.693-34.433 W/m2, 19.398-22.900 W/m2, and 0.9872-0.9902, respectively. Compared to the benchmark models (including MLP, LSTM, and TCN), the RMSE and MAE reduce by 10.76%–22.00% and 5.47%–20.40%, respectively. The experimental results indicate that the proposed model shows accurate and robust forecasting performance and is a reliable alternative to hourly irradiance forecasting.
Quantum Simulation of Molecular Electronic States with a Transcorrelated Hamiltonian: Higher Accuracy with Fewer Qubits

Simulation of electronic structure is one of the most promising applications on noisy intermediate-scale quantum (NISQ) era devices. However, NISQ devices suffer from a number of challenges like limited qubit connectivity, short coherence times, and sizable gate error rates. Thus, desired quantum algorithms should require shallow circuit depths and low qubit counts to take advantage of these devices. Here, we attempt to reduce quantum resource requirements for molecular simulations on a quantum computer while maintaining the desired accuracy with the help of classical quantum chemical theories of canonical transformation and explicit correlation. In this work, compact ab initio Hamiltonians are generated classically, in the second quantized form, through an approximate similarity transformation of the Hamiltonian with (a) an explicitly correlated two-body unitary operator with generalized pair excitations that remove the Coulombic electron-electron singularities from the Hamiltonian and (b) a unitary one-body operator to efficiently capture the orbital relaxation effects required for accurate description of the excited states. The resulting transcorrelated Hamiltonians are able to describe both the ground and the excited states of molecular systems in a balanced manner. Using the variational quantum eigensolver (VQE) method based on the unitary coupled cluster with singles and doubles (UCCSD) ansatz and only a minimal basis set (ANO-RCC-MB), we demonstrate that the transcorrelated Hamiltonians can produce ground state energies comparable to the reference CCSD energies with the much larger cc-pVTZ basis set. This leads to a reduction in the number of required CNOT gates by more than 3 orders of magnitude for the chemical species studied in this work. Furthermore, using the quantum equation of motion (qEOM) formalism in conjunction with the transcorrelated Hamiltonian, we are able to reduce the deviations in the excitation energies from the reference EOM-CCSD/cc-pVTZ values by an order of magnitude. The transcorrelated Hamiltonians developed here are Hermitian and contain only one- and two-body interaction terms and thus can be easily combined with any quantum algorithm for accurate electronic structure simulations.
A systematic literature review on educational recommender systems for teaching and learning: research trends, limitations and opportunities

Recommender systems have become one of the main tools for personalized content filtering in the educational domain. Those who support teaching and learning activities, particularly, have gained increasing attention in the past years. This growing interest has motivated the emergence of new approaches and models in the field, in spite of it, there is a gap in literature about the current trends on how recommendations have been produced, how recommenders have been evaluated as well as what are the research limitations and opportunities for advancement in the field. In this regard, this paper reports the main findings of a systematic literature review covering these four dimensions. The study is based on the analysis of a set of primary studies (N = 16 out of 756, published from 2015 to 2020) included according to defined criteria. Results indicate that the hybrid approach has been the leading strategy for recommendation production. Concerning the purpose of the evaluation, the recommenders were evaluated mainly regarding the quality of accuracy and a reduced number of studies were found that investigated their pedagogical effectiveness. This evidence points to a potential research opportunity for the development of multidimensional evaluation frameworks that effectively support the verification of the impact of recommendations on the teaching and learning process. Also, we identify and discuss main limitations to clarify current difficulties that demand attention for future research.The online version contains supplementary material available at 10.1007/s10639-022-11341-9.