Reviewerly/exHarmony huggingface dataset:
3D Reconstruction of Deformable Colon Structures based on Preoperative Model and Deep Neural Network

In colonoscopy procedures, it is important to rebuild and visualize the colonic surface to minimize the missing regions and reinspect for abnormalities. Due to the fast camera motion and deformation of the colon in standard forward-viewing colonoscopies, traditional simultaneous localization and mapping (SLAM) systems work poorly for 3D reconstruction of colon surfaces and are prone to severe drift. Thus in this paper, a preoperative colon model segmented from CT scans is used together with the colonoscopic images to achieve the 3D colon reconstruction. The proposed framework includes dense depth estimation from monocular colonoscopic images using a deep neural network (DNN), visual odometry (VO) based camera motion estimation and an embedded deformation (ED) graph based non-rigid registration algorithm for deforming 3D scans to the segmented colon model. A realistic simulator is used to generate different simulation datasets with ground truth. Simulation results demonstrate the good performance of the proposed 3D colonic surface reconstruction method in terms of accuracy and robustness. In-vivo experiments are also conducted and the results show the practicality of the proposed framework for providing useful shape and texture information in colonoscopy applications.
Identifying Similar Test Cases That Are Specified in Natural Language

Software testing is still a manual process in many industries, despite the recent improvements in automated testing techniques. As a result, test cases are often specified in natural language by different employees and many redundant test cases might exist in the test suite. This increases the (already high) cost of test execution. Manually identifying similar test cases is a time-consuming and error-prone task. Therefore, in this paper, we propose an unsupervised approach to identify similar test cases. Our approach uses a combination of text embedding, text similarity and clustering techniques to identify similar test cases. We evaluate five different text embedding techniques, two text similarity metrics, and two clustering techniques to cluster similar test steps and four techniques to identify similar test cases from the test step clusters. Through an evaluation in an industrial setting, we showed that our approach achieves a high performance to cluster test steps (an F-score of 87.39%) and identify similar test cases (an F-score of 83.47%). Furthermore, a validation with developers indicates several different practical usages of our approach (such as identifying redundant and legacy test cases), which help to reduce the testing manual effort and time.
Research on the Collaborative Evolution of Blockchain Industry Ecosystems in Terms of Value Co-Creation

The formation of blockchain industrial ecology can help improve the open and efficient value synergy network, and this paper seeks to clarify the value relationship among industrial units and the trend of synergistic evolution of blockchain industrial ecosystem. Based on value co-creation theory, the blockchain industrial ecosystem synergy evolution process is analyzed, and the composite system synergy model is used to empirically investigate the evolution synergy of China’s blockchain industrial ecosystem from 2015 to 2020. The results show that: although the development level of China’s blockchain industry ecosystem continues to improve, it is still at a low level, and the policy-driven effect is obvious. There is a large disparity in the orderliness level of each subsystem of the blockchain industry ecosystem, and the industrial integration and application implementation are in a good situation, while the blockchain enterprises, located in the value pivot subsystem, are in a relatively tough position, and China’s blockchain industry ecosystem is overall in a state of reconciliation and has not yet formed a synergistic effect. Handling the synergistic relationship between the government, the market and other value-supporting units, and blockchain enterprises is the top priority for further promoting the synergistic evolution of the blockchain industry ecosystem.
Introduction of a new nomen-clature of academic specialities in Russia: Continuity and innovations

Introduction. The nomenclature of academic specialities to be awarded academic degrees is a system-forming element of academic degree holders’ certification system in any country of the world. Therefore, there is a need to revise and update the relevant structure of academic specialities in order to meet the prospective demand from research and development sector of high-tech industries. Apparently, the current nomenclature of 2017, as the instrument of realisation of public policy in the sphere of the state certification of academic and teaching staff in Russia, needed updating. The aim of the present research was to analyse a new version of the nomenclature of academic specialities in Russia to be awarded the degree of Doctor or Candidate of Science, and to make conceptual proposals on the implementation of new nomenclature provisions taking into account possible transformation of scientific and educational environment, including the emergence of new complex areas of academic research. Methodology and research methods. The research object is a system of certification of academic and teaching staff from the position of a three-level structure of the nomenclature of academic specialities, according to which academic degrees are awarded. The methods of comparison and data statistical analysis were applied to assess structural changes in the nomenclature. Results and scientific novelty. The authors considered the prerequisites for update of the nomenclature of academic specialities approved in 2017. The features of a new edition of the nomenclature of academic specialities were highlighted. The analysis of its new structure and content changes was carried out. It is demonstrated that current network of dissertation councils (1696 councils) can be divided into 4 groups according to the degree of compliance of academic specialities and fields of science with the previous and recent versions of nomenclature. It is necessary to create new dissertation councils for 20 new academic specialities. Practical significance. The authors revealed the features of the new nomenclature, requiring the reorganisation of councils network for the defense of doctoral and candidate dissertations. On this basis, to provide promising areas of academic research, the proposals on the implementation of new nomenclature provisions are outlined. The stages of re-opening of dissertation councils network are described.
K-Wav2vec 2.0: Automatic Speech Recognition based on Joint Decoding of Graphemes and Syllables

Wav2vec 2.0 is an end-to-end framework of self-supervised learning for speech representation that is successful in automatic speech recognition (ASR), but most of the work on the topic has been developed with a single language: English. Therefore, it is unclear whether the self-supervised framework is effective in recognizing other languages with different writing systems, such as Korean which uses the Hangul having a unique writing system. In this paper, we present K-Wav2Vec 2.0, which is a modified version of Wav2vec 2.0 designed for Korean automatic speech recognition by exploring and optimizing various factors of the original Wav2vec 2.0. In fine-tuning, we propose a multi-task hierarchical architecture to reflect the Korean writing structure. Moreover, a joint decoder is applied to alleviate the problem of words existing outside of the vocabulary. In pre-training, we attempted the cross-lingual transfer of the pre-trained model by further pre-training the English Wav2vec 2.0 on a Korean dataset, considering limited resources. Our experimental results demonstrate that the proposed method yields the best performance on both Korean ASR datasets: Ksponspeech (a large-scale Korean speech corpus) and Clovacall (a call-based dialog corpus). Further pre-training is also effective in language adaptation, leading to large improvements without additional data.
A Study of the Plausibility of Attention between RNN Encoders in Natural Language Inference

Attention maps in neural models for NLP are appealing to explain the decision made by a model, hopefully emphasizing words that justify the decision. While many empirical studies hint that attention maps can provide such justification from the analysis of sound examples, only a few assess the plausibility of explanations based on attention maps, i.e., the usefulness of attention maps for humans to understand the decision. These studies furthermore focus on text classification. In this paper, we report on a preliminary assessment of attention maps in a sentence comparison task, namely natural language inference. We compare the cross-attention weights between two RNN encoders with human-based and heuristic-based annotations on the eSNLI corpus. We show that the heuristic reasonably correlates with human annotations and can thus facilitate evaluation of plausible explanations in sentence comparison tasks. Raw attention weights however remain only loosely related to a plausible explanation.
Exploring Emerging Technologies with Analysis of Bibliographic Data Focused on Plasma Surface Treatment

Research trends and emerging technologies were explored through the Web of Science (WoS) literature of the last decade in relation to plasma technology, especially plasma surface treatment, widely used in all industries. For this, a network analysis using country and author keywords and emerging technology search algorithms, with regard to novelty, fast growth and impact, were used. As a result, we derived 40 keywords in terms of novelty and fast growth. Additionally, with these keywords, we traced the impact based on the citation relationships. Finally, nine keywords which were analyzed to contain many new technological issues were identified by deriving the author keywords included in the relevant documents. It is expected that the new technology fields derived from this paper can contribute to establishing a preemptive R&amp;D strategy.
Attribute Spaces: Supporting Design Space Exploration in Virtual Reality

Exploring the design space of configurations for objects in virtual scenes is a challenge within virtual reality authoring tools due to the lack of visualization capabilities, non-destructive operations, suggestions, and flexibility. This work introduces Attribute Spaces, tools for visualizing and manipulating object attributes in virtual reality during 3D content generation. Attribute Spaces enable designers to systematically explore design spaces by supporting rapid comparisons between design alternatives and offering design suggestions. Custom combinations of attributes can be grouped and manipulated simultaneously for several objects. The grouping supports the creation of custom operation combinations that can be used as tools to edit multiple attribute, as well as snapshots of promising design decisions for later review. In an evaluation of Attribute Spaces by 3D design experts, our approach was found to enhance users' understanding of their design space exploration progress and showed promise for integration into existing 3D workflows.
Defining an Optimal Configuration Set for Selective Search Strategy - A Risk-Sensitive Approach

A search engine generally applies a single search strategy to any user query. The search combines many component processes (e.g., indexing, query expansion, search-weighting model, document ranking) and their hyperparameters, whose values are optimized based on past queries and then applied to all future queries. Even an optimized system may perform poorly on some queries, however, whereas another system might perform better on those queries. Selective search strategy aims to select the most appropriate combination of components and hyperparameter values to apply for each individual query. The number of candidate combinations is huge. To adapt best to any query, the ideal system would use many combinations. In the real world it would be too costly to use and maintain thousands of configurations. A trade-off must therefore be found between performance and cost. In this paper, we describe a risk-sensitive approach to optimize the set of configurations that should be included in a selective search strategy. This approach solves the problem of which and how many configurations to include in the system. We show that the use of 20 configurations results in significantly greater effectiveness than current approaches when tested on three TREC reference collections, by about 23% when compared to L2R documents and about 10% when compared to other selective approaches, and that it offers an appropriate trade-off between system complexity and system effectiveness.
User Scored Evaluation of Non-Unique Explanations for Relational Graph Convolutional Network Link Prediction on Knowledge Graphs

Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms, or explanation methods, have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. In this paper, we introduce a method, including a dataset (FrenchRoyalty-200k), to benchmark explanation methods on the task of link prediction on KGs, when there are multiple explanations to consider. We conduct a user experiment, where users score each possible ground truth explanation based on their understanding of the explanation. We propose the use of several scoring metrics, using relevance weights derived from user scores for each predicted explanation. Lastly, we benchmark this dataset on state-of-the-art explanation methods for link prediction using the proposed scoring metrics.
Efficient Learning to Learn a Robust CTR Model for Web-scale Online Sponsored Search Advertising

Click-through rate (CTR) prediction is crucial for online sponsored search advertising. Several successful CTR models have been adopted in the industry, including the regularized logistic regression (LR). Nonetheless, the learning process suffers from two limitations: 1) Feature crosses for high-order information may generate trillions of features, which are sparse for online learning examples; 2) Rapid changing of data distribution brings challenges to the accurate learning since the model has to perform a fast adaptation on the new data. Moreover, existing adaptive optimizers are ineffective in handling the sparsity issue for high-dimensional features.
Analysing Agreement Among Different Evaluators in God Class and Feature Envy Detection

The automatic detection of Design Smells has evolved in parallel to the evolution of automatic refactoring tools. There was a huge rise in research activity regarding Design Smell detection from 2010 to the present. However, it should be noted that the adoption of Design Smell detection in real software development practice is not comparable to the adoption of automatic refactoring tools. On the basis of the assumption that it is the objectiveness of a refactoring operation as opposed to the subjectivity in definition and identification of Design Smells that makes the difference, in this paper, the lack of agreement between different evaluators when detecting Design Smells is empirically studied. To do so, a series of experiments and studies were designed and conducted to analyse the concordance in Design Smell detection of different persons and tools, including a comparison between them. This work focuses on two well known Design Smells: God Class and Feature Envy. Concordance analysis is based on the Kappa statistic for inter-rater agreement (particularly Kappa-Fleiss). The results obtained show that there is no agreement in detection in general, and, in those cases where a certain agreement appears, it is considered to be a fair or poor degree of agreement, according to a Kappa-Fleiss interpretation scale. This seems to confirm that there is a subjective component which makes the raters evaluate the presence of Design Smells differently. The study also raises the question of a lack of training and experience regarding Design Smells.
Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey

The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.
A multisource domain adaptation method for quality prediction in small-batch production systems

Quality prediction for small-batch production processes is a complex problem due to limitations in available training samples. In this study, a multisource domain adaptation joint-Y partial least square (PLS) method is proposed to learn the similarities between domains and use them to construct a quality prediction model. Without constraints on the number of source and target domains, the proposed method can transfer more historical information for the in-operation process than traditional methods. Numerical experiments and a real-world case study of quality prediction in computer wafer production are performed to verify the effectiveness of the proposed method. The results show that the prediction accuracy of the proposed method is high in cases with few training samples in the target domain compared to the accuracies of the joint-Y PLS model and the traditional PLS model.
Reduced Reference Quality Assessment for Image Retargeting by Earth Mover’s Distance

A reduced reference quality assessment algorithm for image retargeting by earth mover’s distance is proposed in this paper. In the reference image, all the feature points are extracted using scale invariant feature transform. Let the histograms of image patch around each feature point be local information, and the histograms of saliency feature as global information. Those feature information is extracted at the sender side and transmitted to the receiver side. After that, the same feature information extraction process is performed for the retargeted image at the receiver side. Finally, all feature information of the reference and retargeted images is used collectively to compute the quality of the retargeted image. An overall quality score is calculated from the local and global similarity measure using earth mover’s distance between reference and retargeted images. The key step in our algorithm is to provide an earth mover’s distance metric in a manner that indicates how the local and global information in the reference image is preserved in corresponding retargeted image. Experimental results show that the proposed algorithm can improve the image quality scores on four common criteria in the retargeted image quality assessment community.
International cooperation of Russian universities and academic mobility (based on self-examination reports)

The development of international cooperation along with the growing number of the participants of academic mobility are among the attributes of internationalization of education. Its positive effects are universally acknowledged, however take effort to be achieved. Based on the example of national research universities, the article examines the peculiarities of international activity and opportunities for the Russian universities to participate in the international academic mobility. Primary focus is given to the student international internships, which are considered as highly effective educational and mentoring practices. The theoretical framework for this research is comprised of a set of provisions that characterize international academic exchange as mutually beneficial interaction of different values, cultures, experience, knowledge, interests, and goals. The scientific novelty lies in introduction of the new empirical self-examination reports of the universities, use of various statistical data and rating results. The conclusion is made that international academic mobility has its own structure, pronounced geographical focus, and industry characteristics. Cooperation with individual foreign universities and enterprises, as well as with international university associations and branch associations is well developed and creates favorable environment for the advancement of international academic mobility. However, these opportunities are not used to the fullest, as testified by relatively low number of the Russian participants, prevalence of incoming flow vs outgoing, and unequal conditions for the capital and regional universities.
Iterative Hierarchical Attention for Answering Complex Questions over Long Documents

We propose a new model, DocHopper, that iteratively attends to different parts of long, hierarchically structured documents to answer complex questions. Similar to multi-hop question-answering (QA) systems, at each step, DocHopper uses a query $q$ to attend to information from a document, combines this ``retrieved'' information with $q$ to produce the next query. However, in contrast to most previous multi-hop QA systems, DocHopper is able to ``retrieve'' either short passages or long sections of the document, thus emulating a multi-step process of ``navigating'' through a long document to answer a question. To enable this novel behavior, DocHopper does not combine document information with $q$ by concatenating text to the text of $q$, but by combining a compact neural representation of $q$ with a compact neural representation of a hierarchical part of the document, which can potentially be quite large. We experiment with DocHopper on four different QA tasks that require reading long and complex documents to answer multi-hop questions, and show that DocHopper achieves state-of-the-art results on three of the datasets. Additionally, DocHopper is efficient at inference time, being 3--10 times faster than the baselines.