Reviewerly/exHarmony huggingface dataset:
CADPP: An Effective Approach to Recommend Attentive and Diverse Long-tail Items

As the long-tail items are widely seen in various recommendation related applications, e.g., E-commerce and music recommendation, the long-tail recommendation consequently becomes an important research issue attracting both academic and industrial attentions. Apparently, it is a very challenging practical issue and the corresponding key challenges to address this task is to find the long-tail items which best match users’ preferences but are sufficiently diverse to avoid recommending similar long-tail items. To address this issue, this paper proposes a novel long-tail item recommendation approach which is based on the multi-pointer co-attention mechanism and the determinant point process (abbreviated as CADPP). Specifically, we design the multi-pointer co-attention mechanism for extracting important feature embeddings to capture the common characteristics of multiple items clicked by the users. We also employ the determinant point process (DPP) to allow diverse long-tail items but are relevant to the target items. To evaluate the model performance, extensive experiments have been performed on two real-world datasets. The promising results have demonstrated that the proposed CADPP is superior to both baseline and the state-of-the-art approaches with respect to the widely adopted evaluation metrics.
Blended learning in lifelong adult education in the aspects of covid-19 epidemical restriction

Digital skills are one of the most important skills that were highlighted in the times of COVID-19, including areas like landscape architecture. COVID-19 pandemic raised possibilities for blended-learning in adulteducation that were not used much before the pandemic. Data shows that Latvia’s inhabitants digital skills compared to the EU average is much lower and there is a need for innovative ways to attract lifelong learners to participate in skills’ advancement courses. At the end of 2020, a survey was conducted with the aim of the survey to find out the society's self-assessment of digital skills and the need to improve them for remote working. Results showed that a high number of respondents are willing to participate in blended learning courses and are eager to develop their digital skills.
Determining the lithogeochemical background concentrations of 39 elements in Bavarian rocks

An approach is presented to establish a state-wide basis for geochemical background values from the available data of the Geological Survey of Bavaria. For this purpose, typical element background concentrations of rock units in Bavaria were statistically evaluated and presented in a lithogeochemical map derived from the geological map at a scale of 1:25,000. The elements comprise 39 geogenic main and trace elements (SiO2, Al2O3, Fe2O3, MnO, MgO, CaO, Na2O, K2O, TiO2, P2O5, Li, Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, As, Rb, Sr, Y, Zr, Nb, Mo, Cd, Sn, Sb, Cs, Ba, La, Ce, Tl, Pb, Bi, Th, and U). The distributions of element concentrations in the lithogeochemical units follow closely lognormal patterns in a large majority of cases. Statistical parameters (10th, 25th, 50th, 75th, 90th percentiles) of investigated elements were determined using the cenfit function of the NADA package in the open-source R program. The investigation, based on 8,838 analysed samples, provided data for about 2/3 of the area of Bavaria. The lithogeochemical map with medians (50th percentiles) and background values (90th percentiles) of the investigated elements is available in a web map application. Thus, the regional geogenic background values of the investigated elements in Bavaria are publicly available for a large variety of applications, such as environmental issues and applied research questions.
Performance Analysis of Object Classification System for Traffic Objects Using Various SVM Kernels

In the area of autonomous vehicle, object classification is an important task. Different classification algorithms are available. In this work, we have focused on different kernels of support vector machine (SVM) to analyze the performance of traffic object classification system. We have performed experimentation on open-source database had been carried out on the basis of performance metrics such as recall, precision, F1, and accuracy. Our classification system provides maximum accuracy 69% with the help of RBF kernel.
DIC deformation analysis using B-spline smoothing with consideration of characteristic noise properties

This paper deals with 3D Digital Image Correlation (DIC) analysis including smoothing of the raw data and subsequent strain determination. The evaluation method is based on B-spline approximation of the 3D coordinates with optimization of the essential smoothing parameters. Thereby, a noise analysis of the reference and deformed state by 2D Fast Fourier Transform is performed with adjustment of smoothing parameters during the optimization. In the procedure, the similarity of noise characteristic is evaluated using the Pearson correlation coefficient. The presented evaluation method is demonstrated by an example of a DIC deformation analysis on bending loaded hybrid laminates. As a result, a smooth and plausible strain distribution is achieved.
Formalization of People and Crowd Detection and Tracking for Smart Video Surveillance

One of the promising areas of development and implementation of artificial intelligence is the automatic detection and tracking of moving objects in video sequence. The paper presents a formalization of the problem of detection and tracking of people and crowd in video. At first, we defined person, group of persons and crowd motion detection types and formalized them. For crowd, we defined three main types of its motion: direct motion, aggregation and dispersion. Then, we formalised the task of tracking for these three groups of people (single person, group of persons and crowd). Based on these formalizations, we developed algorithms for detection and tracking people and crowd in video sequences for indoor and outdoor environment. The results of experiments for video sequences obtained using a stationary and moving video camera are presented.
Machine Learning for Green Smart Health Toward Improving Cancer Data Feature Awareness

Radiation therapy and chemotherapy may be considered life-saving treatments for cancer patients. Though this treatment is not always successful, it left very higher effects on our environment. Drugs used in cancer treatment stop the growth and division of cells; released into the environment, they can affect the ecosystem through altered fertility and increased genetic defects. For a safer greener world undoubtedly, we can rely on machine learning technology to diagnose cancer in early stages. Hence, we may call that choosing the right influencing feature may reduce morbidity and green technology for early cancer diagnosis. Cervical cancer is an excellent example for such a study, as well as impacting individuals, families, and the environment. Cervical cancer presents almost no symptoms at the early stages of development of this condition. Because multi-factors may be involved, this demands a lot of research and analysis to identify causative or linked features. Choosing the right influencing feature is a challenging field in data science due to the presence and complexity of multi-dimensional data. The researchers have applied and optimized an ensemble learning algorithm as it is the best model for multi-modal medical data when relatively high dimensionality is present. The main objective of this study was to minimize the dependency on data pre-processing techniques, while analyzing the data (filling/ignoring missing values with the statistical method). Also, increasing such awareness of feature selection will immensely impact the environment (e.g., chemotherapy-free, less radiation therapy). Main factors were studied and validated using root mean square error (RSME) and mean absolute error (MAE). The classification accuracy for features was obtained by tenfold cross-validation and test (where 66% is training data and 34% test data).
An Enhanced Attentive Implicit Relation Embedding for Social Recommendation

Nowadays, recommendation system incorporating social networks has attracted a lot of research attention and is widely used to understand user preferences regarding social relations. Besides, graph neural networks(GNNs) have proven to learn graph-structured data effectively. Thus, integrating GNN into social recommendations has become one challenge. Many approaches encode both the user-item interaction and social relations to learn user preference. However, the implicit relations are also crucial to understanding the features of users and items. Thus, we propose the attentive implicit relation embedding for social recommendation(SR-AIR), which models the user-item interaction and social networks, utilizing a graph attention mechanism on implicit relations of users and items. We evaluate our framework on two real-world datasets and demonstrate that our framework performs the best compared with state-of-the-art baselines
Detection of Metastatic Cancer on Lymph Node Sections with Deep Convolutional Neural Network

Cancer detection is designed to diagnose the presence of cancerous tissue from a medical scan. Detecting cancer as early as possible is very important, which provides a better chance of saving patients' lives. Generally, plenty of resources and time are required to train a high skill in identifying medical images for cancer detection. However, machine learning models can be used to help doctors to make a more precise and quick diagnosis. Typically, most previous models focus on detecting a single type of cancer which only requires a model to learn limited features. In this paper, we will focus on detecting metastatic cancer based on machine learning models, which has many more features and requires a more generalized model. Therefore, we propose a machine learning model to identify metastatic tissue in histopathologic scans of lymph node sections. A deep convolutional neural network model is used with carefully tuned hyperparameters to predict the image's label on the PatchCamelyon benchmark dataset. Besides, a ResNet model and a simple convolutional neural network model are trained. Our experiment results on the PatchCamelyon benchmark dataset show that our model performs much better than baselines. On this basis, deep convolutional neural networks are a proper method for cancer image classification. These results shed light on how we can help doctors detect metastatic cancer earlier with our deep convolutional neural network model.
Identifying the Most Frequently Used Words in Spam Mail Using Random Forest Classifier and Mutual Information Content

Nowadays, email is an important medium of communication used by almost everyone whether for official or personal purposes, and this has encouraged some users to exploit this medium to send spam emails either for marketing purposes or for potentially harmful purposes. The massive increase in the number of spam messages led to the need to find ways to identify and filter these emails, which encouraged many researchers to produce work in this field. In this paper, we present a method for identifying and detecting spam email messages based on their contents. The approach uses the mutual information contents method to define the relationship between the text the email contains and its class to select the most frequently used text in spam emails. The random forest classifier was used to classify emails into legitimate and spam due to its performance and the advantage of overcoming the overfitting issue associated with regular decision tree classifiers. The proposed algorithm was applied to a dataset containing 3000 features and 5150 instances, and the results obtained were carefully studied and discussed. The algorithm showed an outstanding performance, which is evident in the accuracy obtained in some cases, which reached 97%, and the optimum accuracy which reached 96.4%.
Cosine Metric Supervised Deep Hashing

Deep supervised hashing hash has been widely utilized in large-scale image retrieval due to its lightweight storage and fast search speed. The distribution of features from the existing hashing methods has been inevitably distorted from the original feature distribution, which resulted in the performance decline on image retrieval. With the constraints of the Euclidian metric, the distortion can be mitigated to certain extent. However, the Euclidian distance is sensitive to the norm of the features. On the point, we propose a novel deep hashing method, called cosine metric supervised deep hashing (CMDH), to perform hash learning by incorporating cosine metric and category loss. CMDH uses the cosine metric to effectively mitigate the impact caused by the diverse sample vector norms on retrieval performance. In addition, joint deep hash learning constructs binary coding over cosine loss and category loss, which further alleviates distortion in binary feature learning for efficient retrieval.
Generalization of the Formal Method for Determining the State of Processors of a Multiprocessor System Under Testing

The paper proposes a generalization of the formal procedure for establishing the state of processors of an M-diagnosed system based on the results of performing a given set of mutual test checks of processors, in accordance with the Preparata-Metze-Chien model. Αn approach to solving the problem of a possible assessment of the sufficiency of a set of test checks to unambiguously determine the state of the system is also proposed. In addition, as a result of special transformations, the process of establishing the state of the system processors based on the results of the tests carried out can be greatly simplified. Thus, a significant part of the calculations aimed at performing self-diagnostics of the system is transferred from the operational stage to the development stage.
EVGAN: Optimization of Generative Adversarial Networks Using Wasserstein Distance and Neuroevolution

Generative Adversarial Networks (or called GANs) is a generative type of model which can be used to generate new data points from the given initial dataset. In this paper, the training problems of GANs such as 'vanishing gradient' and 'mode collapse' are reduced using the proposed EVGAN which stands for 'evolving GAN'. An improvement was done with the help of using Wasserstein loss function instead of the Minimax loss in the traditional GAN. Also, coevolution ensures that the best models from both the generator and discriminator pool are selected for further evolution thereby making the training of GAN more stable. Speciation is also included with a threshold of min. no of species thereby helping in increasing the diversity of the generated image. The model is evaluated in the MNIST dataset and shows better performance in accuracy as well as convergence when compared to the traditional GAN and WGAN.
Research on Load Transfer of Greenhouse Based on Blockchain

In order to overcome the low utilization rate of the greenhouse electric energy, avoid difference between peak and valley load is too high. The paper proposes an optimization model of greenhouse load transfer based on blockchain. This model establishes an optimal regulation model based on the minimum difference between the peak and valley loads of the greenhouse as the objective function, and uses the simulated annealing algorithm to solve the model. Through the PBFT consensus mechanism of the blockchain, distributed computation are carried out on the electricity nodes of the greenhouse, and the scheduling scheme is optimized for the second time. The experimental results show that compared with the traditional load transfer model, the method proposed in this paper can reduce the load peak-valley difference by 20% and increase the load factor by 4%. Improve the stability of greenhouse power system, and provides theoretical support for power dispatch problems.
Statistical and Computational Needs for Big Data Challenges

The traditional way of formatting information from transactional systems to make them available for “statistical processing” does not work in a situation where data is arriving in huge volumes from diverse sources, and where even the formats could be changing. Faced with this volume and diversification, it is essential to develop techniques to make best use of all of these stocks in order to extract the maximum amount of information and knowledge. Traditional analysis methods have been based largely on the assumption that statisticians can work with data within the confines of their own computing environment. But the growth of the amounts of data is changing that paradigm, especially which ride of the progress in computational data analysis. This chapter builds upon sources but also goes further in the examination to answer this question: What needs to be done in this area to deal with big data challenges?
Signature Extraction from Bilingual Document Images Using Blobs Method

In this paper, we present an effective method for signature extraction from bilingual (Kannada and English) printed/handwritten documents using the contour and blobs-based method. Experimentation is carried out on our own dataset containing 150 real-time documents. The performance of the proposed method is evaluated using the parameters like., accuracy, precision, recall, and F1-score. The proposed method performs better than the existing methods considered for comparison purposes and obtained an accuracy of 84.41%.
A preliminary investigation of developer profiles based on their activities and code quality: Who does what?

Developers work on different tasks in different conditions based on individual technical skills and personal habits. Identifying developer groups by mining their repositories is key for various tasks ranging from understanding developers types in open source projects, to help project managers concerned with the team allocation and coordination of human resources in companies. We aimed at identifying distinct groups of developer profiles based on well defined characteristics and at characterizing the most common quality issue types introduced by each profile in their code. We considered 77,932 commits of 33 open source Java projects, clustering their 2460 developers using dimensionality reduction techniques and applying the k-means algorithm. We identified five profiles among 2460 developers based on project experience, developer productivity and the common quality issues they introduce in the code. Results can be used by developer teams to detect and cope with harmful practices, in order to be more efficient by reducing the number of bugs they produce, looking for adequate training options, and balancing their teams.
Deep Multi-agent Reinforcement Learning for Tag Game

Deep reinforcement learning (DRL) is one of the most promising branches of machine learning. DRL has paved the way for an intelligent society made of autonomous agents that learn from its experience. The world in itself can be considered as a collection of agents. The agents take action in an environment based on their observation to obtain the maximum reward. Inspired from the break-through success in mimicking the game of Go, a lot of research has been conducted to optimize the behavior of agents in a single-agent framework. However, the ability of agents to interact and cooperate in a multi-agent setting is still a complex problem. This work interprets how individual agents in a Tag Game simulation develop cooperative and competitive abilities by mere manipulation of their reward function. Also, the paper compares the behavior of agents under various environmental scenarios from single to multi-agent cases.
On the main approaches to creating conditions for training personnel with higher education on an interdisciplinary basis

The paper reviews the regulatory issues in the field of higher education as related to the improvement of personnel training quality based on enhancing the classification of professional educational programs in the Russian Federation. The features of the current state of higher education in Russia, the problems and possible prospects for transformations in this area, as well as the main stages of fulfilling the task of transforming the higher education professional educational programs are discussed. The liberalization of federal state educational standards of higher education and the increased independence of higher education institutions during the development of educational programs taking into account professional standards indicate the need to create an updated classification system for the Russian education. The classification system being developed should become a common one for all types of professional educational programs, including professional training programs and additional professional programs. The paper proposes new approaches to the system structure and basic classification levels of the List of specialties and areas of training, as to the establishment of the conditions for training personnel with higher education on an interdisciplinary basis, taking into account the common subject content of professional activities and providing the conditions for the existence of enlarged groups of specialties and areas of training.
Self-dual 2-quasi-cyclic codes and dihedral codes

We characterize the structure of 2-quasi-cyclic codes over a finite field F by the so-called Goursat Lemma. With the characterization, we exhibit a necessary and sufficient condition for a 2-quasi-cyclic code being a dihedral code. And we obtain a necessary and sufficient condition for a self-dual 2-quasi-cyclic code being a dihedral code (if charF=2), or a consta-dihedral code (if charF≠2). As a consequence, any self-dual 2-quasi-cyclic code generated by one element must be (consta-)dihedral. In particular, any self-dual double circulant code must be (consta-)dihedral. We also obtain necessary and sufficient conditions under which the three classes (the self-dual double circulant codes, the self-dual 2-quasi-cyclic codes, and the self-dual (consta-)dihedral codes) of codes coincide with each other.
Vehicle Type Classification Using Hybrid Features and a Deep Neural Network

In this research, a framework incorporating hybrid features is proposed to improve the performance of vehicle type classification. The proposed model includes a camera response model to enhance the collected images and a Gaussian mixture model to localize the object of interest. The feature vectors are extracted from the pre-processed images using Gabor features, histogram of oriented gradients, and local optimal-oriented pattern. The hybrid set of features discriminate the classes better; further, an ant colony optimizer is used to reduce the dimension of the extracted feature vectors. Finally, deep neural network is used to classify the types of vehicles in the images. The proposed model was tested on the MIO vision traffic camera dataset and a real-world dataset consisting of videos of multiple lanes of a toll plaza. The proposed model showed an improvement in accuracy ranging from 0.28% to 8.68% in the MIO TCD dataset when compared to well-known neural network architectures like AlexNet, Inception V3, ResNet 50, VGG 19, Xception, and DenseNet.