Reviewerly/exHarmony huggingface dataset:
Combined proliferation and apoptosis index provides better risk stratification in breast cancer

Aims Breast cancer (BC) risk stratification is critical for predicting behaviour and guiding management decision‐making. Despite the well‐established prognostic value of cellular proliferation in BC, the interplay between proliferation and apoptosis remains to be defined. In this study, we hypothesised that the combined proliferation and apoptosis indices can provide a more accurate in‐vivo growth rate measure and a precise prognostic predictor. Methods and results Apoptotic and mitotic figures were counted in whole slide images (WSI) generated from haematoxylin and eosin‐stained sections of 1545 BC cases derived from two well‐defined BC cohorts. Counts were carried out visually within defined areas. There was a significant correlation between mitosis and apoptosis scores. High apoptotic counts were associated with features of aggressive behaviour, including high grade, high pleomorphism score and hormonal receptor negativity. Although the mitotic index (MI) and apoptotic index (AI) were independent prognostic indicators, the prognostic value was synergistically higher when combined. BC patients with a high combined AI and MI had the shortest survival. Replacing the mitosis score with the mitosis–apoptosis index in the Nottingham grading system revealed that the modified grade with the new score had a higher significant association with BC‐specific survival with a higher hazard ratio. Conclusion Apoptotic figures count provides additional prognostic value in BC when combined with MI; such a combination can be implemented to assess the behaviour of BC and provides an accurate prognostic indicator. This can be considered when using artificial intelligence algorithms to assess proliferation in BC.
A Mixture Autoregressive Model Based on an Asymmetric Exponential Power Distribution

In nonlinear time series analysis, the mixture autoregressive model (MAR) is an effective statistical tool to capture the multimodality of data. However, the traditional methods usually need to assume that the error follows a specific distribution that is not adaptive to the dataset. This paper proposes a mixture autoregressive model via an asymmetric exponential power distribution, which includes normal distribution, skew-normal distribution, generalized error distribution, Laplace distribution, asymmetric Laplace distribution, and uniform distribution as special cases. Therefore, the proposed method can be seen as a generalization of some existing model, which can adapt to unknown error structures to improve prediction accuracy, even in the case of fat tail and asymmetry. In addition, an expectation-maximization algorithm is applied to implement the proposed optimization problem. The finite sample performance of the proposed approach is illustrated via some numerical simulations. Finally, we apply the proposed methodology to analyze the daily return series of the Hong Kong Hang Seng Index. The results indicate that the proposed method is more robust and adaptive to the error distributions than other existing methods.
Type Hierarchy Enhanced Event Detection without Triggers

Event detection (ED) aims to detect events from a given text and categorize them into event types. Most of the current approaches to ED rely heavily on the human annotations of triggers, which are often costly and affect the application of ED in other fields. However, triggers are not necessary for the event detection task. We propose a novel framework called Type Hierarchy Enhanced Event Detection Without Triggers (THEED) to avoid this problem. More specifically, We construct a type hierarchy concept module using the external knowledge graph Probase to enhance the semantic representation of event types. In addition, we divide input instances into word-level and context-level representations, which can make the model use different level features. The experimental result indicates that our proposed approach achieves better improvement. Additionally, it is significantly competitive with mainstream trigger-based models.
Methodika for the formation of the concept of "Function" in the course of algebra of the basic school in the context of the system-activity approach

The article substantiates the author's concept of creating and using an elective course for the organization of educational interaction of participants in the educational process when teaching mathematics. The author's elective course on the topic &amp;quot;Functions and their graphs&amp;quot; is presented, which allows students to develop the ability to logical thinking and communication, the ability to generalize and systematize information and material, as well as create conditions for students' design and research activities. The application of the methodological recommendations formulated by the authors will allow mathematics teachers to increase the level of motivation of students, as well as to eliminate gaps in students' knowledge in time when studying the topic &amp;quot;Function&amp;quot;.
Methodical of forming self-control in students of grades 5-6 when studying numbers

The article presents an overview of methodological approaches to the formation of self-control among students of grades 5-6 in the study of mathematics. The analysis of the state of this issue showed that in the works of A.A. Krylov, S.D. Yakushev and G.K. Selevko, etc., the importance of self-control within the framework of organizing independent work in the classroom and in the works of A.G. Mordkovich, Yu.M. Kolyagin, etc. in general, when studying mathematics on the example of a numerical line is indicated. It is proved that one of the means of forming self-control among students of grades 5-6 are assessment sheets, which include the objectives of studying the subject content, a route sheet for the lesson with the stages of academic work, a system of reflexive assessments.
Light Field Image Super-Resolution Using Deep Residual Networks on Lenslet Images

Due to its widespread usage in many applications, numerous deep learning algorithms have been proposed to overcome Light Field's trade-off (LF). The sensor's low resolution limits angular and spatial resolution, which causes this trade-off. The proposed method should be able to model the non-local properties of the 4D LF data fully to mitigate this problem. Therefore, this paper proposes a different approach to increase spatial and angular information interaction for LF image super-resolution (SR). We achieved this by processing the LF Sub-Aperture Images (SAI) independently to extract the spatial information and the LF Macro-Pixel Image (MPI) to extract the angular information. The MPI or Lenslet LF image is characterized by its ability to integrate more complementary information between different viewpoints (SAIs). In particular, we extract initial features and then process MAI and SAIs alternately to incorporate angular and spatial information. Finally, the interacted features are added to the initial extracted features to reconstruct the final output. We trained the proposed network to minimize the sum of absolute errors between low-resolution (LR) input and high-resolution (HR) output images. Experimental results prove the high performance of our proposed method over the state-of-the-art methods on LFSR for small baseline LF images.
Univariate machine learning models applied in photovoltaic power prediction using Python

Photovoltaic field has attracted the attention of researchers around the world because of the intermittence of this energy since its integration into the electricity grid poses problems in maintaining the balance between consumption and production. In fact, forecasting PV (photovoltaic) energy sources would make it possible to anticipate the availability of production sources and thus facilitate network management. Indeed, the PV production depends on many parameters such as the meteorological parameters, irradiance, and installation conditions of PV modules. However, this study concerns the prediction of photovoltaic production using machine learning techniques applied for the first time on the Beni Mellal’s photovoltaic station. These methods make it possible to predict the production of photovoltaic electricity by programming a computer to learn from previous data of the parameters on which the photovoltaic production depends and then to predict future ones, that is to say direct forecasting. First, a three-year meteorological parameter (including just ambient temperature and irradiance) and panel parameters (panel temperature) database is used to train the algorithm and a three different months database to test it; all, using supervised learning models based on three approaches – programmed by Python: Support Vector Regression, Decision Tree Regression, and Random Forest. These approaches aim in first time to analyze the correlation between meteorological parameters and photovoltaic production and then, in other time, to predict PV production directly. The results have shown that the global inclined irradiance and the panel temperature are the data most correlated with the photovoltaic production with a correlation coefficient of 0.88 and 0.61, respectively, so that led us to rely, on the first level, on global inclined irradiance and panel temperature, and then on other meteorological data, on the second level to predict PV output. It is also concluded in this study that Random Forest was the best used approach with a mean square error of 0.18.
Morph-Rec: A Novel Computer-Aided Liver Segmentation Model based on Morphological Reconstruction Operation

An abdominal Computed Tomography (CT) scan gives more information about diseases of the liver, gallbladder, and biliary tract. In the image processing approach, liver segmentation is an essential step to be done before liver lesion detection. Liver segmentation removes the unwanted regions from the CT image and makes the task of lesion detection easier. In this paper, a novel Morph-Rec model based on morphological reconstruction operation is proposed for liver segmentation from CT images. The proposed work is focused on segmenting the liver region from the CT slices irrespective of the size and shape of the liver region. The proposed model is validated on 2650 CT slices of 120 and 20 CT scans from the LITS and 3DIRCADb datasets, respectively. The proposed Morph-Rec method is evaluated using metrics such as dice score, accuracy, F1 score, Jaccard index and Matthew’s correlation coefficient. To justify the adaptability and efficiency of the proposed model, it is also validated on 50 CT slices of nine CT scans provided by a local scan centre. The proposed method has produced excellent results on all metrics and the obtained results are better than the state-of-the-art conventional methods for liver segmentation. Hence, the proposed technique is an automatic and dataset-generic model that can perform liver segmentation precisely on any CT acquisition of the liver.
Big Data Classification Using Enhanced Dynamic KPCA and Convolutional Multi-Layer Bi-LSTM Network

Over the last few years, Big Data has sparked a lot of interest in a variety of engineering and scientific fields. Despite its numerous advantages, big data presents several issues such as big data management, big data analytics, big data security, and privacy which must be addressed to improve service quality. The proposed method focuses on data preprocessing, feature extraction, and classification. At first, data preprocessing has been performed and the weights have been assigned by the automated weight assignment model. In addition, the feature extraction model applies Principal Component Analysis (PCA) which employs feature correlation at the initial level, with best Information Gain (IG) and time-series data's similarity search with Kernel Dynamic Time Wrapping (DTW) method. On this basis, an Enhanced dynamic KPCA algorithm is introduced by integrating the kernel trick, DTW algorithm, PCA, and information gain. Then Convolutional multi-layer Bi-LSTM algorithms have been applied for classification. To analyze the big data performance, 8 types of datasets such as 32-pendigits, Bank-Marketing, Click Prediction, EEG, Electricity-normalized, Jm1, Magic telescope, and Amazon employee access are used. The performances are evaluated by different metrics with existing methods of LSTM, BRNN, MFC, and DL methods.
Increased Performance of Transformers Language Models in Information Question and Response Systems

Purpose of research. The purpose of this work is to increase the performance of question and response information systems in Russian. Scientific novelty of the work is to increase the performance for RuBERT model, which was trained to find the answer to the question in the text. As far as a more efficient language model allows more requests to be processed in the same time, the results of this work can be used in various information question and response systems for which response speed is important. Methods. The present work uses methods of processing natural language, machine learning, reducing the size of artificial neural networks. The language model was configured and trained using Torch and Onnxruntime machine learning libraries. The original model and training dataset were taken from the Huggingface Library. Results. As a result of the study, the performance of RuBERT language model was increased using methods to reduce the size of neural networks, such as distillation of knowledge and quantization, as well as by exporting the model to ONNX format and running it in ONNX runtime. Conclusion. As a result, the model, to which knowledge distillation, quantization and ONNX optimization were simultaneously applied, received a performance increase of ~ 4.6 times (from 66.57 to 404.46 requests per minute), while the size of the model decreased ~ 13 times (from 676.29 MB to 51.66 MB). The downside of obtained performance was EM deterioration (from 61.3 to 56.87) and F-measure (from 81.66 to 76.97).
Assessing Carbon Reduction Potential of Rooftop PV in China through Remote Sensing Data-Driven Simulations

Developing rooftop photovoltaic (PV) has become an important initiative for achieving carbon neutrality in China, but the carbon reduction potential assessment has not properly considered the spatial and temporal variability of PV generation and the curtailment in electricity dispatch. In this study, we propose a technical framework to fill the gap in assessing carbon reduction potential through remote sensing data-driven simulations. The spatio-temporal variations in rooftop PV generations were simulated on an hourly basis, and a dispatch analysis was then performed in combination with hourly load profiles to quantify the PV curtailment in different scenarios. Our results showed that the total rooftop PV potential in China reached 6.5 PWh yr−1, mainly concentrated in the eastern region where PV generation showed high variability. The carbon reduction from 100% flexible grids with 12 h of storage capacity is close to the theoretical maximum, while without storage, the potential may be halved. To maximize the carbon reduction potential, rooftop PV development should consider grid characteristics and regional differences. This study has important implications for the development of rooftop PV and the design of carbon-neutral pathways based on it.
Low-False-Alarm-Rate Timing and Duration Estimation of Noisy Frequency Agile Signal by Image Homogeneous Detection and Morphological Signature Matching Schemes

Frequency hopping spread spectrum (FHSS) applies widely to communication and radar systems to ensure communication information and channel signal quality by tuning frequency within a wide frequency range in a random sequence. An efficient signal processing scheme to resolve the timing and duration signature from an FHSS signal provides crucial information for signal detection and radio band management purposes. In this research, hopping time was first identified by a two-dimensional temporal correlation function (TCF). The timing information was shown at TCF phase discontinuities. To enhance and resolve the timing signature of TCF in a noisy environment, three stages of signature enhancement and morphological matching processes were applied: first, computing the TCF of the FHSS signal and enhancing discontinuities via wavelet transform; second, a dual-diagonal edge finding scheme to extract the timing pattern signature and eliminate mismatching distortion morphologically; finally, Hough transform resolved the agile frequency timing from purified line segments. A grand-scale Monte Carlo simulation of the FHSS signals with additive white Gaussian noise was carried out in the research. The results demonstrated reliable hopping time estimation obtained in SNR at 0 dB and above, with a minimal false detection rate of 1.79%, while the prior related research had an unattended false detection rate of up to 35.29% in such a noisy environment.
Method for Restoring Audio Signal Using Convolutional Neural Networks

This paper describes a research of the restoring distorted audio signal possibility. Based on the previously obtained results of using deep machine learning methods, the concept of a neural network to correct a distorted audio signal has been developed. On the basis of the originally obtained results, several new neural network architectures were developed, focused on the audio signal restoring. The paper contains descriptions of the developed architectures with a theoretical substantiation of the possibility of their application. The presented architectures were tested to solve the problem of restoring the part of a specifc instrument in a musical composition where it was removed. The results of testing the developed architectures of neural networks are presented in several forms.
Extracting Semantic Relations from the Texts of Scientifc Articles

Nowadays, the number of scientifc publications existing in the form of electronic text is constantly growing. As a result, the tasks related to the text processing of scientifc articles become especially actual. This paper is dedicated to the task of extracting semantic relations between entities from the texts of scientifc articles in Russian, where we consider scientifc terms as entities. Relation extraction can be useful in some specialized areas, such as searching and question-answering systems, as well as in the compilation of ontologies. In our work, we have created a corpus of scientifc texts consisting of 136 abstracts of scientifc articles in Russian, in which 353 relations of the following types were highlighted: USAGE, ISA, TOOL, SYNONYMS, PART_OF, CAUSE. This corpus was used to train the machine learning models. In addition, we have implemented the automatic semantic relation extraction algorithm and tested it on the already existing corpus RuSERRC. The neural network model BERT was used to implement the algorithm. We’ve done a number of experiments using vectors derived from diﬀerent language models, as well as two neural network architectures. The developed tool and the annotated corpus are publicly available and can be useful for other researchers.
Development of the Information Environment of SB RAS under Sanctions and Information Warfare against the Russian Federation

The purpose of the work is to develop the information environment of the Siberian Branch of the Russian Academy of Sciences (SB RAS). The information environment of SB RAS has begun to take shape since the end of the 1990s with the creation of a corporate website, websites of institutes and the NSU website. Now the information environment of SB RAS unites more than 100 scientifc organizations and universities of Siberia, serves as a platform where the scientifc and technical potential of the East of Russia is presented. Because of the recently introduced sanctions and the information warfare against Russia, some urgent tasks have arisen – in particular, to replace the information systems with open data such as Figshare and Zenodo. The article proposes to present a catalog of domestic open data databases on the SB RAS Portal. As an example, the section “Import-independence” that was created in May 2022 and contains open data is presented.
ICTs, Crime and Human Rights

As modern information and communication technologies become pervasive in Africa, transforming lives and societies, their criminal usage has also become widespread and a growing concern. This chapter tracks the rising usage of information and communications technologies (ICTs) and the corresponding spread of cybercrime in the African region. It further outlines efforts to tackle cybercrime at regional, sub-regional (with a focus on SADC) and domestic levels. The chapter highlights how cybercrime and efforts to fight it implicate human rights and fundamental freedoms. Apart from it being a new form of criminality, cybercrime also has the potential to erode people's confidence in ICTs, thereby causing low technological uptake and usage, leading to further exclusion of populations that have faced historical marginalisation from technology and development. Legislative and law enforcement measures to combat cybercrime may also undermine fundamental rights and freedoms. Already, some of the substantive and procedural criminal laws adopted by some African countries have drawn criticism from a human rights perspective.
Dynamic Personalized POI Sequence Recommendation with Fine-Grained Contexts

The Point Of Interest (POI) sequence recommendation is the key task in itinerary and travel route planning. Existing works usually consider the temporal and spatial factors in travel planning. However, the external environment, such as the weather, is usually overlooked. In fact, the weather is an important factor because it can affect a user’s check-in behaviors. Furthermore, most of the existing research is based on a static environment for POI sequence recommendation. While the external environment (e.g., the weather) may change during travel, it is difficult for existing works to adjust the POI sequence in time. What’s more, people usually prefer the attractive routes when traveling. To address these issues, we first conduct comprehensive data analysis on two real-world check-in datasets to study the effects of weather and time, as well as the features of the POI sequence. Based on this, we propose a model of Dynamic Personalized POI Sequence Recommendation with fine-grained contexts ( DPSR for short). It extracts user interest and POI popularity with fine-grained contexts and captures the attractiveness of the POI sequence. Next, we apply the Monte Carlo Tree Search model (MCTS for short) to simulate the process of recommending POI sequence in the dynamic environment, i.e., the weather and time change after visiting a POI. What’s more, we consider different speeds to reflect the fact that people may take different transportation to transfer between POIs. To validate the efficacy of DPSR , we conduct extensive experiments. The results show that our model can improve the accuracy of the recommendation significantly. Furthermore, it can better meet user preferences and enhance experiences.