Reviewerly/exHarmony huggingface dataset:
Self-supervised heterogeneous hypergraph network for knowledge tracing

Recently online intelligent education has caught more and more attention, especially due to the global influence of Covid-19. A major task of intelligent education is Knowledge Tracing (KT) which aims to capture students’ dynamic status based on their historical interaction records and predict their responses to new questions. However, most existing KT methods suffer from the record data sparsity problem. In reality, there are a huge number of questions in the online database and students can only interact with a very small set of these questions. The records of some questions could be extremely sparse, which may significantly degrade the performance of traditional KT methods. Although recent graph neural network (GNN) based KT methods can fuse graph-structured information and improve the representation of questions to some extent, the pairwise structure of GNN neglects the complex high-order and heterogeneous relations among questions. To resolve the above issues, we develop a novel KT model with the heterogeneous hypergraph network (HHN) and propose an attentive mechanism, including intra- and inter-graph attentions, to aggregate neighbors’ information upon HHN. To further enhance the question representation, we supplement the supervised prediction task of KT with an auxiliary self-supervised task, i.e., we additionally generate an augmented view with adaptive data augmentation to implement contrastive learning and exploit the unobserved relations among questions. We conduct extensive experiments on several real-world datasets. Experimental results demonstrate that our proposed method achieves significant performance improvement compared to some state-of-the-art KT methods.
Homomorphic filtering for the image enhancement based on fractional-order derivative and genetic algorithm

The main aim of image enhancement is to improve the visual quality or appearance of an image. This article presents an image enhancement method based on Grunwald-Letnikov, Riemann-Liouville fractional-order derivatives and genetic algorithm to boost the homomorphic filtering performance. Homomorphic filtering is used to attenuate the contribution made by the illumination and amplify the reflectance components of an image. This work uses a fractional-order derivative to enhance the mid- and high-frequencies and preserve the low-frequencies. The enhancement of the image depends on the parameters required for the homomorphic filter function and fractional-order value, which are not the same for all types of images. Hence, the genetic algorithm is applied, which automatically determines these parameters by optimizing the fitness function. The capability of the proposed approach is evaluated using performance metrics such as information entropy, average gradient, and contrast improvement index on different sizes of images. An average improvement in information entropy of 6.5%, average gradient of 52%, and contrast improvement index of 75%, respectively, are achieved for standard, medical images and images with low contrast and non-uniform illumination conditions. Also, the proposed method outperforms the existing methods by producing a better visual appearance of the image.
A hybrid adaptive Differential Evolution based on Gaussian tail mutation

In this paper, we propose an improved version of JADE by hybridizing the JADE algorithm with a Gaussian tail, a modified hunger games search (HGS) algorithm, and a distance-based multi-population (DbMP) approach named as HJADE-GT. In the proposed algorithm, two sets of operators (modified HGS operator and JADE operator with Gaussian tail) are utilized to generate offspring to further enhance the exploration and exploitation abilities. DbMP approach is proposed to make full use of feedback information from the whole population. In HJADE-GT, the main population is divided into three fixed-size subpopulations: exploration subpopulation, balanced subpopulation, and exploitation subpopulation. Secondly, a modified HGS operator is incorporated into the exploration subpopulation to improve global searchability. Thirdly, the JADE operator with a Gaussian tail is utilized to enhance the ability of exploitation subpopulation. Finally, the DbMP approach is utilized for the balanced subpopulation to choose an appropriate operator for current individuals to make full use of feedback information from the exploration subpopulation and exploitation subpopulation. In the experimental studies, it is demonstrated that the proposed algorithm presents competitive performance with 13 well-known algorithms, including jDE, SaDE, JADE, MPEDE, SHADE, CoDE, SaJADE, iLSHADE, jSO, CMAES, MGFPA, ESSA, and PPSO on CEC2017 benchmark functions. Four engineering problems and three dynamic economic emission dispatch (DEED) problems were utilized to verify the performance of HJADE-GT, and the experiments on DEED problems confirm that HJADE-GT is an efficient algorithm to solve engineering and large-scale constrained DEED problems.
Classification and Prediction of Sustainable Quality of Experience of Telecommunication Service Users Using Machine Learning Models

The quality of experience (QoE) of the individual user of telecommunication services is one of the most important criteria for choosing the service package of mobile providers. To evaluate the sustainability of QoE, this paper uses indicators of user satisfaction or dissatisfaction with the quality of network services (QoS), especially with conversational, streaming, interactive and background classes of traffic in networks. The importance of knowing the impact of selected combinations of paired legal–regulatory, technological–process, content-formatted and performative, contextual–relational and subjective user-influencing factors on QoE sustainability is investigated using a multiple linear regression model created in Minitab statistical software, machine learning model based on boosted decision trees created in the MATLAB software package and predictive models created by using an automatic modeling method. The classification of influence factors and their matching for the analysis of interaction fields of users and services aim to mark QoE as sustainable by determining the accuracy of the weight of subjective ratings of user satisfaction indicators as transitional variables in the predictive model of QoE. The hypothetical setting is that the individual user’s curiosity, creativity, communication, personality, courage, confidence, charisma, competence, common sense and memory are adequate transition variables in a sustainable QoE model. Using the applied methodology with an original research approach, data were collected on the evaluations of research variables from anonymous users of mobile operators in the geo-space of Republika Srpska and B&amp;H. By treating the data with mathematical and machine learning models, the QoE assessment was performed at the level of an individual user, and after that, several models were created for the prediction and classification of QoEi. The results show that the relative error (RE) of the predictive models, created over the collected dataset, is insufficiently low, so the improvement of the prediction performance was achieved via data augmentation (DA). In this way, the relative prediction error is reduced to a value of RE = 0.247. The DA method was also applied for the creating a classification model, which at best demonstrated an accuracy of 94.048%.
Towards Energy Transition: Use of Blockchain in Renewable Certificates to Support Sustainability Commitments

Corporations increasingly consider sustainability as an important goal and set net zero carbon emissions targets, consequently looking towards their electricity procurement to achieve them. Guarantees of Origin (GOs) are widely used as insurances for the renewability of electricity supplies and proofs of compliance to renewable standards; however, they suffer from structural problems. Their transactional history cannot distinguish between those traded among market actors and the ones that come directly from power plants, giving rise to transparency issues. Certificate trading dissuades producers from investing in an increase in renewable capacity resulting in lack of additionality while complex frameworks and administrative structures emerge to keep track of the vast network of GOs. These issues can be resolved through the introduction of blockchain networks which can provide transparency and help incentivise renewable investment while increasing automation and process simplification. This review explores the benefits and challenges of blockchain implementation for GOs and proposes a rethinking of how this scheme may fulfil the future needs of the energy sector.
Fluid fermionic fragments for optimizing quantum measurements of electronic Hamiltonians in the variational quantum eigensolver

Measuring the expectation value of the molecular electronic Hamiltonian is one of the challenging parts of the variational quantum eigensolver. A widely used strategy is to express the Hamiltonian as a sum of measurable fragments using fermionic operator algebra. Such fragments have an advantage of conserving molecular symmetries that can be used for error mitigation. The number of measurements required to obtain the Hamiltonian expectation value is proportional to a sum of fragment variances. Here, we introduce a new method for lowering the fragments&amp;apos; variances by exploiting flexibility in the fragments&amp;apos; form. Due to idempotency of the occupation number operators, some parts of two-electron fragments can be turned into one-electron fragments, which then can be partially collected in a purely one-electron fragment. This repartitioning does not affect the expectation value of the Hamiltonian but has non-vanishing contributions to the variance of each fragment. The proposed method finds the optimal repartitioning by employing variances estimated using a classically efficient proxy for the quantum wavefunction. Numerical tests on several molecules show that repartitioning of one-electron terms lowers the number of measurements by more than an order of magnitude.
Human-Computer Interaction System: A Survey of Talking-Head Generation

Virtual human is widely employed in various industries, including personal assistance, intelligent customer service, and online education, thanks to the rapid development of artificial intelligence. An anthropomorphic digital human can quickly contact people and enhance user experience in human–computer interaction. Hence, we design the human–computer interaction system framework, which includes speech recognition, text-to-speech, dialogue systems, and virtual human generation. Next, we classify the model of talking-head video generation by the virtual human deep generation framework. Meanwhile, we systematically review the past five years’ worth of technological advancements and trends in talking-head video generation, highlight the critical works and summarize the dataset.
Evaluation insight into Abu Zenima clay deposits as a prospective raw material source for ceramics industry: Remote Sensing and Characterization

Abstract The rapid development and mutations have heightened ceramic industrialization to supply the countries' requirements worldwide. Therefore, the continuous exploration for new reserves of possible ceramic-raw materials is needed to overwhelm the increased demand for ceramic industries. In this study, the suitability assessment of potential applications for Upper Cretaceous (Santonian) clay deposits at Abu Zenima area, as raw materials in ceramic industries, was extensively performed. Remote sensing data were employed to map the Kaolinite-bearing formation as well as determine the additional occurrences of clay reserves in the studied area. In this context, ten representative clayey materials from the Matulla Formation were sampled and examined for their mineralogical, geochemical, morphological, physical, thermal, and plasticity characteristics. The mineralogical and chemical compositions of starting clay materials were examined. The physicochemical surface properties of the studied clay were studied utilizing SEM–EDX and TEM. The particle-size analysis confirmed the adequate characteristics of samples for white ceramic stoneware and ceramic tiles manufacturing. The technological and suitability properties of investigated clay deposits proved the industrial appropriateness of Abu Zenima clay as a potential ceramic raw material for various ceramic products. The existence of high kaolin reserves in the studied area with reasonable quality and quantity has regional significance. It would significantly help reduce the manufacturing cost and overwhelm the high consumption rate. The ceramic manufacturers in the investigated areas are expected to bring steady producers into the industry in the long term to gain the advantage of low-cost raw materials, labor, and factory construction.
Three-dimensional UCAV path planning using a novel modified artificial ecosystem optimizer

As a challenging high-dimensional multimodal optimization problem, path planning for unmanned combat aerial vehicles (UCAVs) has evolved into a hard optimization problem with multiple objectives and types of constraints in complex operational environments. The traditional approaches lack good search capability in complex multimodal search spaces, resulting in difficulties in providing satisfactory flight paths under multiple constraints. Intelligent optimization algorithms have become the first choice for solving path planning problems because of their excellent global exploration capabilities. Artificial ecosystem optimizer (AEO) is an efficient and intelligent optimization algorithm that has remarkable effectiveness in handling optimization problems. However, it also has the disadvantage of slow convergence. In this work, a modified version of the AEO, named MAEO, is proposed to overcome its shortcomings and apply it to the UCAV path planning problem. In the MAEO algorithm, a new production model that focuses on the current optimal search area is proposed to improve the quality of the producer, thereby effectively guiding consumers to search for the optimal space. Then, an enhanced updating consumption mechanism inspired by animal predation behavior is designed, including two predation operations: one is the introduction of the dynamic elite individual in the original update method to simulate that consumers are also predated by their natural enemies during predation to enhance the convergence speed of the algorithm. The second is a unique spiral predation strategy adopted by the consumers to round up the prey. This mechanism not only effectively avoids the lack of population diversity but also improves the global exploration ability of the algorithm. Furthermore, an adaptive Cauchy mutation strategy is successfully hybridized with the AEO algorithm to boost the ability of the algorithm to escape from the local optimum. Simulation experiments of path planning in a series of complex three-dimensional environments show that the algorithm can plan a path satisfying the constraints stably and efficiently, which proves the superiority of the algorithm.
MSHT: Multi-Stage Hybrid Transformer for the ROSE Image Analysis of Pancreatic Cancer

Pancreatic cancer is one of the most malignant cancers with high mortality. The rapid on-site evaluation (ROSE) technique can significantly accelerate the diagnostic workflow of pancreatic cancer by immediately analyzing the fast-stained cytopathological images with on-site pathologists. However, the broader expansion of ROSE diagnosis has been hindered by the shortage of experienced pathologists. Deep learning has great potential for the automatic classification of ROSE images in diagnosis. But it is challenging to model the complicated local and global image features. The traditional convolutional neural network (CNN) structure can effectively extract spatial features, while it tends to ignore global features when the prominent local features are misleading. In contrast, the Transformer structure has excellent advantages in capturing global features and long-range relations, while it has limited ability in utilizing local features. We propose a multi-stage hybrid Transformer (MSHT) to combine the strengths of both, where a CNN backbone robustly extracts multi-stage local features at different scales as the attention guidance, and a Transformer encodes them for sophisticated global modeling. Going beyond the strength of each single method, the MSHT can simultaneously enhance the Transformer global modeling ability with the local guidance from CNN features. To evaluate the method in this unexplored field, a dataset of 4240 ROSE images is collected where MSHT achieves 95.68% in classification accuracy with more accurate attention regions. The distinctively superior results compared to the state-of-the-art models make MSHT extremely promising for cytopathological image analysis. The codes and records are available at: https://github.com/sagizty/ Multi-Stage-Hybrid-Transformer.
Comparison of BERT implementations for natural language processing of narrative medical documents

Bidirectional Encoder Representations from Transformers (BERT) word embedding models have been successfully used for many natural language processing (NLP) tasks, including medical named entity recognition. However, there are many more linguistically complicated concepts in healthcare documentation, often reflecting medical decision-making processes or complex patient characteristics, where performance of transformer-based models has not been as well investigated. Furthermore, the dataset on which a BERT model has been pre-trained could affect performance. We compared accuracy of identification of three linguistically complex medical concepts – a) discussion of bariatric surgery between patients and their healthcare providers; b) non-acceptance of statin treatment recommendation by patients; and c) tobacco use status documentation – by three BERT implementations: regular BERT; BioBERT and ClinicalBERT. For each of the three NLP tasks, all three BERT implementations were trained on a manually annotated training dataset of outpatient provider notes and then evaluated on a held-out manually annotated test dataset. All datasets were obtained from the electronic health record system of Mass General Brigham. Filtering by keywords was used to improve class balance by undersampling the null class. Prevalence of study labels (concepts) ranged from 1.3% to 11.8% and was similar between training and held-out validation datasets within each task-model combination. Over 80% of NLP tasks achieved recall and 75% of tasks achieved precision between 0.4 and 0.9. Among different study evaluation categories, F1 score ranged from 0.0 to 0.860. Macro-averaged F1 score ranged from 0.466 to 0.854. Overall, ClinicalBERT achieved best performance (by F1-macro score) in the Bariatric Surgery task, BioBERT in the Tobacco Use task and regular BERT in the Statin Non-Acceptance task. The mean macro-F1 score across all task-model pairs was 0.761 for ClinicalBERT, 0.735 for BioBERT and 0.699 for regular BERT. BERT implementations trained on documents from biomedical domain – both BioBERT and ClinicalBERT – achieve superior NLP performance for identifying a range of complex medical concepts compared to regular BERT. Neither of the two biomedical BERT implementations we tested attained clearly greater accuracy than the other.
USIS: Unsupervised Semantic Image Synthesis

Semantic Image Synthesis (SIS) is a subclass of I2I (I2) translation where a photorealistic image is synthesized from a segmentation mask. SIS has mainly been addressed as a supervised problem. However, state-of-the-art methods depend on a massive amount of labeled data and cannot be applied in an unpaired setting. On the other hand, generic unpaired I2I frameworks underperform in comparison. In this work, we propose a new framework, Unsupervised Semantic Image Synthesis (USIS), as a first step towards closing the performance gap between paired and unpaired settings. We design a simple and effective learning scheme that combines the fragmented benefits of cycle losses and relationship preservation constraints. Then, we make the discovery that, contrary to I2I translation, discriminator design is crucial for label-to-image translation. To this end, we design a new discriminator with a wavelet-based encoder and a decoder to reconstruct the real images. The self-supervised reconstruction loss in the decoder prevents the encoder from overfitting on a few wavelet coefficients. We test our methodology on 3 challenging datasets and set a new standard for unpaired SIS. The generated images demonstrate significantly better diversity, quality and multimodality.
Blockchain technology in energy systems: A state‐of‐the‐art review

Abstract Blockchain is a powerful technology to facilitate decarbonization, decentralization, digitalization, and democratization (4D's) of the energy systems of the future. The 4D's are the driving forces of transition into new energy systems that are more sustainable, resilient, efficient, and equitable. Although this technology can be applied to a wide spectrum of applications in the power sector, a set of challenges and limitations still need to be addressed to facilitate a full‐scope implementation in energy systems. This paper presents an overview of blockchain technology from its inception through its most recent evolution and presents a thematic review of state of the art in the application of this technology in power systems. Further, it addresses the barriers preventing the power sector from large‐scale, full‐scope adoption of this technology. Finally, the emerging blockchain trends in the near future will be discussed and its potential to facilitate a secure, decentralized energy trading platform will be investigated.
Evaluation of automatic discrimination between benign and malignant prostate tissue in the era of high precision digital pathology

Prostate cancer is a major health concern in aging men. Paralleling an aging society, prostate cancer prevalence increases emphasizing the need for efficient diagnostic algorithms.Retrospectively, 106 prostate tissue samples from 48 patients (mean age, [Formula: see text] years) were included in the study. Patients suffered from prostate cancer (n = 38) or benign prostatic hyperplasia (n = 10) and were treated with radical prostatectomy or Holmium laser enucleation of the prostate, respectively. We constructed tissue microarrays (TMAs) comprising representative malignant (n = 38) and benign (n = 68) tissue cores. TMAs were processed to histological slides, stained, digitized and assessed for the applicability of machine learning strategies and open-source tools in diagnosis of prostate cancer. We applied the software QuPath to extract features for shape, stain intensity, and texture of TMA cores for three stainings, H&E, ERG, and PIN-4. Three machine learning algorithms, neural network (NN), support vector machines (SVM), and random forest (RF), were trained and cross-validated with 100 Monte Carlo random splits into 70% training set and 30% test set. We determined AUC values for single color channels, with and without optimization of hyperparameters by exhaustive grid search. We applied recursive feature elimination to feature sets of multiple color transforms.Mean AUC was above 0.80. PIN-4 stainings yielded higher AUC than H&E and ERG. For PIN-4 with the color transform saturation, NN, RF, and SVM revealed AUC of [Formula: see text], [Formula: see text], and [Formula: see text], respectively. Optimization of hyperparameters improved the AUC only slightly by 0.01. For H&E, feature selection resulted in no increase of AUC but to an increase of 0.02-0.06 for ERG and PIN-4.Automated pipelines may be able to discriminate with high accuracy between malignant and benign tissue. We found PIN-4 staining best suited for classification. Further bioinformatic analysis of larger data sets would be crucial to evaluate the reliability of automated classification methods for clinical practice and to evaluate potential discrimination of aggressiveness of cancer to pave the way to automatic precision medicine.