Reviewerly/exHarmony huggingface dataset:
Coordination Strategies: Managing Inter-team Coordination Challenges in Large-Scale Agile

Abstract Inter-team coordination in large-scale software development can be challenging when relying on agile development methods that emphasize iterative and frequent delivery in autonomous teams. Previous research has introduced the concept of coordination strategies, which refer to a set of coordination mechanisms to manage dependencies. We report on a case study in a large-scale agile development program with 16 development teams. Through interviews, meeting observations, and supplemental document analyses, we explore the challenges to inter-team coordination and how dependencies are managed. We found four coordination strategies: 1) aligning autonomous teams, 2) maintaining overview in the large-scale setting, 3) managing prioritizations, and 4) managing architecture and technical dependencies. This study extends previous research on coordination strategies within teams to the inter-team level. We propose that large-scale organizations can use coordination strategies to understand how they coordinate across teams and manage their unique coordination situation.
Transprofessionalism as a Predictor for the Preadaptation of an Agent to the Professional Future

The article analyzes the possibilities of transprofessionalism as a factor in the preadaptation of a personality to an uncertain professional future in a post-industrial society. The logical-semantic model of transprofessionalism and a heuristic model of an agent’s preadaptation to uncertain professional future are presented. The foresight technology system was chosen as a methodology for developing transprofessionalism and predicting the social and professional future. It was focused on the formation of readiness for changes and the preadaptation for unpredictable future. The result of this technology is the convergence of a post-industrial society characteristics and semantic qualities of the professional future. The generalization of the research results allowed the authors to justify the possibility of high school training of transprofessionals with meta-professional skills who are ready to meet the challenges of the digital economy. The purpose of the research was to determine effective technologies for the training of innovative specialists (transprofessionals) on the basis of the developed transprofessional models of pre-adaptation to the professional future. The methodological basis of the study was non-classical psychology and a convergent ap-proach. In the scientific substantiation of transprofessionalism as a predictor of preadaptation to the future, the concept of professional development, the multidimensional, transdiscipli-nary, competence and project approaches, as well as the modeling of the predictable future were used. The article presents the logical-semantic and heuristic models of convergence and transprofessionalism development, as well as foresight technologies for preparing agents for an uncertain professional future. The structure of transprofessionalism is revealed through a meaningful analysis and description of its components’ interaction, which include the trans-fessional orientation, as well as the regulatory, vocational and educational, information and communication, operational and technological components. The intercoordinate space in the proposed model forms the psychological potential of the personality, its resource capabilities. Based on the analysis of trends and challenges of the post-industrial society, it was concluded that it is necessary to introduce into the educational process the technology of problem-oriented learning (PBL) and foresight technologies aimed at developing students' readiness for change, proactive goal-setting and self-design in a variable educational space (high humanitarian technologies, minors, design methods involving digital didactic tools, game technologies, discussions and moderation, etc.). Concerning practical significance, the materials of the article can be used for the preparation of transprofessional specialists who are ready for pre-adaptation of an uncertain social and professional future.
Twitter based Sentiment Analysis of Impact of Covid-19 on Education Globaly

Education system has been gravely affected due to widespread of Covid-19 across the globe. In this paper we present a thorough sentiment analysis of tweets related to education available on twitter platform and deduce conclusions about its impact on people’s emotions as the pandemic advanced over the months. Through twitter over ninety thousand tweets have been gathered related to the circumstances involving the change in education system over the world. Using Natural language tool kit (NLTK) functionalities and Naive Bayes Classifier a sentiment analysis has been performed on the gathered dataset. Based on the results of this analysis we infer to exhibit the impact of covid-19 on education and how people’s sentiment altered due to the changes with regard to the education system. Thus, we would like to present a better understanding of people’s sentiment on education while trying to cope with the pandemic in such unprecedented times.
Generalized evidence passing for effect handlers: efficient compilation of effect handlers to C

This paper studies compilation techniques for algebraic effect handlers. In particular, we present a sequence of refinements of algebraic effects, going via multi-prompt delimited control, _generalized evidence passing_, yield bubbling, and finally a monadic translation into plain lambda calculus which can be compiled efficiently to many target platforms. Along the way we explore various interesting points in the design space. We provide two implementations of our techniques, one as a library in Haskell, and one as a C backend for the Koka programming language. We show that our techniques are effective, by comparing against three other best-in-class implementations of effect handlers: multi-core OCaml, the _Ev.Eff_ Haskell library, and the libhandler C library. We hope this work can serve as a basis for future designs and implementations of algebraic effects.
Relation Classification for Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study

Background Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications. However, no prior work has utilized DL to extract relations between bleeding events and relevant entities. Objective In this study, we aimed to evaluate multiple DL systems on a novel EHR data set for bleeding event–related relation classification. Methods We first expert annotated a new data set of 1046 deidentified EHR notes for bleeding events and their attributes. On this data set, we evaluated three state-of-the-art DL architectures for the bleeding event relation classification task, namely, convolutional neural network (CNN), attention-guided graph convolutional network (AGGCN), and Bidirectional Encoder Representations from Transformers (BERT). We used three BERT-based models, namely, BERT pretrained on biomedical data (BioBERT), BioBERT pretrained on clinical text (Bio+Clinical BERT), and BioBERT pretrained on EHR notes (EhrBERT). Results Our experiments showed that the BERT-based models significantly outperformed the CNN and AGGCN models. Specifically, BioBERT achieved a macro F1 score of 0.842, outperforming both the AGGCN (macro F1 score, 0.828) and CNN models (macro F1 score, 0.763) by 1.4% (P&lt;.001) and 7.9% (P&lt;.001), respectively. Conclusions In this comprehensive study, we explored and compared different DL systems to classify relations between bleeding events and other medical concepts. On our corpus, BERT-based models outperformed other DL models for identifying the relations of bleeding-related entities. In addition to pretrained contextualized word representation, BERT-based models benefited from the use of target entity representation over traditional sequence representation
Parts Semantic Segmentation Aware Representation Learning for Person Re-Identification

Person re-identification is a typical computer vision problem which aims at matching pedestrians across disjoint camera views. It is challenging due to the misalignment of body parts caused by pose variations, background clutter, detection errors, camera point of view variation, different accessories and occlusion. In this paper, we propose a person re-identification network which fuses global and local features, to deal with part misalignment problem. The network is a four-branch convolutional neural network (CNN) which learns global person appearance and local features of three human body parts respectively. Local patches, including the head, torso and lower body, are segmented by using a U_Net semantic segmentation CNN architecture. All four feature maps are then concatenated and fused to represent a person image. We propose a DropParts method to solve the parts missing problem, with which the local features are weighed according to the number of parts found by semantic segmentation. Since three body parts are well aligned, the approach significantly improves person re-identification. Experiments on the standard benchmark datasets, such as Market1501, CUHK03 and DukeMTMC-reID datasets, show the effectiveness of our proposed pipeline.
Development of critical thinking as a means of forming STEM competencies

The article analyzes the scientific literature in order to determine different approaches to the interpretation of the concept of “critical thinking”, describes its components, discusses the basic concepts, functions of critical thinking in teaching. Critical thinking is presented as one of the main competences of STEM education. The conditions for the development of critical thinking in the process of STEM-learning are determined. Methodical problems are solved, which should help students to develop STEM competences based on the development of their critical thinking.
On the Inductive Bias of Masked Language Modeling: From Statistical to Syntactic Dependencies

We study how masking and predicting tokens in an unsupervised fashion can give rise to linguistic structures and downstream performance gains.Recent theories have suggested that pretrained language models acquire useful inductive biases through masks that implicitly act as cloze reductions.While appealing, we show that the success of the random masking strategy used in practice cannot be explained by such cloze-like masks alone.We construct cloze-like masks using task-specific lexicons for three different classification datasets and show that the majority of pretrained performance gains come from generic masks that are not associated with the lexicon.To explain the empirical success of these generic masks, we demonstrate a correspondence between the masked language model (MLM) objective and existing methods for learning statistical dependencies in graphical models.Using this, we derive a method for extracting these learned statistical dependencies in MLMs and show that these dependencies encode useful inductive biases in the form of syntactic structures.In an unsupervised parsing evaluation, simply forming a minimum spanning tree on the implied statistical dependence structure outperforms a classic method for unsupervised parsing (58.74 vs. 55.91 UUAS).
Mediators in Determining what Processing BERT Performs First

Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in such comparisons. As a test-case mediating factor, we consider the prediction's context length, namely the length of the span whose processing is minimally required to perform the prediction. We show that not controlling for context length may lead to contradictory conclusions as to the localization patterns of the network, depending on the distribution of the probing dataset. Indeed, when probing BERT with seven tasks, we find that it is possible to get 196 different rankings between them when manipulating the distribution of context lengths in the probing dataset. We conclude by presenting best practices for conducting such comparisons in the future.
Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming

One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance.The universal solution of a fixed bitrate ladder is inadequate in ensuring a high quality of user experience without re-buffering or introducing annoying compression artifacts.However, a content-tailored solution, based on extensively encoding across all resolutions and over a wide quality range is highly expensive in terms of computational, financial, and energy costs.Inspired by this, we propose an approach that exploits machine learning to predict a contentoptimized bitrate ladder for on-demand video services.The method extracts spatio-temporal features from the uncompressed content, trains machine-learning models to predict the Pareto front parameters and, based on that, builds the ladder within a defined bitrate range.The method has the benefit of significantly reducing the number of encodes required per sequence.The presented results, based on 100 HEVC-encoded sequences, demonstrate a reduction in the number of encodes required when compared to an exhaustive search and an interpolation-based method, by 89.06% and 61.46%, respectively, at the cost of an average Bjøntegaard Delta Rate difference of 1.78% compared to the exhaustive approach.Finally, a hybrid method is introduced that selects either the proposed or the interpolation-based method depending on the sequence features.This results in an overall 83.83% reduction of required encodings at the cost of an average Bjøntegaard Delta Rate difference of 1.26%.
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth

Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited. This work proposes a new way to understand self-attention networks: we show that their output can be decomposed into a sum of smaller terms, each involving the operation of a sequence of attention heads across layers. Using this decomposition, we prove that self-attention possesses a strong inductive bias towards "token uniformity". Specifically, without skip connections or multi-layer perceptrons (MLPs), the output converges doubly exponentially to a rank-1 matrix. On the other hand, skip connections and MLPs stop the output from degeneration. Our experiments verify the identified convergence phenomena on different variants of standard transformer architectures.
Implementing Vertical Federated Learning Using Autoencoders: Practical Application, Generalizability, and Utility Study

Background Machine learning (ML) is now widely deployed in our everyday lives. Building robust ML models requires a massive amount of data for training. Traditional ML algorithms require training data centralization, which raises privacy and data governance issues. Federated learning (FL) is an approach to overcome this issue. We focused on applying FL on vertically partitioned data, in which an individual’s record is scattered among different sites. Objective The aim of this study was to perform FL on vertically partitioned data to achieve performance comparable to that of centralized models without exposing the raw data. Methods We used three different datasets (Adult income, Schwannoma, and eICU datasets) and vertically divided each dataset into different pieces. Following the vertical division of data, overcomplete autoencoder-based model training was performed for each site. Following training, each site’s data were transformed into latent data, which were aggregated for training. A tabular neural network model with categorical embedding was used for training. A centrally based model was used as a baseline model, which was compared to that of FL in terms of accuracy and area under the receiver operating characteristic curve (AUROC). Results The autoencoder-based network successfully transformed the original data into latent representations with no domain knowledge applied. These altered data were different from the original data in terms of the feature space and data distributions, indicating appropriate data security. The loss of performance was minimal when using an overcomplete autoencoder; accuracy loss was 1.2%, 8.89%, and 1.23%, and AUROC loss was 1.1%, 0%, and 1.12% in the Adult income, Schwannoma, and eICU dataset, respectively. Conclusions We proposed an autoencoder-based ML model for vertically incomplete data. Since our model is based on unsupervised learning, no domain-specific knowledge is required in individual sites. Under the circumstances where direct data sharing is not available, our approach may be a practical solution enabling both data protection and building a robust model.
Application of multi-label classification models for the diagnosis of diabetic complications

Abstract Background Early diagnosis for the diabetes complications is clinically demanding with great significancy. Regarding the complexity of diabetes complications, we applied a multi-label classification (MLC) model to predict four diabetic complications simultaneously using data in the modern electronic health records (EHRs), and leveraged the correlations between the complications to further improve the prediction accuracy. Methods We obtained the demographic characteristics and laboratory data from the EHRs for patients admitted to Changzhou No. 2 People’s Hospital, the affiliated hospital of Nanjing Medical University in China from May 2013 to June 2020. The data included 93 biochemical indicators and 9,765 patients. We used the Pearson correlation coefficient (PCC) to analyze the correlations between different diabetic complications from a statistical perspective. We used an MLC model, based on the Random Forest (RF) technique, to leverage these correlations and predict four complications simultaneously. We explored four different MLC models; a Label Power Set (LP), Classifier Chains (CC), Ensemble Classifier Chains (ECC), and Calibrated Label Ranking (CLR). We used traditional Binary Relevance (BR) as a comparison. We used 11 different performance metrics and the area under the receiver operating characteristic curve (AUROC) to evaluate these models. We analyzed the weights of the learned model and illustrated (1) the top 10 key indicators of different complications and (2) the correlations between different diabetic complications. Results The MLC models including CC, ECC and CLR outperformed the traditional BR method in most performance metrics; the ECC models performed the best in Hamming loss (0.1760), Accuracy (0.7020), F1_Score (0.7855), Precision (0.8649), F1_micro (0.8078), F1_macro (0.7773), Recall_micro (0.8631), Recall_macro (0.8009), and AUROC (0.8231). The two diabetic complication correlation matrices drawn from the PCC analysis and the MLC models were consistent with each other and indicated that the complications correlated to different extents. The top 10 key indicators given by the model are valuable in medical application. Conclusions Our MLC model can effectively utilize the potential correlation between different diabetic complications to further improve the prediction accuracy. This model should be explored further in other complex diseases with multiple complications.
An End-to-End Progressive Multi-Task Learning Framework for Medical Named Entity Recognition and Normalization

Medical named entity recognition (NER) and normalization (NEN) are fundamental for constructing knowledge graphs and building QA systems.Existing implementations for medical NER and NEN are suffered from the error propagation between the two tasks.The mispredicted mentions from NER will directly influence the results of NEN.Therefore, the NER module is the bottleneck of the whole system.Besides, the learnable features for both tasks are beneficial to improving the model performance.To avoid the disadvantages of existing models and exploit the generalized representation across the two tasks, we design an end-to-end progressive multi-task learning model for jointly modeling medical NER and NEN in an effective way.There are three level tasks with progressive difficulty in the framework.The progressive tasks can reduce the error propagation with the incremental task settings which implies the lower level tasks gain the supervised signals other than errors from the higher level tasks to improve their performances.Besides, the context features are exploited to enrich the semantic information of entity mentions extracted by NER.The performance of NEN profits from the enhanced entity mention features.The standard entities from knowledge bases are introduced into the NER module for extracting corresponding entity mentions correctly.The empirical results on two publicly available medical literature datasets demonstrate the superiority of our method over nine typical methods.
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition

Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulates ASR for each source language into many small ASR tasks and meta-learns a model initialization on all tasks from different source languages to access fast adaptation on unseen target languages. However, for different source languages, the quantity and difficulty vary greatly because of their different data scales and diverse phonological systems, which leads to task-quantity and task-difficulty imbalance issues and thus a failure of multilingual meta-learning ASR (MML-ASR). In this work, we solve this problem by developing a novel adversarial meta sampling (AMS) approach to improve MML-ASR. When sampling tasks in MML-ASR, AMS adaptively determines the task sampling probability for each source language. Specifically, for each source language, if the query loss is large, it means that its tasks are not well sampled to train ASR model in terms of its quantity and difficulty and thus should be sampled more frequently for extra learning. Inspired by this fact, we feed the historical task query loss of all source language domain into a network to learn a task sampling policy for adversarially increasing the current query loss of MML-ASR. Thus, the learnt task sampling policy can master the learning situation of each language and thus predicts good task sampling probability for each language for more effective learning. Finally, experiment results on two multilingual datasets show significant performance improvement when applying our AMS on MML-ASR, and also demonstrate the applicability of AMS to other low-resource speech tasks and transfer learning ASR approaches.