Reviewerly/exHarmony huggingface dataset:
Amplifying Rényi Differential Privacy via Shuffling

Differential privacy is a useful tool to build machine learning models which do not release too much information about the training data. We study the Rényi differential privacy of stochastic gradient descent when each training example is sampled without replacement (also known as cyclic SGD). Cyclic SGD is typically faster than traditional SGD and is the algorithm of choice in large-scale implementations. We recover privacy guarantees for cyclic SGD which are competitive with those known for sampling with replacement. Our proof techniques make no assumptions on the model or on the data and are hence widely applicable.
Homogeneous Group Performance and Stability in Checkers

Experiments on groups of Checkers programs, playing by majority voting, were performed to investigate performance and stability. Homogeneous groups, copies of the same program, was used to perform these experiments instead of heterogeneous group that was more complicated by factors of different programs. Experiments were performed based on a search-depth of 5, 10 and 12 using the Samuel checkers program. Games of checkers were played between groups of size ranging from 1 up to 10 for each side. Experiment results suggest that group performance increases as a kind of logarithm function as the group size gradually increases for stronger player, and the performance slowly decreases in the case of a weaker player, stability seems to increase as the group size increases.
Plotting Our Future: The UC Visualization Laboratory & Services Model

Poster presented at the 2019 Special Libraries Association (SLA) annual conference.Abstract: In 2018, the University of Cincinnati Libraries’ Research & Data Services (RDS) unit unveiled a new ...
INTELLECTUAL DEVELOPMENT OF THE PERSONALITY IN THE CONTEXT OF THE PROGRAMME «PHILOSOPHY FOR CHILDREN»

The article deals with M. Limpan’s programme «Philosophy for children» and its impact on the intellectual development of the personality. The details of the programme’s implementation and its development have been characterised. It has been stated that the programme of «Philosophy for Children» is aimed to teach children philosophizing, that is a way of thinking about the world and orientation in it. It has been defined that the programme focuses on the formation of a particular personality able to make responsible decisions. Two basic conditions have been outlined in theoretical, pedagogical and philosophical conception of Lipman’s theory. The formation of children’s certain practical skills and abilities as the result of using the programme has been determined by the authors.
THE INFLUENCE OF THE INTENSITY OF INTERNET MEDIA USAGE AND LEARNING MOTIVATION ON INCREASING LEARNING OUTCOMES OF BOGA DASAR SUBJECTS FOR GRADE X STUDENTS AT SMK NEGERI 57 JAKARTA

Purpose of this study is to examine the intensity of internet media usage and learning motivation towards improving student learning outcomes and strengthen theories related to learning motivation, intensity of internet media usage to improve student learning outcomes. This study was conducted at SMK 57 Jakarta using a quantitative approach with survey method. The target population is students of 57 Jakarta Vocational School majoring in Catering, while the affordable population is students of class X (ten) SMK Negeri 57 Jakarta 2017-2018 academic year totaling 139 people and 103 students as research samples using Slovin formula. Data analysis techniques used in this research is with descriptive and inferential statistics which are Regression Linearity Tests. Based on the results of the analysis above it can be concluded that there is no significant influence between the intensity of Internet Media Usage on Basic Food Subject Learning Outcomes. Based on the results of the above analysis it can be concluded that there is no significant effect between Student learning Motivation on Basic Food Subject Learning Outcomes. Based on the results of the analysis above it can be concluded that there is no significant effect between the intensity of Internet Media Usage and Learning Motivation simultaneously on Basic Food Subject Learning Outcomes.
Variation in Unconscious Lexical Processing: Education and Experience Make a Difference

Over the past twenty years numerous studies have investigated the extent to which morphological constituents of words are activated during the process of word recognition. In the vast majority of these studies it has been assumed that a correspondence exists between the formal linguistic analysis of a word and its representation in the minds of native speakers. This paper investigates the the extent to which this correspondence can be affected by individual variation that is associated with education, exposure and training. We investigated students who had recently completed a course in medical terminology. These students, and matched control subjects, responded to medical and nonmedical multimorphemic stimuli in a lexical decision task. The results indicate that the medical terminology students’ training affected their performance on novel medical words as well as their performance on very common medical words (e.g., psychiatry) that would have been part of their vocabulary prior to taking the course. The results therefore support the view that automatic unconscious lexical processing can indeed be modified by explicit training and specialized exposure. This finding has consequences for the generalizability of studies conducted on university students to the general population of native speakers.
READ: a three-communicating-stage distributed super points detections algorithm

A super point is a host that interacts with a far larger number of counterparts in the network over a period of time. Super point detection plays an important role in network research and application. With the increase of network scale, distributed super point detection has become a hot research topic. Compared with single-node super point detection algorithm, the difficulty of super point detection in multi-node distributed environment is how to reduce communication overhead. Therefore, this paper proposes a three-stage communication distributed super point detection algorithm: Rough Estimator based Asynchronous Distributed super point detection algorithm (READ). READ uses a lightweight estimator, the Rough Estimator (RE), which is fast in computation and takes less memory to generate candidate super point. At the same time, the Linear Estimator (LE) is used to accurately estimate the cardinality of each candidate super point, so as to detect the super point correctly. In READ, each node scans IP address pairs asynchronously. When reaching the time window boundary, READ starts three-stage communication to detect the super point. In this paper, we proof that the accuracy of READ in distributed environment is no less than that in the single node environment. Four groups of 10 Gb/s and 40 Gb/s real-world high-speed network traffic are used to test READ. The experimental results show that READ not only has higher accuracy in distributed environment, but also has less than 5% of communication burden compared with existing algorithms.
Detection of Mo geochemical anomaly in depth using a new scenario based on spectrum–area fractal analysis

Detection of deep and hidden mineralization using the surface geochemical data is a challenging subject in the mineral exploration. In this work, a novel scenario based on the spectrum–area fractal analysis (SAFA) and the principal component analysis (PCA) has been applied to distinguish and delineate the blind and deep Mo anomaly in the Dalli Cu–Au porphyry mineralization area. The Dalli mineral deposit is located on the volcanic–plutonic belt of Sahand–Bazman in the central part of Iran. The geochemical data was transformed to the frequency domain using the Fourier transformation, and SAFA was applied for classification of geochemical frequencies and detection of geochemical populations. The very low-frequency signals in the fractal method were separated using the low-pass filter function and were interpreted using PCA. This scenario demonstrates that the Mo element has an important role in the mineralization phase in the very low-frequency signals that are related to the deep mineralization; it is an important innovation in this work. Then the Mo geochemical anomaly has been mapped using the inverse Fourier transformation. This research work shows that the high-power spectrum values in SAFA are related to the background elements and the deep mineralization. Two exploratory boreholes drilled inside and outside the deep Mo anomaly area properly confirm the results of the proposed approach.
음절 연쇄에서 나타나는 일본인 학습자의 한국어 종성 발음 유형

This study aims to analyze the pronouncing patterns of Japanese learner in Korean syllable final consonant in terms of place and manner of articulation. For the analysis, we have assembled and researched actual data of the elementary-level learner’s pronunciation. As a result of the research, we claim the three types of pronouncing error which are concerned with the place assimilation, open syllabification and the changing of articulation based on the place and manner.In Japanese, there are phonemes so-called ‘sokuon(促音, つ)’ and ‘hatsuon(撥 音, ん)’ also depicted as /Q/ and /N/ that can be recognized as the coda. These elements influence the learner’s pronunciation of Korean syllable final consonants that is added up to typical error causes ill-formed place assimilation and the open syllabification.The Japanese learner’s error, however, is not only concerned with the place of underlying /Q/ and /N/. They also change the sonority of obstruent to avoid the unmarked segmental sequence. Moreover, some errors are caused by the asymmetry of phonetic feature between Japanese and Korean syllable final nasal.
Lost in Migration: Digitalizing Diaspora and Decolonizing Syrian Refugee Narratives

Author(s): Yen, Leia | Advisor(s): Snelson, Daniel | Abstract: Although there are traditional works in print about the Syrian refugee crisis, an unprecedented amount of digitalized narratives demonstrates a shift in the body of diaspora literature towards using internet technologies to convey refugee stories. Mobile devices in the hands of refugees have made the crisis one of the most self-documented in history and yet the most prevalent and influential digital representations of Syria are those created by third parties. How do digital literary forms effect diaspora narratives, especially when the texts are created by distant mediators? I argue that digital narratives of the Syrian refugee crisis create two layers of liminality. The first layer is the migration narrative itself; the second layer, though, is a new type of liminality created by digital spaces in which narratives hybridize as they encounter alternative values, beliefs, and social constructs embedded within the structures of digital texts. My thesis takes the form of a website that deconstructs the design elements of the digital texts “Searching for Syria,” migrant-related selfies and memes, and “Heln’s First Year.” I use these case studies as examples of how synthesizing post-colonial theory with postmodern deconstructivism can work to decolonize digital texts as well as identify the ways in which hypermediacy can be used for ethical design.
MEMBACA: SEBAGAI MEDITASI PIKIRAN DAN BAHASA

Lingua Scientia Language Journal (JBLS) is an OJS-based language journal (Open Journal System) containing of high quality articles published by Center for Language Development of IAIN Tulungagung in June and November each year.
Makerspaces: A Practical Guide for Librarians

"Makerspaces: A Practical Guide for Librarians." Journal of Electronic Resources in Medical Libraries, 16(2), pp. 94–95
일본 중학교 독도교육의 실태 -교과서 기술내용의 변화를 중심으로-

The purpose of this paper is to examine the actual situation of Dokdo - related education in junior high school after the announcement of the revision bill of 'Commentary on Middle and Higher Education Guidance' in 2014. In this thesis, we analyzed the contents of technical texts of geography, citizens, and history textbook which are actually used in educational field. According to the on Study Guideline revised in 2014, according to the textbook certification standards that the views of the Japanese government must be reflected in the textbooks, Dokdo was designated as the original territory of Japan and Korea is illegally occupied . The textbooks described in accordance with these guidelines and the instruction manuals of the school guidebooks follow the Japanese government's right-wing territorial policies and almost twice as much as the existing contents of the textbooks. have. However, these textbooks of Japanese middle school textbooks showed a tendency to respond to the right - wing policy preemptively before the request of the Japanese government. Therefore, the revised standards in 2014 are post-testicular textbooks that passed the screening test in 2011, and textbooks that passed the screening test in 2015 are more accurate than those of the Japanese government. . As a result, under the leadership of the Japanese government, the textbooks that passed the test after 2015 in accordance with the revised Guidance on Teaching Guidance Revised in 2014 became a massive publicity material that criticized the Japanese government's views on the Dokdo issue. I have done it.
Joint Language Identification of Code-Switching Speech using Attention based E2E Network

Language identification (LID) has relevance in many speech processing applications. For the automatic recognition of code-switching speech, the conventional approaches often employ an LID system for detecting the languages present within an utterance. In the existing works, the LID on code-switching speech involves modelling of the underlying languages separately. In this work, we propose a joint modelling based LID system for code-switching speech. To achieve the same, an attention-based end-to-end (E2E) network has been explored. For the development and evaluation of the proposed approach, a recently created Hindi-English code-switching corpus has been used. For the contrast purpose, an LID system employing the connectionist temporal classification-based E2E network is also developed. On comparing both the LID systems, the attention based approach is noted to result in better LID accuracy. The effective location of code-switching boundaries within the utterance by the proposed approach has been demonstrated by plotting the attention weights of E2E network.
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace-Beltrami equation on a high dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. For reproducibility, the code is available at \url{https://github.com/BaoWangMath/DNN-DataDependentActivation}.
Linguistic Freedom and Hate Speech: A Critical Review of Nigerian Politicians’ Speeches and Their Implications

This paper deals with the linguistic freedom of the people of Nigeria as entrenched in the constitution of the Federal Republic of Nigeria and the misuse of that freedom through hate-speech by Nigerian politicians against their opponents in the public for and even in the National Dailies. This paper condemns such actions in its entirety exposing the dangers involved and the implications both at present and in future. The paper goes further to give a better alternative to hate-speech, which is persuasion; this is believed to be more convincing than the former. The theories of persuasion were explained and elaborated. These theories are social judgment theory and the Relationship based persuasion theory. It concludes by saying that whatever the situation, it is when Nigerians see themselves as one, respect one another and work together, that Nigeria can stand tall in the comity of nations of the world. Based on the havoc and the hatred that hate-speech causes, the paper recommends that government should do everything possible eradicating this societal cankerworm or reducing it to the barest minimum, by taking drastic actions against perpetrators, because,  to build Nigeria and make it one continually, is a task that must be done by all. Keywords : Linguistic freedom, hate-speech, politicians, persuasion, societal cankerworm. DOI : 10.7176/NMMC/80-01 Publication date :May 31 st 2019
The one-dimensional material model of the continental crust

Summary The research is about constructing a new algorithm for spatial modeling of the solid composition of rocks, which is consistent with the results of geophysical, petrophysical and geochemical studies. The basic geophysical methods used to solve a complex inverse problem are deep seismic sounding and gravimetry. The determination of probability distributions is carried out by statistical processing of the results of geological and geochemical studies for the material composition and the introduction of confidence intervals for the results of geophysical research. This gives the main advantage over classical methods of inversion with optimization - to avoid the possibility of obtaining geologically meaningless solutions.
IMPLEMENTASI DATA MINING ROUGH SET DALAM MENENTUKAN TINGKAT KERUSAKAN ALAT DAN BAHAN KIMIA (STUDI KASUS DI LABORATORIUM RESEP SMK S-16 FARMASI BENGKULU)

Data mining is one of the methods used in extracting the data in generating new knowledge fromstored data. The extent of damage to the equipment and chemicals that affect the cost of school often donot know how much it cost the school. With data mining that generates knowledge in the form of somerules and help the school to predict the extent of damage and the cost of chemicals that can be in.
How Does API Migration Impact Software Quality and Comprehension? An Empirical Study

The migration process between different third-party software libraries is hard, complex and error-prone. Typically, during a library migration process, developers opt to replace methods from the retired library with other methods from a new library without altering the software behavior. However, the extent to which such a migration process to new libraries will be rewarded with an improved software quality is still unknown. In this paper, we aim at studying and analyzing the impact of library API migration on software quality. We conduct a large-scale empirical study on 9 popular API migrations, collected from a corpus of 57,447 open-source Java projects. We compute the values of commonly-used software quality metrics before and after a migration occurs. The statistical analysis of the obtained results provides evidence that library migrations are likely to improve different software quality attributes including significantly reduced coupling, increased cohesion, and improved code readability. Furthermore, we release an online portal that helps software developers to understand the pre-impact of a library migration on software quality and recommend migration examples that adopt the best design and implementation practices to improve software quality. Finally, we provide the software engineering community with a large scale dataset to foster research in software library migration.
Truck Traffic Monitoring with Satellite Images

The road freight sector is responsible for a large and growing share of greenhouse gas emissions, but reliable data on the amount of freight that is moved on roads in many parts of the world are scarce. Many low- and middle-income countries have limited ground-based traffic monitoring and freight surveying activities. In this proof of concept, we show that we can use an object detection network to count trucks in satellite images and predict average annual daily truck traffic from those counts. We describe a complete model, test the uncertainty of the estimation, and discuss the transfer to developing countries.
Formation of methodological culture of undergraduates of technological fields of education

С. И. Осипова, Т
Concurrency in evolutionary algorithms

tutorial Share on Concurrency in evolutionary algorithms Author: J J Merelo University of Granada, Granada (Spain) University of Granada, Granada (Spain)View Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 1172–1174https://doi.org/10.1145/3319619.3323395Published:13 July 2019Publication History 0citation47DownloadsMetricsTotal Citations0Total Downloads47Last 12 Months3Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Attentive CT Lesion Detection Using Deep Pyramid Inference with Multi-Scale Booster

Accurate lesion detection in computer tomography (CT) slices benefits pathologic organ analysis in the medical diagnosis process. More recently, it has been tackled as an object detection problem using the Convolutional Neural Networks (CNNs). Despite the achievements from off-the-shelf CNN models, the current detection accuracy is limited by the inability of CNNs on lesions at vastly different scales. In this paper, we propose a Multi-Scale Booster (MSB) with channel and spatial attention integrated into the backbone Feature Pyramid Network (FPN). In each pyramid level, the proposed MSB captures fine-grained scale variations by using Hierarchically Dilated Convolutions (HDC). Meanwhile, the proposed channel and spatial attention modules increase the network's capability of selecting relevant features response for lesion detection. Extensive experiments on the DeepLesion benchmark dataset demonstrate that the proposed method performs superiorly against state-of-the-art approaches.
Solving Hard Coreference Problems

Coreference resolution is a key problem in natural language understanding that still escapes reliable solutions. One fundamental difficulty has been that of resolving instances involving pronouns since they often require deep language understanding and use of background knowledge. In this paper, we propose an algorithmic solution that involves a new representation for the knowledge required to address hard coreference problems, along with a constrained optimization framework that uses this knowledge in coreference decision making. Our representation, Predicate Schemas, is instantiated with knowledge acquired in an unsupervised way, and is compiled automatically into constraints that impact the coreference decision. We present a general coreference resolution system that significantly improves state-of-the-art performance on hard, Winograd-style, pronoun resolution cases, while still performing at the state-of-the-art level on standard coreference resolution datasets.