Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Control for Full-Duplex Device-to-Device Underlaid Cellular Networks: A Stackelberg Game Approach

In spectrum sharing cognitive radio networks, unauthorized users (secondary users) are allowed to use the spectrum of authorized users (primary users) to improve spectrum utilization. Due to limited spectrum resources, how to formulate a reasonable spectrum allocation scheme is very important. As a mathematical analysis tool, game theory can solve the problem of resource allocation well. In recent years, it has been applied to the research of resource allocation in spectrum sharing networks by some literatures. In a cellular network consisting of multiple cellular users and full-duplex end-to-end communication users D2D (device-to-device), the self-interference caused by full-duplex communication and the interference caused by the D2D users to the cellular users will significantly reduce system throughput. In order to reduce the interference in the network, this paper introduces a power control algorithm based on Stackelberg game, which sets the cellular users and D2D users as the leaders and followers, respectively. The cellular users and the D2D users compete with each other to minimize the cost, and we propose new utility functions. We build an optimization problem under the outage probability constraint and power constraint and the transmission power of the users is obtained by using the Lagrangian dual decomposition method. The simulation results show that the proposed game algorithm improves network performance compared with other existing schemes.
A Roadmap For Implementing The Sustainable Software Engineering Curricula In Ukraine According To European E-Competence Framework And Student-Centered Learning Conception

The paper focuses on and propose the semi-formal method of constructing individual educational students trajectories for the professional training of future software engineers in Ukrainian universities. The method for the student-centered learning conception support presented by the authors was developed using graph theory. The set of educational paths that the student is invited to choose corresponds with the requirements of IT enterprises and provides the student with the opportunity to choose his or her educational path for the level of education legally enshrined in Ukraine. The article presents the result of educational trajectories constructing for the process of the professional training of future software engineers from a Junior Bachelor level to Doctor of Philosophy. Ukrainian universities can use the proposed educational trajectories of the professional training of future software engineers for curricula and syllabus preparation. The practical development of this work is an interactive online assistant that will recommend to the student the choice of the educational path from the already achieved level to the target level, which the student will choose.
Statistical approaches in literature: Comparing and clustering the alternatives of love in Divan of Hafiz

Hafiz is one of the finest lyric poets of Iran. He learnt a lot from scholars and writers. This research deals with the alternatives of the word ‘Love’ in Divan of Hafiz. First, these words are determined and their counts are computed. Then, we classify them into sixteen categories. Finally, the counts of the words and the categories are compared using chi-square test and cluster analysis. The clustering results showed that the word ‘Way’ and the category ‘Human’ have the most applications as the alternatives of the word ‘Love’ in Divan of Hafiz.
Integrating Semantic Zoning Information with the Prediction of Road Link Speed Based on Taxi GPS Data

Road link speed is one of the important indicators for traffic states. In order to incorporate the spatiotemporal dynamics and correlation characteristics of road links into speed prediction, this paper proposes a method based on LDA and GCN. First, we construct a trajectory dataset from map-matched GPS location data of taxis. Then, we use the LDA algorithm to extract the semantic function vectors of urban zones and quantify the spatial dynamic characteristics of road links based on taxi trajectories. Finally, we add semantic function vectors to the dataset and train a graph convolutional network to learn the spatial and temporal dependencies of road links. The learned model is used to predict the future speed of road links. The proposed method is compared with six baseline models on the same dataset generated by GPS equipped on taxis in Shenzhen, China, and the results show that our method has better prediction performance when semantic zoning information is added. Both composite and single-valued semantic zoning information can improve the performance of graph convolutional networks by 6.46% and 8.35%, respectively, while the baseline machine learning models work only for single-valued semantic zoning information on the experimental dataset.
Random Network Transmission and Countermeasures in Containing Global Spread of COVID-19-Alike Pandemic: A Hybrid Modelling Approach

Since the outbreak of the novel coronavirus disease (COVID-19) at the beginning of December 2019, there have been more than 28.69 million cumulative confirmed cases worldwide as of 12th September 2020, affecting over 200 countries and regions with more than 920,463 deaths. The COVID-19 pandemic has been sweeping worldwide with unexpected rapidity. In this paper, a hybrid modelling strategy based on tessellation structure- (TS-) configured SEIR model is adopted to estimate the scale of the pandemic spread. Building on the data pertaining to the global pandemic transmission over the last six months around the world, key impact factors in the transmission and control procedure have been analysed, including isolation rate, number of the infected cases before taking prevention measures, degree of contact scope, and medical level, so as to capture the fundamental factor influencing the pandemic. The quantitative evaluation allowed us to illustrate the magnitude of risks of pandemic and to recommend appropriate national health policy of prevention measures for effectively controlling both intra- and interregional pandemic spread. Our modelling results clearly indicate that the early-stage preventive measures are the most effective action to be taken to contain the pandemic spread of the highly contagious nature of the COVID-19.
Multisensor-Based Autonomous Grasp Planning for Mobile Manipulator Navigation System with a Novel Soft Gripper

Mobile manipulators are widely used in different fields for transferring and grasping tasks such as in medical assisting devices, industrial production, and hotel services. It is challenging to improve navigation accuracies and grasping success rates in complex environments. In this paper, we develop a multisensor-based mobile grasping system which is configured with a vision system and a novel gripper set in an UR5 manipulator. Additionally, an error term of a cost function based on DWA (dynamic window approach) is proposed to improve the navigation performance of the mobile platform through visual guidance. In the process of mobile grasping, the size and position of the object can be identified by a visual recognition algorithm, and then the finger space and chassis position can be automatically adjusted; thus, the object can be grasped by the UR5 manipulator and gripper. To demonstrate the proposed methods, comparison experiments are also conducted using our developed mobile grasping system. According to the analysis of the experimental results, the motion accuracy of the mobile chassis has been improved significantly, satisfying the requirements of navigation and grasping success rates, as well as achieving a high performance over a wide grasping size range from 1.7 mm to 200 mm.
Beyond algorithms: Ranking at scale at Booking.com

Booking.com is one of the world’s largest online travel companies where millions of customers find their accommodation through an extensive and diverse list of properties including hotels, apart-ments, guest houses, and more. Our customers heavily rely on the ranking of search results in order to find a property that satisfies their preferences and criteria. While most of the Machine Learning literature focuses on the algorithmic aspect of ranking, not much has been published about the intricacies of developing a ranking that is Machine Learning powered and delivers business impact in a commercial environment. In this work, we describe the various challenges we faced while developing a large-scale Machine Learning powered ranking, including signal definition and feature representation complexity in the modelling aspect, information leakage and speed of innovation in experimentation for online evaluation, and response time minimization around serving at scale in production. We display an overview of the business gains of the step changes in the Machine Learning ranking of Booking.com. Our main conclusion is that there are multiple aspects that need to be carefully addressed for the creation of a Machine Learned ranker in a large-scale commercial setting.
Do Commodities Determine the EU Emission Allowances Price?

This paper presents an analysis of the influence of gas, coal, electricity and Brent (crude oil) prices on the EU emission allowance price by means of a vector autoregression analysis. Statistically significant influences on the price of CO2 emission allowances can be identified for all energy market variables examined, except electricity prices. Thus, the present analysis supports the assumptions of earlier publications that the influence of the energy market on the European Emissions Trading System (EU ETS) is decreasing and that the efforts of the European Commission are having an effect. The EU ETS is designed to stimulate the reduction of emissions by setting caps and to create monetary incentives for investment in new, low-emission technologies by trading emission allowances. However, the allocation efficiency of this system is conditional on the relative price stability of the emission allowances, as this is the only way to make reliable forecasts for risk calculations and investment decisions by companies. Using a vector autoregression model (VAR), this paper demonstrates significant influences of energy prices on the European Emission Allowances (EUA) price in the third phase of the EU ETS.
Comparing Feature Engineering Approaches to Predict Complex Programming Behaviors

Using machine learning to classify student code has many applications in computer science education, such as auto-grading, identifying struggling students from their code, and propagating feedback to address particular misconceptions. However, a fundamental challenge of using machine learning for code classiﬁcation is how to represent program code as a vector to be processed by modern learning algorithms. A piece of programming code is structurally represented by an abstract syntax tree (AST), and a variety of approaches have been proposed to extract features from these ASTs to use in learning algorithms, but no work has directly compared their eﬀectiveness. In this paper, we do so by comparing three diﬀerent feature engineering approaches for classifying the behavior of novices’ open-ended programming projects according to expert labels. In order to evaluate the eﬀec-tiveness of these feature engineering approaches, we hand-labeled a dataset of novice programs from the Scratch repository to indicate the presence of ﬁve complex, game-related programming behaviors. We compared these feature engineering approaches by evaluating their classiﬁcation eﬀec-tiveness. Our results show that the three approaches perform similarly across diﬀerent target labels. However, we also ﬁnd evidence that all approaches led to overﬁtting, suggesting the need for future research to select and reduce code features, which may reveal advantages in more complex feature engineering approaches.
Methods of analysis by the U.S. Geological Survey Organic Geochemistry Research Group--Update and additions to the determination of chloroacetanilide herbicide degradation compounds in water using high-performance liquid chromatography/mass spectrometry

An analytical method using high-performance liquid chromatography/mass spectrometry (HPLC/MS) was developed by the U.S. Geological Survey in 1999 for the analysis of selected chloroacetanilide herbicide degradation compounds in water. These compounds were acetochlor ethane sulfonic acid (ESA), acetochlor oxanilic acid (OXA), alachlor ESA, alachlor OXA, metolachlor ESA, and metolachlor OXA. The HPLC/MS method was updated in 2000, and the method detection limits were modified accordingly. Four other degradation compounds also were added to the list of compounds that can be analyzed using HPLC/MS; these compounds were dimethenamid ESA, dimethenamid OXA, flufenacet ESA, and flufenacet OXA. Except for flufenacet OXA, good precision and accuracy were demonstrated for the updated HPLC/MS method in buffered reagent water, surface water, and ground water. The mean HPLC/MS recoveries of the degradation compounds from water samples spiked at 0.20 and 'U.S. Geological Survey, Lawrence, Kansas. 2University of Kansas, Center for Research, Inc., and U.S. Geological Survey, Lawrence, Kansas. 1.0 ng/L (microgram per liter) ranged from 75 to 114 percent, with relative standard deviations of 15.8 percent or less for all compounds except flufenacet OXA, which had relative standard deviations ranging from 11.3 to 48.9 percent. Method detection levels (MDL's) using the updated HPLC/MS method varied from 0.009 to 0.045 ng/L, with the flufenacet OXA MDL at 0.072 ng/L. The updated HPLC/MS method is valuable for acquiring information about the fate and transport of the parent chloroacetanilide herbicides in water.
Development of an Electronic Multimedia Interactive Textbook for Physics Study at Technical Universities

The article reveals the problem of creating and improving e-textbooks on the example of the development of an electronic textbook for physics study at technical universities. The disadvantages of e-textbooks based on standard platforms and templates are considered. The problems of using Web technologies for the improvement of such textbooks are discussed. There are numerous platforms and templates for creating electronic textbooks. However, our analysis has showed inconvenience and inadequacy of the resources for the needs of the engineering education; the corresponding texts have a large volume, complex structure, contain multilevel cross-content focused both on understanding the fundamental laws and their technological application during engineering activities. Therefore, students find it difficult to navigate in a large amount of material with numerous equations, massive evidence base, and large graphic material. The
Phantom Embeddings: Using Embeddings Space for Model Regularization in Deep Neural Networks

The strength of machine learning models stems from their ability to learn complex function approximations from data; however, this strength also makes training deep neural networks challenging. Notably, the complex models tend to memorize the training data, which results in poor regularization performance on test data. The regularization techniques such as L1, L2, dropout, etc. are proposed to reduce the overfitting effect; however, they bring in additional hyperparameters tuning complexity. These methods also fall short when the inter-class similarity is high due to the underlying data distribution, leading to a less accurate model. In this paper, we present a novel approach to regularize the models by leveraging the information-rich latent embeddings and their high intra-class correlation. We create phantom embeddings from a subset of homogenous samples and use these phantom embeddings to decrease the inter-class similarity of instances in their latent embedding space. The resulting models generalize better as a combination of their embedding and regularize them without requiring an expensive hyperparameter search. We evaluate our method on two popular and challenging image classification datasets (CIFAR and FashionMNIST) and show how our approach outperforms the standard baselines while displaying better training behavior.
Fusing Multi-label Classification and Semantic Tagging

Companies have an increasing demand for enriching documents with metadata. In an applied setting, we present a three-part workﬂow for the combination of multi-label classiﬁcation and semantic tagging using a collection of key-phrases. The workﬂow is illustrated on the basis of patent abstracts with the CPC scheme. The key-phrases are drawn from a training set collection of documents without manual interaction. The union of CPC labels and key-phrases provides a label set on which a multi-label classiﬁer model is generated by supervised training. We show learning curves for both key-phrases and classiﬁcation categories, and a semantic graph generated from cosine similarities. We conclude that, given suﬃcient training data, the number of label categories is highly scalable.
INWEND: Using CBR to Automate Legal Assessment in the Context of the EU General Data Protection Regulation

The European General Data Protection Regulation (GDPR), which governs the processing of personal data in all EU Member States, contains an exemption for “purely personal or household activities”. Whether this so-called household exemption covers the setup of an online communication forum, particularly on a social media or chat platform, is a question of the individual case. We present a case-based reasoning approach to automatically assessing a scenario provided by the user and generating a tailored legal recommendation.
Information Technology for Constructing Individual Educational Trajectories Based on Latent-Semantic Analysis of Motivational Letters and Professional Achievements of Students

The article considers the possibility of constructing of individual educational trajectories by students, the significance of self-determining the way of self-development. The relevance of the work is determined by introducing the paradigm of student learning. The construction of individual educational trajectory is reduced to the problems of analyzing students' motivation and determining academic disciplines which will constitute the variable part of student's individual curriculum. Students' motivations were analyzed by the applying content analysis to students ' motivational letters. As a result, the data on students’ priority fields of knowledge and activity directions were obtained. Determination of academic disciplines is carried out on the basis of Latent semantic analysis (LSA). The results of the content analysis of the students' motivational letters, the results of the students' career focus survey and the results of tests on students’ current knowledge levels are the input of the LSA. The output of Latent semantic analysis is a list of knowledge areas relevant for the student’s profession. Ontologies of subject areas are built based on the list of knowledge areas. Ontology of subject areas is proposed to be built in the form of a thesaurus, describing the categorically-conceptual apparatus of the subject area. Determination of the key words of the subject area topics chosen by the student is carried out by using the Latent Dirichlet allocation. Defined keywords are the criteria for selecting the disciplines which will constitute the student's individual curriculum. Approbation of proposed information technology has shown its reliability.
Machine Learning for Self-Adaptive Internet of Underwater Things

Internet of Underwater Things (IoUTs) has gained increased momentum thanks to the advancements in underwater nodes, sensing, and communication technologies. This novel paradigm has tremendous potential to empower smart ocean applications. However, the harsh and dynamic nature of the underwater environment and underwater communication, the stringent requirements of underwater applications, and the difficulty and cost for IoUT management and maintenance have limited the development and application of IoUTs. In this regard, machine learning has been proposed to create self-adaptive IoUTs and boost the performance of smart oceans applications. In this paper, we shed light on the design of machine learning models for the on-the-fly intelligent and autonomous management of IoUT networking parameters and configurations aimed at boosting data delivery. We discuss the recent proposals for IoUT network management and how machine learning algorithms can improve such solutions at different networking layers. Finally, we point out some future research directions in need of further attention.
Impact Analysis of Document Digitization on Event Extraction

This paper tackles the epidemiological event extraction task applied to digitized documents. Event extraction is an information extraction task that focuses on identifying event mentions from textual data. In the context of event-based health surveillance from digitized documents, several key issues remain challenging in spite of great efforts. First, image documents are indexed through their digitized version and thus, they may contain numerous errors, e.g. misspellings. Second, it is important to address international news, which would imply the inclusion of multilingual data. To clarify these important aspects of how to extract epidemic-related events, it remains necessary to maximize the use of digitized data. In this paper, we investigate the impact of working with digitized multilingual documents with dierent levels of synthetic noise over the performance of an event extraction system. This type of analysis, to our knowledge, has not been alleviated in previous research.
A Short-Term Wind Speed Forecasting Hybrid Model Based on Empirical Mode Decomposition and Multiple Kernel Learning

Short-term wind speed forecasting plays an increasingly important role in the security, scheduling, and optimization of power systems. As wind speed signals are usually nonlinear and nonstationary, how to accurately forecast future states is a challenge for existing methods. In this paper, for highly complex wind speed signals, we propose a multiple kernel learning- (MKL-) based method to adaptively assign the weights of multiple prediction functions, which extends conventional wind speed forecasting methods using a support vector machine. First, empirical mode decomposition (EMD) is used to decompose complex signals into several intrinsic mode function component signals with different time scales. Then, for each channel, one multiple kernel model is constructed for forecasting the current sequence signal. Finally, several experiments are carried out on different New Zealand wind farm data, and the relevant prediction accuracy indexes and confidence intervals are evaluated. Extensive experimental results show that, compared with existing machine learning methods, the EMD-MKL model proposed in this paper has better performance in terms of the prediction accuracy evaluation indexes and confidence intervals and shows a better ability to generalize.