Reviewerly/exHarmony huggingface dataset:
Quadratic optimization for the hyper-parameter based on maximum entropy search

The method based on entropy was used for the Bayesian optimization. Based on compelling information theory, Entropy Search (ES) and Predictive Entropy Search (PES) maximized information about the unknown function when the loss function reaches the maximum value. However, both methods were plagued by complicated calculations for estimating entropy. The most important motivation of this article is to improve and modularize the entropy search itself, making this method more flexible and effective for model adaptation. After the initial optimization and pruning module processing, a reasonable initial configuration for the complex model was successfully established, further reducing the space required for secondary optimization hyper-parameter search. The advantage of this method is that, on the one hand, the basic method of Bayesian optimization is used to get the best result of the iteration, while ensuring that the algorithm has theoretical boundedness. On the other hand, through the maximum entropy, the information features of the original space and data set are retained as much as possible to reduce the loss of information due to the initialization process, so as to improve the precision of the secondary optimization of the model. Further, a new algorithm framework is proposed, integrating MES and Sequential Model-Based Optimization (SMBO). With MES as the final module of the whole optimization process, a more accurate and reasonable algorithmic model was built, which lays a solid mathematical basis for the final empirical analysis.
Infrared Thermal Imaging Face Expression Recognition Based on Harris Algorithm

In the field of human–computer interaction, there is a trend toward using emotional interaction and putting emotions at the center. However, traditional facial expression recognition is greatly affected by lighting, skin color and make-up, and is not well recognized. This paper addresses these shortcomings in the following areas: (1) The algorithm of infrared face feature localization is studied, using a combination of region growing algorithm to highlight the organ features of infrared faces, and the Harris corner point detection algorithm to detect the organ features of human beings and locate the location of the large organs related to expressions, such as eyes, nose and mouth. (2) The algorithm for feature extraction is investigated, and the local binarization LBP algorithm is proposed to extract the texture features of the infrared face. The LBP algorithm is synthesized to extract the overall LBP features by applying the innovation of chunking and layering of features. By analyzing and processing the infrared face image data from both visible and infrared face expression data, feature detection, feature extraction and effective classification of the four basic face expressions — happy, angry, disgusted and sad — are performed on the infrared images. The application of the algorithm is processed and improved accordingly to the characteristics of infrared face images, which improves the accuracy of recognition and classification compared to traditional methods.
Dual attention guided multi-scale fusion network for RGB-D salient object detection

While recent research on salient object detection (SOD) has shown remarkable progress in leveraging both RGB and depth data, it is still worth exploring how to use the inherent relationship between the two to extract and fuse features more effectively, and further make more accurate predictions. In this paper, we consider combining the attention mechanism with the characteristics of the SOD, proposing the Dual Attention Guided Multi-scale Fusion Network. We design the multi-scale fusion block by combining multi-scale branches with channel attention to achieve better fusion of RGB and depth information. Using the characteristic of the SOD, the dual attention module is proposed to make the network pay more attention to the currently unpredicted saliency regions and the wrong parts in the already predicted regions. We perform an ablation study to verify the effectiveness of each component. Quantitative and qualitative experimental results demonstrate that our method achieves state-of-the-art (SOTA) performance.
Methods for collecting data on participants of online programs of additional professional education in universities

The article discusses in detail the main methods for collecting data on the participants of online programs of additional professional education (AEP) in universities, which were developed by the authors. The authors used personality-oriented, environmental, product, and data-driven approaches as the basic ones in developing these methods. The authors also took into account the main statements of evidence-based education, learning analytics, and the types of learning data (the LOTS model). The new methodological approaches presented in this article have practical value at various stages of designing and implementing training programs based on the obtained data with the help of feedback, evaluation and learning metrics. The results were tested in small groups of students (12–20 participants) as part of the implementation of programs and courses such as “Assessment and Feedback”, “Fundamentals of Pedagogical Design for Distance and Blended Learning”, and the Summer School for teachers and trainers “Data-Driven Learning Design”. A total of 146 participants were trained using the methods outlined in this article. The results of this study may be of interest to educational organization administrators that provide AEP. They can focus on developing an educational system based on data, designing programs based on current educational needs, improving training effectiveness with valid metrics, and competing with Russian and foreign EdTech companies. Additionally, the findings could be useful for professors implementing educational programs of higher education, instructional designers, and educational product developers.
Two classes of quantum codes from almost MDS codes

Hermitian dual-containing codes are an important class of linear codes which have important applications in the construction of quantum codes. In this paper, two classes of Hermitian dual-containing almost MDS codes over finite fields are studied. By employing the Hermitian construction, a class of quantum codes with minimum distance [Formula: see text] and a class of quantum codes with minimum distance [Formula: see text] are constructed.
Transformation of IT Higher Education Through Corporate Education

In this chapter the process of conversion of regular higher education in IT-field towards corporate education is shown. The steps of the cooperation between a university and an IT-company in the field of teaching students were analyzed. In this chapter, the advantages of a new cooperation are pointed out. The problems of this cooperation were discussed in the article. There are some issues in IT sector of economy. Students who have an education in computer science want to get an IT-job. But many of them are faced with a problem of labor adaptation when they are getting a job at the first time. That means that during labor adaptation an employee is not making money for an employer but is bothering colleagues to get more knowledge which is needed to work. Some employers want to make labor adaptation time shorter for students who are getting job according to their specialization at the first time.
Artificial intelligence‐based blockchain solutions for intelligent healthcare: A comprehensive review on privacy preserving techniques

Abstract While blockchain technology (BT) is considered secure, there are several vulnerabilities that can breach its security. The study in artificial intelligence (AI) and BT is widely popular due to its expanding importance in enhancing security and computational prowess. In this study, we present a comprehensive and meticulous comprehensive review of AI and BT‐based privacy‐preserving smart healthcare. The selection for this study was based on a holistic and integrated approach which involved examining not only individual studies but also their relationships, and trends. Through a systematic analysis of various phases, we identified 91 primary studies pertaining to information needed to stockpile directions called for retorting the research queries. We have undertaken a descriptive comparison of foundational manuscripts, taking into account an array of essential factors, including performance metrics, security protocols, and computational prowess. Our thorough discussions and debates have led to the identification of research gaps in the current manuscript, as well as the direction for future research. We also propose our constructive approach for the aforementioned integration, highlighting its potential benefits and implications.
A conditioned reflex action embedded associative context learning‐based energy efficient paradigm in smart city milieu

Abstract An intelligent video surveillance system is crucial to enhance public safety, crime prevention, traffic, and crowd management in a smart city milieu. Situational awareness is an essential aspect of these surveillance systems and it is inferred through underlying context aware frameworks. However, these systems may not possess the ability to proactively disseminate the real‐time context among its sensor nodes. Moreover, in the specific conditions of occurrence of related or repeated events, these systems may also perform inefficiently through afresh context processing and disseminate cycles, without learning from the relevant context that has already been occurred and processed by the system. It leads to deteriorated performance, especially delay in reaction, overwhelmed processing, and energy expenditures. Therefore, to counter such issues, this research work proposes an energy efficient situational aware framework deployed in visual sensors network that is incorporated with context associative learning. System observes currently occurring context at each instance of an event. Overtime, context is refined and stored in context database. Such mechanism empowers the system to learn from previous experiences and develop relationship among the subsequent events that is embedded through this associative (adaptive) learning. Eventually, each event is processed through intelligent resource allocation, supported through mechanism of context learning that further illustrates the independent functions of reduced processing and improved (rapid) decision making resulting in evolution of energy efficient computing paradigm. Ultimately, the capability of learned reflex‐action is induced through introspectively evolved context of the system in entirety and against specific condition of recurred situation depicting minimum energy expenditure.
Concept drift detection and adaption framework using optimized deep learning and adaptive sliding window approach

Abstract Concept drift in online streaming data is a common issue due to dynamic smart systems, which results in system failure or performance degradation. Though there are several traditional approaches for handling the streaming data, they failed to handle the concept drift imposing the need for developing an adaptable approach to managing the dynamic IoT streaming data. Therefore, in this research, a new method is proposed for handling the concept drift issues in online data streaming. This research develops the dynamic streaming data analytic framework based on the optimized Deep CNN and optimized adaptive and sliding window (OASW) approach that effectively addresses both memory and time constraints. An optimized Deep CNN classifier is employed as a base classifier for offline learning, which is developed through hybridizing the proposed Desale's aggressive hunt optimization (AHO) algorithm with a Deep CNN classifier for tuning the optimal parameters of the classifier. An optimized adaptive and sliding window is utilized in this research to adapt the pattern changes in the data streams, which effectively handles the concept drift. The experimental analysis reveals that the proposed methods outperform the conventional methods considered for the analysis in terms of specificity, sensitivity, accuracy, F 1 score, and the precision score of 96.65%, 97.77%, 98.63%, 98.1487%, and 98.4469%, respectively.
Incremental Learning-Based Algorithm for Anomaly Detection Using Computed Tomography Data

In a nuclear power plant (NPP), the used tools are visually inspected to ensure their integrity before and after their use in the nuclear reactor. The manual inspection is usually performed by qualified technicians and takes a large amount of time (weeks up to months). In this work, we propose an automated tool inspection that uses a classification model for anomaly detection. The deep learning model classifies the computed tomography (CT) images as defective (with missing components) or defect-free. Moreover, the proposed algorithm enables incremental learning (IL) using a proposed thresholding technique to ensure a high prediction confidence by continuous online training of the deployed online anomaly detection model. The proposed algorithm is tested with existing state-of-the-art IL methods showing that it helps the model quickly learn the anomaly patterns. In addition, it enhances the classification model confidence while preserving a desired minimal performance.
An efficient deep learning algorithm for the segmentation of cardiac ventricles

Abstract For the effective diagnosis of cardio vascular disease (CVD), anatomical characteristics of the heart must be examined, which depends on segmenting the cardiac tissues of interest and then classifying them into appropriate pathological groups. In recent years, deep learning (DL)‐based computer aided design (CAD) segmentation has been employed to automate the segmentation process. Despite the evolution of several DL methods, they still fail due to the shape variation of the heart in patients and the availability of a limited amount of data. This paper proposes an effective Saliency and Active Contour‐based Attention UNet3+ algorithm to segment the ventricles of the heart, which is a challenging task for most researchers, especially with an irregularly shaped right ventricle (RV) that varies over cardiac phases. The algorithm outperforms other state‐of‐the‐art methods in DC metrics, which proves its efficiency in automating the segmentation process.
Online Robotics Activities During the Pandemic Period – Challenges and Experiences

The situation caused by the Covid-19 made it impossible to keep our traditional inperson summer camps and activities, so our university decided to hold the sessions online. Thus, enthusiastic students aged 13–16 were able to participate in robotics sessions that were fully implemented in the online space. In the article, we want to present the experiences and challenges of these sessions, as well as the way of implementation and the range of tools needed for it.
BU-DLNet: Breast Ultrasonography-Based Cancer Detection Using Deep-Learning Network Selection and Feature Optimization

Early detection of breast lesions and distinguishing between malignant and benign lesions are critical for breast cancer (BC) prognosis. Breast ultrasonography (BU) is an important radiological imaging modality for the diagnosis of BC. This study proposes a BU image-based framework for the diagnosis of BC in women. Various pre-trained networks are used to extract the deep features of the BU images. Ten wrapper-based optimization algorithms, including the marine predator algorithm, generalized normal distribution optimization, slime mold algorithm, equilibrium optimizer (EO), manta-ray foraging optimization, atom search optimization, Harris hawks optimization, Henry gas solubility optimization, path finder algorithm, and poor and rich optimization, were employed to compute the optimal subset of deep features using a support vector machine classifier. Furthermore, a network selection algorithm was employed to determine the best pre-trained network. An online BU dataset was used to test the proposed framework. After comprehensive testing and analysis, it was found that the EO algorithm produced the highest classification rate for each pre-trained model. It produced the highest classification accuracy of 96.79%, and it was trained using only a deep feature vector with a size of 562 in the ResNet-50 model. Similarly, the Inception-ResNet-v2 had the second highest classification accuracy of 96.15% using the EO algorithm. Moreover, the results of the proposed framework are compared with those in the literature.
Brief Paper: Knowledge-Based Post-Processing of On-Line Hangeul Short-HandWriting Recognition

In this paper we propose a technique of correcting online handwriting recognition errors using morphemic lexicons and a set of confusion lists for mistaken characters collected from classification errors.Given a string of Hangeul characters returned by a baseline recognizer, we raise a set of alternative characters out of a confusion dictionary, one for each character in the returned string.Then we conduct a post-processing via a dynamic programming-based lattice search for a sequence of legal words while consulting morphemic lexicons.The proposed methods can handle short broken phrases in natural online handwriting of sentences.Experiment shows that, for handwriting strings of 3.09 characters long on average, the proposed method improved the phrase recognition accuracy from 55.43% before post-processing to 86.63%, an error reduction of 70.00%, with single hypotheses.
Evaluation of Heuristics for Taken’s Theorem Hyper-Parameters Optimization in Time Series Forecasting Tasks

This study compares three methods for optimizing the hyper-parameters m (embedding dimension) and τ (time delay) from Taken’s Theorem for time-series forecasting to train a Support Vector Regression system (SVR). Firstly, we use a method which utilizes Mutual Information for optimizing τ and a technique referred to as “Dimension Congruence” to optimize m. Secondly, we employ a grid search and random search, combined with a cross-validation scheme, to optimize m and τ hyper-parameters. Lastly, various real-world time series are used to analyze the three proposed strategies.
Spatially resolved generation profiles for building, land and water-bound PV: a case study of four Dutch energy transition scenarios

Abstract. Alongside a transition from steerable and centralized traditional electricity generation to intermittent and more decentralized renewable electricity generation from solar panels and wind turbines, Dutch energy transition scenarios project a widespread deployment of heat pumps and electric vehicles towards 2050. While clearly contributing to the decarbonization of the Dutch energy system, these developments impose challenges regarding electricity supply-demand mismatch and grid congestion. Spatially resolved electricity demand and supply profiles are required to gain a better insight into where and when such problems are likely to occur within the different scenarios. The present paper focuses on Dutch solar energy supply and features the construction of geodatabases of scenario-specific, spatially resolved electricity generation profiles for building, land and water-bound PV. Country-level PV capacities are geographically distributed based on spatial variance in roof PV potential and availability of suitable land and water use areas. Corresponding electricity generation profiles are constructed using historical meteorological measurements, a diffuse fraction model and a anisotropic transposition model. Empirically found performance ratio profiles are applied to account for a multitude of performance loss factors, including shading, dust and inverter efficiency. In 2050, building-bound capacity is projected to show only limited overlap with both land-bound and water-bound PV capacity. On the other hand, regions with considerable water-bound PV capacity also tend to show considerable land-bound PV capacity. Compared to the present-day situation, yearly country-level PV electricity generation is projected to be a factor 18.5, 15.7, or 7.7 higher in 2050 when respectively following the Regional, National or International Steering scenarios.
AEA-Net:Affinity-supervised entanglement attentive network for person re-identification

Most existing person re-identification algorithms prioritize the extraction of effective and salient local features while neglecting the affinity between local and adjacent features. This phenomenon will cause misidentification when different people have similar local features. To address this issue, we introduce the AEA-Net, which emphasizes the affinity between the local features of a single image. Specifically, three important components are proposed. The affinity-supervised attention module (ASA) centers on adjacent features and utilizes the affinity between global and adjacent features to supervise the learning of attention. The affinity relationship module (AR) focuses on constructing relationship features between local and adjacent features to enhance the closeness between local features. The tangle hybrid loss (THL) makes the final predictions have a distinct weight profile. Extensive experiments quantitatively and qualitatively demonstrate that our method outperforms the state-of-the-art approaches.
Temporal Facial Alignment with Triple Discriminators

Facial alignment is one of the most important features of 3D facial models.However, when fitting the 3D Morphable Model (3DMM) into a large expression or pose facial image, there is ambiguity as to whether facial shape deformation is caused by identity or expression.To address this, in this paper, we propose a stable and accurate facial alignment framework by introducing multiple stability discriminators.The proposed discriminators determine the regressed camera, face identity, and expression parameters simultaneously from an image.The proposed framework for facial alignment consists of a facial alignment network and stability discriminators: identity, expression, and temporal discriminators.The facial alignment network is trained to predict camera, face identity, and expression parameters based on an image.The stability discriminator is trained to distinguish whether the facial deformation generated by the estimated facial identity and expression parameters is stable.Meanwhile, the discriminator distinguishes whether the deformation between adjacent frames is consistent.By utilizing these stability discriminators, the proposed facial alignment network demonstrates precise and consistent performance in aligning faces in both static and dynamic domains.To verify the performance of the proposed discriminators, the large-scale facial tracking dataset, 300 Videos in the Wild (300VW) dataset, is used for qualitative and quantitative evaluations.The experimental results show that the proposed method outperforms state-of-the-art methods, demonstrating the strong benefits of our method in accurate facial alignment over time.