Reviewerly/exHarmony huggingface dataset:
Discriminative Multi-level Reconstruction under Compact Latent Space for One-Class Novelty Detection

In one-class novelty detection, a model learns solely on the in-class data to single out out-class instances. Autoencoder (AE) variants aim to compactly model the in-class data to reconstruct it exclusively, thus differentiating the in-class from out-class by the reconstruction error. However, compact modeling in an improper way might collapse the latent representations of the in-class data and thus their reconstruction, which would lead to performance deterioration. Moreover, to properly measure the reconstruction error of high-dimensional data, a metric is required that captures high-level semantics of the data. To this end, we propose Discriminative Compact AE (DCAE) that learns both compact and collapse-free latent representations of the in-class data, thereby reconstructing them both finely and exclusively. In DCAE, (a) we force a compact latent space to bijectively represent the in-class data by reconstructing them through internal discriminative layers of generative adversarial nets. (b) Based on the deep encoder's vulnerability to open set risk, out-class instances are encoded into the same compact latent space and reconstructed poorly without sacrificing the quality of in-class data reconstruction. (c) In inference, the reconstruction error is measured by a novel metric that computes the dissimilarity between a query and its reconstruction based on the class semantics captured by the internal discriminator. Extensive experiments on public image datasets validate the effectiveness of our proposed model on both novelty and adversarial example detection, delivering state-of-the-art performance.
Alcune osservazioni sui frammenti tramandati dai lessici

No AccessAlcune osservazioni sui frammenti tramandati dai lessiciRenzo TosiRenzo TosiSearch for more papers by this authorhttps://doi.org/10.13109/9783946317999.39SectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInRedditEmail About Previous chapter Next chapter FiguresReferencesRelatedDetails Download book coverStudia ComicaVolume 11 1st editionISBN: 978-3-946317-26-5 eISBN: 978-3-946317-99-9HistoryPublished online:February 2021 PDF download
Real or fake? Fourier analysis of fenerative adversarial network fundus images

With the increasing use of deep learning methodologies in various biomedical applications, there is a need for a large number of labeled medical image datasets for training and validation purposes. However, the accumulation of labeled datasets is expensive and time consuming. Recently, generative adversarial networks (GAN) have been utilized to generate synthetic datasets. Currently, the accuracy of generative adversarial networks is calculated using a structural similarity index measure (SSIM). SSIM is not adequate for comparison of images as it underestimates the distortions near hard edges. In this paper, we compare the real DRIVE dataset to the synthetic FunSyn-Net using Fourier transform techniques and show that Fourier behavior is quite different in the two datasets, especially at high frequencies. It is observed that for real images, the amplitude of the Fourier components exponentially decreased with increasing frequency. For the synthesized images, the rate of decrease of the amplitude is much slower. If a linear function is fit to the high frequency components, the slope distributions for the two datasets are completely different with no offset. The average slope in the log scale for DRIVE dataset and FunSyn-Net were 0.0195, and 0.009 respectively. We also looked at auto correlations for the horizontal cut of the Fourier transform and again saw a statistically significant difference between the means for the two datasets. Finally, we also observed that Fourier transforms with real images have higher magnitude squared coherence as compared to the synthesized images. Fourier transform has shown great success for finding differences between real and synthesized images and can be used to improve the synthesized GAN models.
3D DP Improved Algorithm Based on Bending Hierarchy Structure Identification

Abstract 3D Douglas-Peucker (DP) algorithm is an effective 3D discrete point data synthesis algorithm. In view of over-synthesis of compound bending during simplification of 3D DP algorithm with additional bending adjustment index, this paper designs a bending hierarchical structured algorithm to improve the original algorithm. Bending convexity threshold is introduced to merge the bending operation, curve skeletons with different scales are selected through statistics of bending height distribution. Bending adjustment index on different hierarchies of bending skeleton is calculated for synchronous synthesis between line features and DEM. The experimental results show that the structuring process well retains the original details of the curve, clearly presents the overall curve trend with obvious simplification effect, thus solving the problem of over-synthesis of compound bending, so that the curve displays different bending hierarchy structures. The simplification result demonstrates completeness of geographical significance and similarity between the graphics before and after the simplification.
Stereovision surface stitching for image updating in open spine surgery

Preoperative CT (pCT) images acquired in a supine position can be inaccurate for intraoperative image guidance in open spine surgery due to alignment change between supine pCT and intraoperative prone positioning. We have developed a level-wise registration framework to compensate for the alignment change using intraoperative stereovision (iSV) data of the exposed spine. A hand-held stereovision system was developed, but the field of view was limited to 1-2 segments per image. Although multiple iSV surfaces can be combined to capture the full field based on tracking information, acquisition is limited to one snapshot at a time with minimized hand motion due to asynchronization between image and tracking data. In this study, we developed methods to concatenate iSV surfaces without relying on tracking information, and illustrate the methods using data acquired from a pig spine. To register two iSV surfaces, the 2D texture maps were registered using an optical flow algorithm, and the 3D point cloud of the second iSV surface was registered with the first using 3D spatial information of each pixel. The two registered iSV surfaces were then merged to form one composite iSV surface. Multiple iSV surfaces were stitched sequentially. Results from 4 image pairs show that 2D and 3D registration accuracy was 2.7±0.6 pixels and 1.0±0.1 mm, respectively, across 8 landmarks. The overall accuracy of the final composite surface was 0.9±0.4 mm. These preliminary data show that iSV can potentially be acquired at video rate to improve efficiency to recover the full surgical field.
On the Importance of Looking at the Manifold

Data rarely lies on uniquely Euclidean spaces. Even data typically represented in regular domains, such as images, can have a higher level of relational information, either between data samples or even relations within samples, e.g., how the objects in an image are linked. With this perspective our data points can be enriched by explicitly accounting for this connectivity and analyzing them as a graph. Herein, we analyze various approaches for unsupervised representation learning and investigate the importance of considering topological information and its impact when learning representations. We explore a spectrum of models, ranging from uniquely learning representations based on the isolated features of the nodes (focusing on Variational Autoencoders), to uniquely learning representations based on the topology (using node2vec) passing through models that integrate both node features and topological information in a hybrid fashion. For the latter we use Graph Neural Networks, precisely Deep Graph Infomax (DGI), and an extension of the typical formulation of the VAE where the topological structure is accounted for via an explicit regularization of the loss (Graph-Regularized VAEs, introduced in this work). To extensively investigate these methodologies, we consider a wide variety of data types: synthetic data point clouds, MNIST, citation networks, and chemical reactions. We show that each of the representations learned by these models may have critical importance for further downstream tasks, and that accounting for the topological features can greatly improve the modeling capabilities for certain problems. We further provide a framework to analyze these, and future models under different scenarios and types of data.
DEMYSTIFYING ONLINE INSTRUCTION IN LIBRARIES: PEOPLE, PROCESS, AND TOOLS.

"DEMYSTIFYING ONLINE INSTRUCTION IN LIBRARIES: PEOPLE, PROCESS, AND TOOLS.." Public Services Quarterly, 17(1), pp. 38–39
일제강점기 가톨릭 교회의 지면을 통한 의료 계몽 활동: 『경향잡지』와 『가톨릭청년』을 중심으로

This research note examines the development and meaning of the Catholic Church’s medical enlightenment activities in the Japanese colonial period. Before and after the turn of the 20th century, enlightenment activities to spread new information became active in Korea, resulting from the country’s incorporation into the world order. Not only the political system, but also everyday life was no longer the same, and media such as magazines and newspapers became popular means of informing on and knowing the new world. The Catholic Church was one actor that took the lead in spreading new cultures to colonial Korea by publishing magazines and newspapers such as the Kyeong Hyang Shinmun, Kyeong Hyang Magazine, and Catholic Youth.The circulation of medical information and paper consultations were at the center of the Catholic Church’s activities. It provided medical information, such as the concept and treatment of diseases, while also conducting medical consultations that “listened” to individuals’ symptoms and gave prescriptions accordingly. This series of activities was largely divided into four stages. First, folk remedies were considered part of the information necessary for everyday life through the “Various Problems” section of Kyeong Hyang Magazine. Next, the basics of medicine were covered as part of the knowledge on Western natural sciences in the magazine’s “Science” section. Then, Kyeong Hyang Magazine published the “Sanitation” section, which exclusively addressed medicine, in a series, and finally, Catholic Youth conveyed the basics of Western medicine while conducting paper consultations.The Catholic Church’s medical enlightenment was the result of the overlapping interests of the Catholic Church, the general Korean public, and the Japanese Government-General, colonial Korea’s three main agents of producing, consuming, and managing new information. First, the Church’s medical enlightenment distributed the knowledge that the general public “wanted to know.” In colonial Korea, medical personnel were scarce, so magazines and newspapers were useful windows to obtaining medical information and consulting on the physical condition of the body. The Church also presented the knowledge that it itself “wanted to inform”. Small dispensaries could not meet the medical needs of many, so mass media was a promising alternative. Lastly, the Catholic Church delivered knowledge “permitted” by the Japanese Government-General, as the dissemination of medical information did not threaten the legitimacy of colonial rule and helped develop colonial Korea. From the position of the Governor-General, the Catholic Church’s medical enlightenment activities were thus recommendable.
English Textbook’s Readability and Junior Secondary School Students’ Performance in Reading Comprehension.

This study investigated the English textbook readability and secondary school students’ performance in reading comprehension in Ondo state, Nigeria. The study adopted the descriptive survey research. The population consisted of all the public secondary schools in the State. The sample size was 680 JSS 3 students randomly selected from 24 public secondary schools, and Macmillan Brilliant English Student’s textbook (Book 3). The Fry Readability Graph was used to determine the readability level of the comprehension passages used while Cloze Tests on 3 reading comprehension passages were used for data collection. The data was collected from the book through the application of the Fry Formula ratings converted into readability/grade level and students’ performance in Cloze Tests. A major finding shows that the prescribed English textbook matches the students’ level, yet the majority of them read at a frustration level. Findings on the rural/urban performance in Cloze Tests show that students in urban schools outperformed their counterparts in the rural areas (urban schools mean value = 1.72 (SD=0.78) while rural schools mean value=1.41(SD=0.60).In other words, school location had a significant influence on students’ performance in Cloze Tests. Therefore, the study recommended among others, that teachers should be involved in the selection process of book prescription for learners. Also, all educators should make text readability a foremost criterion for book selection. Last but not the least, the Ministry of Education in Ondo State should partner with textbook writers and book publishers and make text readability cardinal point of their partnership.
A Flexible Framework for Discovering Novel Categories with Contrastive Learning

This paper studies the problem of novel category discovery on single- and multi-modal data with labels from different but relevant categories. We present a generic, end-to-end framework to jointly learn a reliable representation and assign clusters to unlabelled data. To avoid over-fitting the learnt embedding to labelled data, we take inspiration from self-supervised representation learning by noise-contrastive estimation and extend it to jointly handle labelled and unlabelled data. In particular, we proposed using category discrimination on labelled data and cross-modal discrimination on multi-modal data to augment instance discrimination used in conventional contrastive learning approaches. We further introduce Winner-Take-All (WTA) hashing algorithm on the shared representation space to generate pairwise pseudo labels for unlabelled data to better predict cluster assignments. We thoroughly evaluate our framework on large-scale multi-modal video benchmarks Kinetics-400 and VGG-Sound, and image benchmarks CIFAR10, CIFAR100 and ImageNet, obtaining state-of-the-art results.
Research on the Application of Blended Teaching in Japanese Audio-Visual Course

Like other language disciplines, Japanese is also a tool discipline for communication, which emphasizes the application of theoretical knowledge to language practice. With the rapid development of modern information technology, the organic integration of network teaching and traditional Japanese teaching makes it a hybrid teaching, which not only broke through the limitations of Japanese teaching, and the enthusiasm of the innovative teaching mode make students learning Japanese is increasingly high. It is helpful for teachers to improve teaching quality and overall level of Japanese teaching, which is also a Japanese education mode of modern information technology innovation and develop the new path of teaching. This paper discusses the concrete implementation method and teaching significance of the hybrid teaching in the process of Japanese teaching in order to make common progress.
Preserved central model for faster bidirectional compression in distributed settings

We develop a new approach to tackle communication constraints in a distributed learning problem with a central server. We propose and analyze a new algorithm that performs bidirectional compression and achieves the same convergence rate as algorithms using only uplink (from the local workers to the central server) compression. To obtain this improvement, we design MCM, an algorithm such that the downlink compression only impacts local models, while the global model is preserved. As a result, and contrary to previous works, the gradients on local servers are computed on perturbed models. Consequently, convergence proofs are more challenging and require a precise control of this perturbation. To ensure it, MCM additionally combines model compression with a memory mechanism. This analysis opens new doors, e.g. incorporating worker dependent randomized-models and partial participation.
Effect of Maternal Skills in Early Detection and Stimulation on Receptive Language Skill of Children Age 4 to 6 Years

ABSTRACT Background: Language is the system someone uses to communicate with another person. Receptive language is important to the understanding of a child’s overall language skills. The purpose of this study was to examine the effect of maternal skills in early detection and stimulation on receptive language skill of children age 4 to 6 years. Subjects and Method: A cross sectional study was conducted at kindergarten in Surakarta, Central Java, from December 2019 to January 2020. A sample of 200 preschool children was selected by fixed disease sampling. The dependent variable was receptive language. The independent variables were early detection and early stimulation. Receptive language was measured by receptive one word picture vocabulary test (ROWPVT). The data were analyzed by a multiple linier regression. Results: Receptive language skill increased with early detection (b= 4.86; 95% CI= 2.68 to 6.33; p&lt;0.001) and early stimulation (b= 3.70; 95% CI= 1.03 to 3.38; p&lt;0.001). Conclusion: Receptive language skill increased with early detection and early stimulation. Keywords: receptive language, early detection, early stimulation, health belief model Correspondence: Anggi Resina Putri. Masters Program in Public Health, Universitas Sebelas Maret. Jl. Ir. Sutami 36A, Surakarta 57126, Central Java. Email: anggiresinaputri@gmail.com. Mobile: 085727387689. DOI: https://doi.org/10.26911/the7thicph.03.100
The Family Education Effect and Language Attitude against Vocabulary Mastery

The purpose of this research was to determine the family education effect and language attitudeagainst vocabulary mastery. The method used is a survey method with a quantitative approach. There were 43 samples from 352 populations which obtained using cluster random sampling techniques. Data collected through questionnaires and tests. Hypothesis testing uses path analysis techniques. The results of data analysis obtained (1) path coefficient (p31) of 0.520 with a value of th 6.965> tt 2.020, (2) path coefficient (p32) of 0.385 with a value of th (2.184)> tt (2,020), (3) path coefficient ( p21) amounting to 0.867 with a value of th7,301)> tt (2,020). The results of the research concluded as (1) family education and language attitude had a direct positive effect against the polite vocabulary mastery, (2) family education had a direct positive effect against language attitude.
Stochastic Optimization with Non-stationary Noise: The Power of Moment Estimation

We investigate stochastic optimization under weaker assumptions on the distribution of noise than those used in usual analysis. Our assumptions are motivated by empirical observations in training neural networks. In particular, standard results on optimal convergence rates for stochastic optimization assume either there exists a uniform bound on the moments of the gradient noise, or that the noise decays as the algorithm progresses. These assumptions do not match the empirical behavior of optimization algorithms used in neural network training where the noise level in stochastic gradients could even increase with time. We address this nonstationary behavior of noise by analyzing convergence rates of stochastic gradient methods subject to changing second moment (or variance) of the stochastic oracle. When the noise variation is known, we show that it is always beneficial to adapt the step-size and exploit the noise variability. When the noise statistics are unknown, we obtain similar improvements by developing an online estimator of the noise level, thereby recovering close variants of RMSProp~\citep{tieleman2012lecture}. Consequently, our results reveal why adaptive step size methods can outperform SGD, while still enjoying theoretical guarantees.
Social Media Tools

In the previous chapter, you learned about podcasts and their effectiveness as an online teaching tool. Since they are on demand, learners can easily fit them into their busy schedules and listen at their convenience on their mobile devices. Plenty of podcasts are freely available on any subject. If you cannot find a podcast to meet your learning needs, you can easily and inexpensively create your own with a few simple tools.
Use of computer for formation and development of practical skills of students in learning process of “straightforward uniform movement and straightforward uneven movement” in physics for VII classes

The presented article indicates the methods of teaching the lesson on the example of the topic “ Uniform Motion and Non-uniform Motion” for VII classes “Physics” both in full-time and in remote (synchronized) form, as well as the movement of the DCM object (dynamic computer model). The result was obtained by conducting studies and calculations on the model using Excel. According to the results of the course, it was revealed that the student comments on the mechanical motion along the sings, using computer programs and summarizing, knows how to summarize his observations. On the other hand, the article states that computer models differ in time by static and dynamic factors. And dynamic computer models differ in “controlled” and “unmanaged” computer models. Key words: computer, model, animation, practical skills, event, speed, time
LibGuides: New to CCSU? Start Here!: Circulation, Course Reserves (Textbooks) and Interlibrary Loan

CCSU New Student Library Guide to resources, links and services What to know about the Circulation Desk, Course Reserves and Interlibrary Loan departments!
Screenplay collaborator granted authorship credit in IPEC ‘encore’

Martin v Kogan [2021] EWHC 24 (Ch), 11 January 2021 (Meade J) Following a retrial, the Intellectual Property Enterprise Court (IPEC) has held a screenplay collaborator to be entitled to a 20% joint authorship share. The IPEC had upheld Nicholas Martin’s claim to be the sole author of the screenplay for the film Florence Foster Jenkins in an initial decision in Martin v Kogan [2017] EWHC 2927 (IPEC), [2018] FSR 9 (Hacon HHJ). On appeal, a retrial was ordered with the trial judge criticized for failing to make crucial findings of fact and for errors of law in the application of copyright law’s joint authorship test: Kogan v Martin [2019] EWCA Civ 1645, [2020] FSR 3 (Floyd, Henderson, and Peter Jackson LLJ). A newly constituted IPEC has now found the screenplay to be a work of joint authorship with the contribution of Martin’s collaborator, Julia Kogan, assessed at 20...