Reviewerly/exHarmony huggingface dataset:
GAN Mask R-CNN:Instance semantic segmentation benefits from generativeadversarial networks

In designing instance segmentation ConvNets that reconstruct masks, segmentation is often taken as its literal definition -- assigning label to every pixel -- for defining the loss functions. That is, using losses that compute the difference between pixels in the predicted (reconstructed) mask and the ground truth mask -- a template matching mechanism. However, any such instance segmentation ConvNet is a generator, so we can lay the problem of predicting masks as a GANs game framework: We can think the ground truth mask is drawn from the true distribution, and a ConvNet like Mask R-CNN is an implicit model that infers the true distribution. Then, designing a discriminator in front of this generator will close the loop of GANs concept and more importantly obtains a loss that is trained not hand-designed. We show this design outperforms the baseline when trying on, without extra settings, several different domains: cellphone recycling, autonomous driving, large-scale object detection, and medical glands. Further, we observe in general GANs yield masks that account for better boundaries, clutter, and small details.
Harnessing the Power of the Second Quantum Revolution

The second quantum revolution has been built on a foundation of fundamental research at the intersection of physics and information science, giving rise to the discipline we now call Quantum Information Science (QIS). The quest for new knowledge and understanding drove the development of new experimental tools and rigorous theory, which defined the roadmap for second-wave quantum technologies, including quantum computers, quantum-enhanced sensors, and communication systems. As technology has matured, the race to develop and commercialize near-term applications has accelerated. In the current regime of Noisy Intermediate Scale Quantum (NISQ) devices, the continued necessity of basic research is manifest. Under what conditions can we truly harness quantum complexity and what are its implications for potential useful applications? These questions remain largely unanswered, and as the QIS industry ramps up, a continuous feedback between basic science and technology is essential. In this Perspective I review how curiosity-driven research led to radical new technologies and why the quest for basic understanding is essential for further progress.
4Ei-Digital Pedagogy Model for Teacher Education

Digital pedagogy integration plays a significant role in stimulating student-teacher enthusiasm in building productive connections to simulated conditions. This chapter focuses on the effective outcomes concerning student-teacher potential in how to acquire teaching styles and towards teaching as a profession. Significant research has been given in the dimension of commitment and professional growth impact on student-teachers learning in the education context. The 4Ei thematic framework model is effective and expects the level at which this technology is enhancing student-teachers digital pedagogy skills. The 21st-century learners use digital pedagogy approaches to influence their expectations with 24×7 multiple media technologies. Nowadays student-teachers technologies are like multimedia, which has an effective communication interface with comprehensive cooperation and association. Student-teachers like to choose with online capabilities that would be inattentive to ignore the opportunity to engage with technology toward their purposes. It is this mode of training that the student-teachers develop to fulfill their interest in teaching and pedagogy.
Quantum mechanics with non-negative distribution and measurements

The issues of interaction between the measuring device and the micro system have not yet been completely resolved. Moreover, if at the dawn of quantum mechanics attention was paid to the influence of the device on the state of the system, then later (D.V. Blokhintsev) interest in the inverse problem (and, in essence, more traditional) - on the influence of the micro-object on the state of the macro-system increased. Measuring instruments consist of a number of interconnected links. The quantum theory of measurements asserts that the first cascades can be quantum, but the last should be classical (in the current terminology - an analyzer and a detector). This paper discusses the integration of such an approach to measuring instruments in the formalism of quantum mechanics. Test (auxiliary) functions in quantum mechanics with nonnegative distribution functions (quantum mechanics Kuryshkin-Wodkevich - QDF), in fact, can and should be associated with the measurement process and measuring instruments. Our thesis is that these functions should be related to the measuring instrument used and describe its interaction with the system. It turns out that exactly the non-Hermitian character of the Hamiltonians in QDF expresses this connection. Thus, the QDF, which has long been positioned as an alternative and exotic quantum theory, is increasingly taking shape as a quantum theory of measurements. Only a short time ago, in our works the meaning of this formalism as the theory of quantum measurements was clarified. The analogies of auxiliary functions in the QDF theory with wave functions in the examples of D. Blokhintsev measuring instruments mentioned in this paper, are promising areas of research which, in the framework of the study of quantum entanglement, can give a significant impetus to the development of quantum cryptography. This work, in this sense, should become a turning point in this reversal of the QDF to new applications.
Performance Analysis of Structural Similarity in Mammograms

This article introduces a new method to microcalcification discovery in digital mammograms, in which the classifiers are designed using the blend of unseen upper hand transformation and synthetic neural networks. Microcalcification diagnosis is actually carried out through drawing out the microcalcification homes from the graphic curve coefficients, as well as these results are actually used as semantic network input for distinction. The neural network has one input, 2 hidden layers as well as one outcome. The body classifies mammography graphics as healthy or uncommon and also the irregular intensity as curable or fatal. Experiments reveal that our technique can easily deliver a much better result. The system is actually examined in the Mammography Image Evaluation data source.
Adaptive Structured Sparse Network for Efficient CNNs with Feature Regularization.

Neural networks have made great progress in pixel to pixel image processing tasks, e.g. super resolution, style transfer and image denoising. However, recent algorithms have a tendency to be too structurally complex to deploy on embedded systems. Traditional accelerating methods fix the options for pruning network weights to produce unstructured or structured sparsity. Many of them lack flexibility for different inputs. In this paper, we propose a Feature Regularization method that can generate input-dependent structured sparsity for hidden features. Our method can improve sparsity level in intermediate features by 60% to over 95% through pruning along the channel dimension for each pixel, thus relieving the computational and memory burden. On BSD100 dataset, the multiply-accumulate operations can be reduced by over 80% for super resolution tasks. In addition, we propose a method to quantitatively control the level of sparsity and design a way to train one model that supports multi-sparsity. We identify the effectiveness of our method for pixel to pixel tasks by qualitative theoretical analysis and experiments.
The Fixed-Point Implementations for Recurrent Neural Networks

In this paper, the learning efficiency of the single-layer and multiple-layer locally recurrent neural networks (RNN) were investigated. In the RNN structure, piecewise linear activation functions were used. In addition, infinite impulse response digital filter played the role of signal recursions. In RNN implementation, pole-L <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> sensitivity minimization was performed. The weight of every neuron was adjusted by using the back-propagation (BP) learning algorithm. Simulation results show that multilayer RNN may have better learning performance. In addition, the RNN with optimal IIR filter implementation may have better learning performance than that of the canonical-form realization.
Social awareness and child care teachers' perception about rights and human rights of children and child care teachers

본 연구는 아동 및 보육교사에 대한 권리와 인권에 대한 사회적 인식과 보육교사의 인식 정도를 살펴보고, 이를 토대로 영유아와 보육교사의 권리와 인권교육 및 인식을 제고하는데 기여하고자 수행되었다. 이러한 목적을 위해 아동과 보육교사에 대한 권리와 인권에 대한 사회적 인식이 어떠한지와 아동과 보육교사에 대한 권리와 인권 실천을 위한 주관적 인식이 어떠한지를 연구문제로 설정하였다. 아동과 보육교사의 권리와 인권에 대한 사회적 인식을 알아보기 위하여 뉴스빅테이터 분석 서비스 사이트인 빅카인즈를 활용해 키워드를 추출하였다. 또한 아동과 보육교사의 권리(인권) 실천을 위한 보육교사의 주관적 인식을 알아보기 위해 2019년 6~7월 K지역의 보육교사 보수교육에 참여한 134명을 대상으로 설문을 실시하였다. 빅카인즈를 활용하여 관련 내용을 검색하고 검색한 내용에 대한 키워드 분석, 연관어 분석 등을 활용하였다. 보육교사의 일반적인 특성과 설문내용을 분석하기 위하여 빈도분석을 사용하였다. 또한 교사들의 주관적 인식에 대한 내용은 빅카인즈의 ‘용어, 개체 분석’을 통해 키워드를 생성하였다. 연구결과, ‘아동학대’가 아동의 권리와 인권을 침해하는 가장 강력한 문제로 나타났으며, 아동과 보육교사의 권리와 인권에 대한 용어 중 인권이라는 용어를 더 선호하였다. 아동권리(인권) 실천을 위한 노력으로는 아동의 마음과 생각을 이해하고 존중하기 위해 노력해야 한다는 의견을 많이 기술하였다. 아동권리(인권) 실천에 대한 어려움으로는 자기의 자녀만을 잘 봐 주기를 기대하는 학부모를 가장 큰 요인으로 들었다.This study extracted to find out social perceptions and childcare teachers’ perceptions about children s and teachers rights and human rights. Based on this, it aims to contribute to raising awareness and education on the rights and human rights of children and child care teachers. For this purpose, first, a research question was set on the social perception of the rights and human rights of children and childcare teachers. Next, the subjective perception of rights and human rights for children and childcare teachers was set as a research question. In order to find out the social awareness of the rights and human rights of children and childcare teachers, keywords were extracted using Bigkines., a news big data analysis service site. In addition, in order to find out the subjective perceptions of childcare teachers for the implementation of the rights (human rights) of children and childcare teachers, a questionnaire was conducted for 134 children who participated in the maintenance education for childcare teachers in the K area from June to July 2019. By using Bigkines, we searched for related contents and used keyword analysis, and related word analysis for the searched contents. Frequency analysis was used to analyze general characteristics and questionnaire contents of childcare teachers. In addition, for the subjective perception of teachers, keywords were generated through ‘the analysis of terms and objects’ of Bigkines. As a result of the study, keyword analysis of the rights and human rights of children and childcare teachers showed that child abuse is the most powerful problem that infringes on children s rights and human rights. It appears that the term human rights is preferred. As an effort to practice child rights (human rights), many comments have been described that efforts should be made to understand and respect children s hearts and thoughts. As for the difficulty in practicing child rights (human rights), parents who expect to see only their children are the biggest factor.
Weakly-Supervised Semantic Segmentation via Self-Regularization

Author(s): Chang, Yu-Ting | Advisor(s): Yang, Ming-Hsuan | Abstract: The goal of semantic segmentation is to assign a semantic category to each pixel in the image. It has been one of the most important tasks in computer vision that enjoys a wide range of applications such as image editing and scene understanding. Recently, deep convolutional neural network (CNN) based methods have been developed for semantic segmentation and achieved significant progress. However, such approaches rely on learning supervised models that require pixel-wise annotations, which take extensive effort and time. To reduce the effort in annotating pixel-wise ground truth labels, numerous weakly-supervised methods are proposed using various types of labels such as image-level, bounding box, point-level, and scribble-based labels. In this thesis, we focus on using image-level labels which can be obtained effortlessly, yet a more challenging case under the weakly-supervised setting.Existing weakly-supervised semantic segmentation methods using image-level annotations typically rely on initial responses to locate object regions. However, such response maps generated by the classification network usually focus on discriminative object parts, due to the fact that the network does not need the entire object for optimizing the objective function. To address this issue, we improve the generated response map by enforcing the network to pay attention to other parts of an object via self-regularization techniques. First, we apply the mixup data augmentation to effectively calibrate the model uncertainty on overconfident predictions, which enables the model to attend to more object regions. Second, we introduce a self-supervised task that discovers sub-categories in an unsupervised manner. By imposing a more challenging task, the model learns better representations, thereby improving the response map. Based on the proposed two self-regularization methods, the produced initial responses are more complete and balanced across object regions, which facilitates the latter steps for weakly-supervised semantic segmentation, i.e., response refinement and segmentation model training.
Impact of Dimensionality on the Evaluation of Stream Data Clustering Algorithms

Handling stream data is a tedious task. Recently numerous techniques are presented for analysing stream data. Stream data clustering is one of the important tasks in stream data mining. A number of application programming interfaces (APIs) are available for implementing the stream data clustering. These APIs can handle the stream data of any dimension. The objective of the presented paper is to explore the impact of dimensionality over the existing standard data stream clustering algorithms. Selected standard data stream clustering algorithms are compared for different dimensions of stream using six performance parameters, namely adjusted Rand index, Dunn index, entropy, F1 measure, purity and within cluster sum of square measure.
킨슬러가(家)의 현지인 중심적 선교

The Kinslers have influenced Korea according to the nation’s needs in various ways to this day. Francis Kinsler served in the schools and church, and created a Bible Club during the Japanese colonial period. After Korea’s liberation, he rebuilt the church and schools. During the Korean War, he organized relief projects for refugees. Arthur Kinsler worked at the Bible Club, Soongsil University, and the Light-house Foundation. In particular, Arthur and his wife, Sue worked for the peace and unification of Korea since they visited North Korea in 1998. Their mission is summarized as ‘native-centered mission.’ The missions of the Kinslers were based on the Bible and worked to meet the needs of locals, but the founder’s dedication has become discredited over time. The Korean mission is recommended to be modeled on the Kinslers’ and move towards glocalization mission.
Array-based convolutional neural networks for automatic earthquake detection and 4D localization

The growing amounts of seismic data necessitates efficient and effective methods to monitor earthquakes. Current methods are computationally expensive, ineffective under noisy environments, or labor-intensive. We leverage advances in machine learning to propose an improved solution - a convolutional neural network that uses array data to seamlessly detect and localize events. When testing this methodology with events at Hawai‘i, we achieve 99.4% accuracy and predict hypocenter locations within a few kilometers of the U.S. Geological Survey catalog. We demonstrate that training with relocated earthquakes reduces localization errors significantly. We outline several ways to improve the model, including enhanced data augmentation and use of relocated offshore earthquakes recorded by ocean bottom seismometers. Application to continuous records shows that our algorithm detects 6 times as many earthquakes as the published catalog. Due to the enhanced detection sensitivity, localization granularity, and minimal computation costs, our solution is valuable, particularly for real-time earthquake monitoring.
Dynamism in Financial Market Regulation: Harnessing Regulatory and Supervisory Technologies

The dynamic development of market practices and services frequently limits regulatory effectiveness. New technologies, however, might assist regulators in better tracking market changes. While Regulatory Technology (RegTech) has been vastly reducing compliance costs, Supervisory Technology (SupTech) has the potential to enhance data accuracy even further. Proper integration between these two will assist regulators in obtaining a continuously updated picture of their regulatees and allow higher regulatory adaptability, without incurring extensive additional costs. Still, harnessing technology for regulatory purposes might lead to an increased dependence on technology providers which risks regulatory capture. We argue in this essay that additional requirements, such as technological neutrality and interoperability, are needed to mitigate such risks. We illustrate our case through blockchain proposals for RegTech and SupTech and their interoperability challenge.
Meta-resource opportunities reflective mechanisms of life successfulness, hope and hardiness of a modern person

The article contains theoretical and empirical data on the study of metaresource opportunities of the mechanisms of a person’s life successfulness and their relationship with positive attitudes towards life and the achievement of life goals. The relevance of the scientific problem is to find new approaches to the understanding and study of the success of the individual in the modern world. The novelty of the study in testing the concept of life successfulness of a person as a meta-resource in a selfregulation system that performs the function of a reflective comparison of social and individual value bases in order to build its own concept of a successful life and acts as a system of interconnected mechanisms, personality traits and conditions. The methodological foundations in understanding life successfulness are the principles of regulatory, subjective and resource approaches. The purpose of the study: to study the degree of formation of mechanisms of life successfulness, as well as to identify their relationship with a positive motivational attitude and disposition of personality hardiness in a sample of respondents at the beginning of the professionalization process. The correlation analysis allowed to judge whether the relationship mechanisms life successfulness existence of interconnections of positive motivational attitude and personality hardiness. This suggests that respondents with more pronounced reflective mechanisms of life successfulness have a positive motivational attitude towards life, are involved in the situation and are able to manage it, open to receiving life experience, and self-confident. The revealed relationship between the variables indicates the presence of a complex regulatory mechanism of the personality, which allows developing and realizing the meta-resource opportunities of the mechanisms of life successfulness.
A Critical Pragmatic Analysis of Fans’ Online Expressions

With the continuous development of various social media platforms and the expansion of the fan base, the fan culture has gradually entered the public’s vision. Internet slang of fans is an important part of fan culture, and many current Internet buzzwords also come from it. From the perspective of critical pragmatics, this paper first analyzes the word-formation characteristics of fans’ online expressions, and then critically analyzes the reasons for the popularity of fans’ online expressions with the relevant theories of pragmatics. It is hoped that it can provide some reference and inspiration for the study of network language and critical pragmatics.
La formación de archivistas en el contexto de las tecnologías disruptivas: el caso del aprendizaje situado en la ENES Morelia (UNAM)

The objective of this work is to reflect on the use of situated learning strategies in the educational practice of the Archives and Document Management program at ENES Morelia, UNAM , specifically on the subjects related to digital archives and disruptive technologies. We will focus on the learning situations that areproposed to the students to offer authentic contexts closer to current professional life. We envision that the evolution of the archival environment will requirenew professionals to possess tools to accomplish the pending tasks of the past, but it will also require them to get involved in spaces of dialogue to create thenew document management scenarios.
Special issue on Hybrid Artificial Intelligence Systems from HAIS 2018 Conference

Depth cues with affluent spatial information have been proven beneficial in boosting salient object detection (SOD), while the depth quality directly affects the subsequent SOD performance. However, it is inevitable to obtain some low-quality depth cues due to thelimitations of its acquisition devices, which can inhibit the SOD performance. Besides, existing methods tend to combine RGB images and depth cues in a direct fusion or a simple fusion module, making them not effectively exploit the complex correlations between the two sources. Moreover, few methods design an appropriate module to fully fuse multi-level features, resulting in cross-level feature interaction insufficient. To address these issues, we propose a novel Multi-level Cross-modal Interaction Network (MCI-Net) for RGB-D based SOD. Our MCI-Net includes two key components: 1) a cross-modal feature learning network, which is used to learn the high-level features for the RGB images and depth cues, effectively enabling the correlations between the two sources to be exploited; and 2) a multi-level interactive integration network, which integrates multi-level cross-modal features to boost the SOD performance. Extensive experiments on six benchmark datasets demonstrate the superiority of our MCI-Net over 14 state-of-the-art methods, and validate the effectiveness of different components in our MCI-Net. More important, our MCI-Net significantly improves the SOD performance as well as has a higher FPS.