Reviewerly/exHarmony huggingface dataset:
Data Science Practices

As the field of transfer learning is deeply contributing in every area of research development, one of the field being image prediction needs improvement. This chapter will present the concept of trained models and then will move on to demonstrate how to use trained models for various applications. Feature extraction can be applied to pre-trained models, and fine-tuning can be applied to pre-trained models for classification of animals into cat and dog groups. This chapter will cover the important aspects of the transfer learning and its applications with improved precision and reduced training times using some trained models like VGG16 and ResNet50.
Research on Monocular Depth Estimation Algorithm Based on Structured Loss

This paper proposes a monocular depth estimation algorithm based on multi-scale structure similarity and gradient matching for improving the accuracy of monocular image depth estimation and solving the problems of inaccurate prediction of geometric shapes and blurred edges in the image. In this algorithm, a joint structured loss is formed by using multi-scale structure similarity degree loss and scale-invariant gradient matching loss. The relative depth points are sorted to achieve monocular depth estimation, which realizes accurate prediction of geometric shapes in the image, reduces edge blur, and improves depth prediction accuracy. Numerical experiments and subjective evaluations are performed on four different types of data sets: Ibims, NYUDv2, DIODE, and Sintel. The results show that the algorithm significantly reduces the depth prediction error, effectively improves the accuracy of the prediction, and has a certain generalization performance.
Accurate Prostate Cancer Detection and Segmentation Using Non-Local Mask R-CNN With Histopathological Ground Truth

We aim to develop deep learning (DL) models to accurately detect and segment intraprostatic lesions (IL) on biparametric MRI (bp-MRI).Three patient cohorts with ground truth IL delineated on different modalities were collected. 158 patients from two datasets had suspicious ILs delineated based on bp-MRI: 97 patients were from PROSTATEx-2 Challenge with biopsy result independent from bp-MRI based delineation, 61 patients were from IMPROD clinical Trial with biopsy done for each delineation; 64 patients from IMPROD clinical Trial had ILs identified and delineated by using whole mount prostatectomy specimen sections as reference standard; 40 private patients were unlabeled. We proposed a non-local Mask R-CNN to improve segmentation accuracy by addressing the imperfect registration issue between MRI modalities. We also proposed to post aggregate 2D predictions to estimate IL volumes within the whole prostatic gland and keep top-5 3D predictions for each patient. In order to explore the small dataset problem, we employed different learning techniques including transfer learning and semi-supervised learning with pseudo labelling. We experimented with two label selection strategies to see how they affected model performance. The first strategy kept only one prediction by referring to biopsy result, in order to minimize false positives; while the second strategy kept all top-5 predictions. 3D top-5 detection rate, dice similarity coefficient (DSC), 95 percentile Hausdorff Distance (95 HD, mm) and true positive ratio (TPR) were our evaluation metrics. We compared DL model prediction with prostatectomy-based ground truth delineation to accurately evaluate the boundary and malignancy level. We separately evaluated ILs of all Gleason Grade Group (GGG) and clinically significant ILs (GGG > 2).Main results are summarized in Table 1.Our proposed method demonstrates state-of-art performance in the detection and segmentation of ILs and shows great effectiveness for clinically significant ILs.
Geochemical characteristics of the Late Devonian basaltoids of the Kanin Peninsula and the Middle Timan

The petrochemistry and geochemistry of dolerites and basalts of the Late Devonian Kanin-Timan complex of the Kanin Peninsula and the Middle Timan are considered. Petrochemically, the rocks of the Kanin-Timan complex of the Kanin Peninsula and the Tsilma river area of the Middle Timan correspond to basaltoids of the normal range of alkalinity and partially to moderately alkaline varieties, and belong to the tholeiitic series. The least differentiated varieties are dolerites of the southeastern Kanin Peninsula, the most differentiated are the basalts of the river Tsilma of the Middle Timan. The lowest REE concentrations were found in the rocks of the central part of the Kanin Peninsula (36.5-56.8 g/t); in the same samples, the lowest LaN/YbN values were recorded (1.85 and 2.4, respectively), which indicates an increased degree of melting of the source. The highest REE concentrations were found in basalts from the river Tsilma (77.13-88.33 g/t), LaN/YbN values (2.49-2.7, respectively). The influence of the crustal component in the formation of melts from which rocks of the Kanin-Timan complex were formed, was established. The source of the melt was spinel-garnet lherzolite, the degree of melting varied from 10 to 30%. The maximum degree of melting was 30%, at which melts were formed, that gave rise to the least differentiated rocks of the Northern Timan and the central part of the Kanin Peninsula. The mantle source, that gave rise to the melts from which the rocks of the Kanin-Timan complex were formed, was enriched with subduction and crustal components, a similar type of source is characteristic of the basaltoids of the No-rilsk trough.
Fast estimation for robust supervised classification with mixture models

Label noise is known to negatively impact the performance of classification algorithms. In this paper, we develop a model robust to label noise that uses both labelled and unlabelled samples. In particular, we propose a novel algorithm to optimize the model parameters that scales efficiently w.r.t. the number of training samples. Our contribution relies on a consensus formulation of the original objective function that is highly parallelizable. The optimization is performed with the Alternating Direction Method of Multipliers framework. Experimental results on synthetic datasets show an improvement of several orders of magnitude in terms of processing time, with no loss in terms of accuracy. Our method appears also tailored to handle real data with significant label noise.
Technology-Based Methods for Creative Teaching and Learning of Foreign Languages

Integrative trends in the world, the expansion of intercultural interaction, and rapid social development contribute to the mastery of one or several foreign languages, not only to communicate with the representatives of other cultures but also to improve professional skills, as modern society requires specialists to solve global problems. The purpose of this study is to analyze the existing and most commonly used methods of teaching a foreign language and methods of learning a foreign language in terms of their effectiveness in the opinion of teachers and students. The combination of creativity and new technologies in teaching and learning a foreign language reach the mentioned aimss. The basis for the current research consists in scientific work by professionals from different countries, covering the issues of teaching foreign languages, as well as the results of a teachers’ questionnaire and bachelors degree students. The empirical methods of the present research comprise studying and analyzing the works of some Russian and European scientists and teachers; interviewing teachers and students in the form of an anonymous questionnaire. Theoretical methods include analysis, synthesis, comparison, generalization, deduction, and induction. From among the great variety of methods the ones most commonly used are those related to ICT. Also, the majority of teachers recognize training video creation as most effective for the formation of students’ creative abilities. According to the students’ opinion, that type of activity has also the greatest learning effect, and stimulates creative abilities, rendering the educative process more interesting and efficient.
What do developers discuss about code comments

Code comments are important for program comprehension, development, and maintenance tasks. Given the varying standards for code comments, and their unstructured or semi-structured nature, developers get easily confused (especially novice developers) about which convention(s) to follow, or what tools to use while writing code documentation. Thus, they post related questions on external online sources to seek better commenting practices. In this paper, we analyze code comment discussions on online sources such as Stack Overflow (SO) and Quora to shed some light on the questions developers ask about commenting practices. We apply Latent Dirichlet Allocation (LDA) to identify emerging topics concerning code comments. Then we manually analyze a statistically significant sample set of posts to derive a taxonomy that provides an overview of the developer questions about commenting practices. Our results highlight that on SO nearly 40% of the questions mention how to write or process comments in documentation tools and environments, and nearly 20% of the questions are about potential limitations and possibilities of documentation tools to add automatically and consistently more information in comments. On the other hand, on Quora, developer questions focus more on background information (35% of the questions) or asking opinions (16% of the questions) about code comments. We found that (i) not all aspects of comments are covered in coding style guidelines, e.g., how to add a specific type of information, (ii) developers need support in learning the syntax and format conventions to add various types of information in comments, and (iii) developers are interested in various automated strategies for comments such as detection of bad comments, or verify comment style automatically, but lack tool support to do that.
Error-Divisible Two-Qubit Gates

We introduce a simple widely applicable formalism for designing ``error-divisible'' two-qubit gates: a quantum gate set where fractional rotations have proportionally reduced error compared to the full entangling gate. In current noisy intermediate-scale quantum (NISQ) algorithms, performance is largely constrained by error proliferation at high circuit depths, of which two-qubit gate error is generally the dominant contribution. Further, in many hardware implementations, arbitrary two-qubit rotations must be composed from multiple two-qubit stock gates, further increasing error. This work introduces a set of criteria, and example wave forms and protocols to satisfy them, using superconducting qubits with tunable couplers for constructing continuous gate sets with significantly reduced leakage and dynamic $ZZ$ errors for small-angle rotations. If implemented at scale, NISQ-algorithm performance could be significantly improved by our error-divisible gate protocols.
Electronic Structure Theory Calculations Using Modern Architectures: KNL vs Haswell

The time to solution and parallel efficiency of several commonly used electronic structure methods (Hartree-Fock, density functional theory, second order perturbation theory, resolution of the identity second order perturbation theory, coupled cluster) are evaluated on both the Intel Xeon Haswell and the Intel Xeon Phi Knights Landing (KNL) architectures. The Haswell completes the benchmark calculations with a faster time to solution than the KNL for all molecules and methods tested. While the Haswell exhibits an average speedup of at least 3.5 relative to the KNL for all nonthreaded computations, the KNL has a better parallel efficiency than the Haswell with increasing core counts. The architectures are further tested using a more computationally costly coupled cluster method on a transition state reaction. The Haswell appears to be the best choice to minimize the time to solution, though for very large systems and high levels of theory that require memory intensive processes the superior memory hierarchy and larger on node memory of the KNL can make it a better choice. These results are used to showcase aspects of novel architectures that will increase efficiency for quantum chemistry applications.
E-SKILLS AND DIGITAL LITERACY FOR FOREIGN LANGUAGES EDUCATION: STUDENT CASE STUDY IN UKRAINE

The COVID-19 pandemic induced amplified digitalization measures in the higher education sphere, informed by the need to take quick comprehensive action to achieve the overarching result to transform educational scenarios into interdisciplinary digital, remote, and hybrid frameworks.The consequent functional tasks to meet this challenge are estimated as 1) to activate comprehensive complex skillsets, otherwise latent or underutilized in the educational process; 2) to boost ICT competence and digital literacy of all participants of the educational process, relocated to the computer realm.The study objective is to diagnose and critically review the empirical case of digital skills viral development and application to construe interdisciplinary competencies of students of European and Oriental Languages major programs in Ukraine, employed in the years 2020-2021 due to quarantine measures.The study premise is the identification of various competency principles, derivative of twenty-first-century skills, and projected digital literacy requirements.The survey method and diagnostic analysis of different digital literacy components and e-skills implementation are used to assess the parameters of efficiency of transforming real-life linguistic education practices into the digital and hybrid format.The investigation novelty is in the identification of the interoperability between various groups of applied e-skills and soft skills, instrumental to develop interdisciplinary professional awareness of Foreign Languages students in Ukraine.The study allows detection of challenges for actual and underdeveloped skills (hard, technical, and soft), that participants of the educational process encountered through digital format adaptation in programs of European and Oriental Languages.
Assessing Appropriate Technologies for Sustainable Irrigation Practices in Muljipura Village, India

Forty percent of India’s water supply is attributed to groundwater. Muljipura, an agriculture-based village in the northern state of Madhya Pradesh, India, primarily utilizes groundwater and rainwater for irrigation, with a greater reliance on groundwater during non-monsoon seasons. Due to over dependence on groundwater, there was a significant decline in groundwater levels between 2007 and 2017. Now the rate of replenishment is unable to meet the demands of villagers. This paper offers a brief assessment of four appropriate technological solutions derived from literature and results obtained from Participatory Rural Appraisal and Human Centered Design methodological approaches. Both participatory-centric methods aided in collecting primary data to identify salient characteristics of Muljipura village and specific challenges faced by villagers. Primary data were combined with secondary data to assess four sustainable solutions. This study recommends an Internet of Things (IoT)-based appropriate technological solution to address rapid depletion of groundwater in the village of Muljipura.
Impact of Self-Efficacy, Organizational Commitment and Job Involvement on Job Performance in Private Bank Employees

Present scenario and organizational environment is volatile and uncertain, and it creates hindrances in the accomplishment of individual goals and organizational goals as well and after demonetization private banking sector is going through high work pressure and highly uncertain work environment. To remove the encumbrance of uncertainty and to grow in an efficient, effective and productive manner organizations need employees who are confident, committed and involved in their job and finally give the best results. Committed and confident employees are the backbone of any organization and it requires a great amount of effort to understand that which factors affects the level of commitment, level of involvement and level of confidence the most. The purpose of this study is to explore the impact of self-efficacy, organizational commitment and job involvement on job performance among private bank employees of Uttarakhand and Uttar Pradesh. Exploratory Factor Analysis extracted the two factors of self-efficacy, four factors of organizational commitment, two factors of job involvement and two factors of job performance. Further multiple regression analysis reveals that self-efficacy have significant impact on job performance whereas organizational commitment and job involvement have insignificant impact. Although various studies have been done previously using aforesaid variables but there is a lack of literature of the collective study in private banking sector in Indian context. Several researches mentioned that this kind of study can be performed for further development in related research. Hence the researcher attempted to validate the scales which will be helpful for academicians, researchers and for those who have stake in this study to formulate the strategies for organizational success.
EFL Teachers’ Problems and Solutions in Teaching English to Students with Intellectual and Developmental Disability

Sekolah Luar Biasa (SLB) is a school specialized for students with special needs, particularly students with Intellectual and Developmental Disability (IDD). In Indonesia, the government guarantees that they have the same right to study formally and informally; and to get the best teachers in the teaching and learning process. In English language teaching, however, even qualified teachers have problems while teaching English to students with IDD. Despite the urgency to solve the problems faced by teachers who teach English or a foreign language to special needs students, especially IDD, there are only a few articles covering this topic. This study, which uses a descriptive qualitative method, aims at uncovering the problems and solutions of five English teachers in teaching students with IDD in SLB Pembina Tingkat Nasional Bagian C Malang. In order to obtain the needed data, an observation checklist, a field note, and an interview guide were utilized. The findings revealed that problems faced by the teachers were related to teachers’ English proficiency, methods and media used in teaching, and students’ ability in learning. The teachers used different solutions to overcome the problems, but the most common solution was using repetition in teaching and practicing. This solution was used as the students had difficulties in understanding and remembering the lesson. The other solutions used during the teaching and learning process include YouTube videos, English learning application, posters, and BSE.
Computational Text Analysis

The increasing availability of large digital text collections, both historical and contemporary, poses challenges for traditional text analysis methods. At the same time, methodological advances in the area of computational text analysis make it possible to study large bodies of texts. Instead of restricting the analysis to a limited sample of the collection, computational methods make it possible to identify structural patterns across the complete corpus. Central for these methods is the concept of collocation, i.e., the observation that some words regularly co-occur and that studying these phenomena enables insights about contextual meaning. The chapter presents an overview of different approaches that work on the local level of individual collocations, the intermediate level of collocation networks and the global level of corpus-wide patterns. Emphasis is put on the example of topic modeling as an advanced method of computational text analysis that identifies recurring themes across a large text corpus and allows studying their development. The method's theoretical basis, as well as practical requirements and procedures, are discussed.
Towards robust vision by multi-task learning on monkey visual cortex

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to image distortions is surprisingly fragile. In contrast, the mammalian visual system is robust to a wide range of perturbations. Recent work suggests that this generalization ability can be explained by useful inductive biases encoded in the representations of visual stimuli throughout the visual cortex. Here, we successfully leveraged these inductive biases with a multi-task learning approach: we jointly trained a deep network to perform image classification and to predict neural activity in macaque primary visual cortex (V1). We measured the out-of-distribution generalization abilities of our network by testing its robustness to image distortions. We found that co-training on monkey V1 data leads to increased robustness despite the absence of those distortions during training. Additionally, we showed that our network's robustness is very close to that of an Oracle network where parts of the architecture are directly trained on noisy images. Our results also demonstrated that the network's representations become more brain-like as their robustness improves. Using a novel constrained reconstruction analysis, we investigated what makes our brain-regularized network more robust. We found that our co-trained network is more sensitive to content than noise when compared to a Baseline network that we trained for image classification alone. Using DeepGaze-predicted saliency maps for ImageNet images, we found that our monkey co-trained network tends to be more sensitive to salient regions in a scene, reminiscent of existing theories on the role of V1 in the detection of object borders and bottom-up saliency. Overall, our work expands the promising research avenue of transferring inductive biases from the brain, and provides a novel analysis of the effects of our transfer.
Automated and Adaptive Geometry Preparation for AR/VR-Applications

Abstract Product visualization in AR/VR applications requires a largely manual process of data preparation. Previous publications focus on error-free triangulation or transformation of product structure data and display attributes for AR/VR applications. This paper focuses on the preparation of the required geometry data. In this context, a significant reduction in effort can be achieved through automation. The steps of geometry preparation are identified and examined with respect to their automation potential. In addition, possible couplings of sub-steps are discussed. Based on these explanations, a structure for the geometry preparation process is proposed. With this structured preparation process it becomes possible to consider the available computing power of the target platform during the geometry preparation. The number of objects to be rendered, the tessellation quality and the level of detail can be controlled by the automated choice of transformation parameters. We present a software tool in which partial steps of the automatic preparation are already implemented. After an analysis of the product structure of a CAD file, the transformation is executed for each component. Functions implemented so far allow, for example, the selection of assemblies and parts based on filter options, the transformation of geometries in batch mode, the removal of certain details and the creation of UV maps. Flexibility, transformation quality and time savings are described and discussed.
Porttinari - a Large Multi-genre Treebank for Brazilian Portuguese

This paper presents the project of a large multi-genre treebank for Brazilian Portuguese, called Porttinari. We address relevant research questions in its construction and annotation, reporting the work already done. The treebank is affiliated with the “Universal Dependencies” international model, widely adopted in the area, and must be the basis for the development of state of the art tagging and parsing systems for Portuguese, as well as for conducting linguistic studies on morphosyntax and syntax for this language.