Reviewerly/exHarmony huggingface dataset:
Predicting Survival in Prostate Cancer Patients with Interpretable Artificial Intelligence

Background: The management of prostate cancer (PCa) that has been diagnosed after PSA testing relies on risk stratification. Treatments have significant urinary, digestive and sexual toxicities that can impact the patients’ quality of life. Many patients do not benefit from treatment because their cancer is indolent or because they die from other competitive causes. New models are needed to stratify patients at diagnosis and decide whether treatment could have an interest or not. Artificial Intelligence (AI) techniques can provide solutions in that setting. Methods: We used data from the prospective clinical trial PLCO and selected patients who were diagnosed with PCa during follow-up. We trained two gradient-boosting models to predict 10-year cancer-specific (CSS) and overall survival (OS) with features that described the general health status of the patients and PCa. Using shapley values, we provided a graphical and interpretable prediction, at the individual level. Findings: During follow-up, 8,776 patients were diagnosed with PCa. The dataset was split into a training (n=7,021) and a testing (n=1,755) dataset. Accuracy was 0.87 (± 0.02) and 0.98 (± 0.01) for OS and CSS respectively. The area under the receiver operating characteristic was 0.84 (± 0.02) and 0.81 (± 0.04) and the area under precision-recall was 0.6 (± 0.03) and 0.55 (± 0.07) for OS and CSS respectively. The models are available online. Interpretation: The clinical question of who to treat and who to observe is a major unmet need in prostate cancer. Using prospective data, we trained two models to predict 10-year CSS and OS with high accuracy. These models can be used online to provide predictions and support informed decision-making in PCa treatment. AI interpretability provides a novel understanding of the predictions to the clinicians and the patients using the models.Funding Statement: None.Declaration of Interests: The authors declare no conflict of interest.Ethics Approval Statement: The authors stated that since this was a secondary analysis of a previously approved clinical trial, no ethics committee approval was required.
A Survey of Graphical Page Object Detection with Deep Neural Networks

In any document, graphical elements like tables, figures, and formulas contain essential information. The processing and interpretation of such information require specialized algorithms. Off-the-shelf OCR components cannot process this information reliably. Therefore, an essential step in document analysis pipelines is to detect these graphical components. It leads to a high-level conceptual understanding of the documents that makes digitization of documents viable. Since the advent of deep learning, the performance of deep learning-based object detection has improved many folds. In this work, we outline and summarize the deep learning approaches for detecting graphical page objects in the document images. Therefore, we discuss the most relevant deep learning-based approaches and state-of-the-art graphical page object detection in document images. This work provides a comprehensive understanding of the current state-of-the-art and related challenges. Furthermore, we discuss leading datasets along with the quantitative evaluation. Moreover, it discusses briefly the promising directions that can be utilized for further improvements.
Psychological factors motivating the choice of university entrants

The article focuses on the study of factors influencing the choice of a university by entrants. Firstyear students’ behaviour is mainly affected by external factors or factors not related to professional educational trajectory choice. An individual choice considers the limitations imposed by the size of family capital, abilities and other characteristics of the applicant, institutional factors (development of infrastructure, etc.). These restrictions affect the choice of future profession and a particular university, which largely determines the education quality. The most popular sources to receive information about universities are the official websites. The analysis of entrants’ answers can become the basis for universities’ PR and advertising programs. They will also be useful for optimising the content of the website. The university presentation’s quality is one of the most critical factors in the independent search for a university without pre-established benefits. Advertising campaigns should be dominated by motives that promote a particular choice. They are the university’s overall image, teachers’ professionalism, and a clear definition of the actual possibilities for further work in the speciality.
On the Distribution of Missing Arguments and Adjuncts under the Pro Approach

This paper focuses on different behaviors of missing arguments and adjuncts.More specifically, this paper shows that interpretations of missing arguments can be freely recovered regardless of their environments while those of missing adjuncts can be recovered in very restricted environments.With respect to this difference, we suggest that missing arguments are syntactically present as silent forms.Hence, their interpretations are relatively less influenced by pragmatic processes.We also suggest that missing adjuncts can be classified into two types.Adjunct reading of one type is obtained through mandatory pragmatic enrichment processes like saturation, and that of the other type is obtained through optional free pragmatic enrichment.Hence, interpretations of missing adjuncts are more influenced by context.We further show that the analysis advanced here can provide a solid explanation for the data pointed out by Yim (2020).We also discuss some advantages of pronoun analysis in comparison to argument ellipsis analyses and Verb-Stranding VP ellipsis analyses.
Genetic programming for feature model synthesis: a replication study

Abstract Software Product Lines (SPLs) make it possible to configure a single system based on features in order to create many different variants and cater to a wide range of customers with varying requirements. This configuration space is often modeled using Feature Models (FMs). However, in practice, the SPL (and consequently the FM) is often created after a set of variants has already been created manually. Automating the task of reverse engineering a feature model that describes a set of variants makes the process of adopting an SPL easier. The genetic programming pipeline is a good fit for feature models and has been shown to produce good reverse engineering results. In this paper, we replicate the results of such an existing approach with a larger set of feature models and investigate the effects of various genetic programming parameters and operators on the results. The design of our replication experiments employs three perspectives: duplicate the exact conditions using various features models, study the interaction of two parameters of the genetic programming approach, and optimize the values for the population and generation parameters and for the mutation and crossover operators. Results reinforce the previously obtained outcome, the original study being confirmed. The relations between the number of features and number of generations, respectively number of features and size of populations were also investigated and best values based on obtained results are provided. The current study also aimed to optimize various parameters of the genetic programming approach, the interpretation of those experiments discovering concrete values.
Dual Extreme Learning Machine Based Online Spatiotemporal Modeling With Adaptive Forgetting Factor

Many industrial thermal processes are large-scale time-varying nonlinear distributed parameter systems (DPSs).To effectively model such systems, dual extreme learning machine based online spatiotemporal modeling with adaptive forgetting factor (AFFD-ELM) is proposed in this paper.This method can recursively update the parameters of the low-order temporal model by using newly arriving data under Karhunen-Loève (KL) based space/time separation.In this way, the time-varying dynamics can be tracked real-time very well as output data increases over time.Besides, since the training samples are usually timeliness, adaptive forgetting factor (AFF) is also embedded in this method to improve the online learning effects by adding a reasonable weight to previous data.This online learning strategy makes the process promising for online modeling under continuously samples environment.The proposed method is utilized for online temperature prediction of the curing oven.Simulation results verify the efficiency and viability of the online spatiotemporal model.
New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract
Humanitarian Contexts of Technical Education in Russia

The article deals with the problem of formation of social and humanitarian competen[1]cies in Russian engineering education. The author shows how different views on the relationship between man and machine, as well as between society and the technosphere, were translated into various public initiatives and analyzes the impact of these initiatives on the evolution of approaches to engineering education. Special attention is paid to the promotion of the ideology of design think[1]ing and the movement for the humanitarization of engineering education. Both gained a consider[1]able popularity in the last quarter of the twentieth century. However, in modern conditions, the relationship between technological progress and the development of human society is taking on new forms that require new answers. There are also new risks – in particular, the risk of complete de[1]humanization of technological progress and the unclear prospect of the formation of a so-called post-human civilization. Social assessment of technology and the development of value-based tech[1]nologies are possible answers. They have received a very significant support in the United States and Western Europe. In particular, several European conferences on this topic have already been held within the EU (the latest one took place in Bratislava in 2019). However, due to some features of the Russian intellectual tradition, as well as the pronounced techno-optimism of mass consciousness, the Russian academic environment has not yet shown sufficient interest to implant these approaches in technical education. The possibility of managing the current technological progress on the basis of a dialogue with civil society, which is popular in the European Union, is also not considered in Russian intellectual environments. At the same time, the leading technical universities in Russia are gradually developing a network of enthusiasts who are studying the European practice of social assessment of technology and its adaptation to Russian conditions. Such adapted practices may become the con[1]ceptual guidelines for Russian engineering education.
ADAPTIVE REDUCED PATHS SUCCESSIVE CANCELLATION LIST DECODING FOR POLAR CODES

Adaptive Reduced Path Successive Cancellation List (ARP-SCL) decoding for polar codes is proposed in this paper. The aim here is to reduce the complexity of the conventional Successive Cancellation List (SCL) and a previously proposed Reduced Path SCL (RP-SCL) decoders. The complexity reduction is obtained at low and moderate signal to noise power ratio (SNR) regions. The operation of the proposed ARP-SCL decoder relied on the calculation of different optimum pruning parameters according to the operating SNR, while RP-SCL used single parameters set. Simulation tests are carried out to determine both the complexity and Bit Error Rate (BER) performances of the proposed ARP-SCL decoder in comparison with SCL and RP-SCL decoders. Additive White Gaussian Noise (AWGN) channel and two fading channel models simulating indoor 5G environment using millimeter Wave (mmWave) with carrier frequency of 28GHz and having either 50 or 400 MHz bandwidths are used. The results revealed that the proposed ARP-SCL decoder reduced the average processed paths up to 46.88%. The largest complexity reductions are obtained in the case of fading channel having 400 MHz bandwidth at high SNR regions. In most test conditions, the mentioned reduction in complexity is achieved without greatly sacrificing the required BER.
Active Learning Methods for Sustainable Education Development

The growing importance of ICT in educational processes activates the development of modern electronic teaching aids in various content areas and the development of teaching methods. The integration of ICT must ensure the improvement of the teaching/learning environment and the development of new teaching/learning approaches. Students’ motivation, engagement and interest in own learning are obligatory for successful and student-oriented education, especially in a situation of crisis, when educational institutions can immediately be closed after a governmental decision (pandemic caused by “Covid-19”) as it happened in March, 2020. All teaching and learning had been moved unexpectedly to remote/distance education setting, to adapt the learning methods and processes and to motivate the learners contributes to sustainable education. The aim of the paper is to develop active learning methods in digital environment to modernize the learning environment required for STEM in Vidzeme University of Applied Sciences, which includes the development of teaching methods and tools and their suitability for the digital age. The methodology includes theoretical research and practical experiments for full time students in face-to-face and digital environment. There are three experimental groups for course “Algorithms and data structures” ‒ one for traditional teaching, one with active learning methods faceto- face classroom and one with active learning methods in digital environment. Results of the research shows that active learning methods influence positively to course results but there are no differences for course results between groups with active learning methods in face-to-face and digital environment.
Multimedia Interactive System of Vocal Music Teaching Based on Voice Recognition

This paper discusses the application of audio signal parameters extraction and analysis technology in multimedia vocal music teaching, and studies the vocal music teaching technology based on voice recognition. In the process of voice recognition, MFCC parameters and speech frame energy constitute a combination of feature parameters as feature parameters, and the improved DTW is taken as pattern matching algorithm of evaluation criteria. Matlab is used to simulate the algorithm used in the system to determine the reliability of its performance, and the running test of the system is realized successfully. The experimental results show that the system realizes the real-time modification and playback of acoustic wave waveform and pitch on the computer, which improves the intuitive and interactive vocal music teaching.
Organization of the university educational environment in the context of the COVID-19 pandemic

The article reveals issues related to the impact of the COVID-19 pandemic on various aspects of human activity. The necessity of transferring at the peak of diseases of educational institutions to the distance format of conducting classes is substantiated. Studying the articles of a number of authors, the high efficiency of distance learning for high school students and university students is noted; the forms of distance learning in various universities of the world and the problems of such learning are revealed; attention is focused on the possibilities of using a hybrid learning model in the educational process with preliminary recording of lectures on YouTube and conducting classes online using the Teams, Skype, Zoom technology platforms; an assessment of the perception of innovative digital technologies by teachers and students is carried out; it is proposed to submit practically significant, complex tasks for exams that require research and the use of competencies by students of several specialized disciplines.Considerable attention is paid to the organization of the educational process at the Financial University under the Government of the Russian Federation. It is noted that many problems of the transition to distance learning were avoided thanks to the advance, systematic and purposeful work to form the university digital environment. The general technological scheme of the educational process of the university in a pandemic is illustrated and disclosed in detail. Information about specialized programs and packages that support the educational process is provided. The role of 1C software products, 1C cloud solutions, which support many basic disciplines in various areas of student training, is especially noted. The possibilities and directions of students’ research work in the field of data analysis and machine learning are revealed. It is concluded that the transition of education to distance learning has not become critical for the Financial University. The University continues to train highly-demanded qualified personnel.
Organizational and pedagogical conditions for training higher education applicants by learning tools of a competence-oriented environment

The organizational and pedagogical conditions for training higher education applicants by learning tools of a competence-oriented environment are analysed. Such pedagogical conditions include integration of the educational process in the context of the face-to-face training and a competence-oriented computer environment, providing systematic interactive work and performance of rating tasks in a learning environment focused on competence and systematic monitoring and control over the process of training of higher education applicants. In order to study the feasibility of using organizational and pedagogical conditions for the training of higher education applicants by learning tools of a competence-oriented environment, the degree of competence of the expert commission was checked and the indicators of competences of experts were calculated. Experimental work on determining the coefficient of concordance gives the opportunity to state that the opinions of experts on the organizational and pedagogical conditions for the training of higher education applicants by learning tools of a competence-oriented computer environment are consistent. It is noted that the implementation of organizational and pedagogical conditions, in fact, involves the optimization of educational and cognitive activities of higher education applicants, and taking into account certain conditions will lead to renewal, productivity, continuity and integrity of acquisition of the competences.
SoK: Privacy-Preserving Collaborative Tree-based Model Learning

Abstract Tree-based models are among the most efficient machine learning techniques for data mining nowadays due to their accuracy, interpretability, and simplicity. The recent orthogonal needs for more data and privacy protection call for collaborative privacy-preserving solutions. In this work, we survey the literature on distributed and privacy-preserving training of tree-based models and we systematize its knowledge based on four axes: the learning algorithm, the collaborative model, the protection mechanism, and the threat model. We use this to identify the strengths and limitations of these works and provide for the first time a framework analyzing the information leakage occurring in distributed tree-based model learning.
GGATB-LSTM: Grouping and Global Attention-based Time-aware Bidirectional LSTM Medical Treatment Behavior Prediction

In China, with the continuous development of national health insurance policies, more and more people have joined the health insurance. How to accurately predict patients future medical treatment behavior becomes a hotspot issue. The biggest challenge in this issue is how to improve the prediction performance by modeling health insurance data with high-dimensional time characteristics. At present, most of the research is to solve this issue by using Recurrent Neural Networks (RNNs) to construct an overall prediction model for the medical visit sequences. However, RNNs can not effectively solve the long-term dependence, and RNNs ignores the importance of time interval of the medical visit sequence. Additionally, the global model may lose some important content to different groups. In order to solve these problems, we propose a Grouping and Global Attention based Time-aware Bidirectional Long Short-Term Memory (GGATB-LSTM) model to achieve medical treatment behavior prediction. The model first constructs a heterogeneous information network based on health insurance data, and uses a tensor CANDECOMP/PARAFAC decomposition method to achieve similarity grouping. In terms of group prediction, a global attention and time factor are introduced to extend the bidirectional LSTM. Finally, the proposed model is evaluated by using real dataset, and conclude that GGATB-LSTM is better than other methods.
Grand Challenges in Signal Processing for Communications

SPECIALTY GRAND CHALLENGE article Front. Signal Process., 12 April 2021 | https://doi.org/10.3389/frsip.2021.664331
A Road Environment Prediction System for Intelligent Vehicle

The road environment prediction is an essential task for intelligent vehicle. In this study, we provide a flexible system that focuses on freespace detection and road environment prediction to host vehicle. The hardware of this system includes two parts: a binocular camera and a low-power mobile platform, which is flexible and portable for a variety of intelligent vehicle. We put forward a multiscale stereo matching algorithm to reduce the computing cost of the hardware unit. Based on disparity space and points cloud, we propose a weighted probability grid map to detect freespace region and a state model to describe the road environment. The experiments show that the proposed system is accurate and robust, which indicates that this technique is fully competent for road environment prediction for intelligent vehicle.