Reviewerly/exHarmony huggingface dataset:
THE MORPHO-PHONEMIC PROCESSES IN INDONESIAN ADVERTISEMENT’S SLOGANS

The use of slogans in the advertisements to introduce and to attract public’s attention upon the products advertised are essential. The use of morpho-phonemic forms as the morphological process in the Indonesian advertisements’ slogans nowadays are interesting to be analyzed since there are lots of them use unique forms to attract public’s attention upon the product. The public’s attention upon the advertisements would influence the level of public’s awareness upon the product and eventually would raise the number of purchase and the usage of the product offered. This research explained the forms of the morphological process used in the Indonesian advertisement’s slogans, and the purposes of the morphological process used in the Indonesian advertisement’s slogans. Based on O'Grady and Dobrovolsky’s theories, this research uses descriptive qualitative method by observing and documenting the forms of morpho-phonemic changing which are used in Indonesian slogans, continued by analyzing the data. The objects of the study were the texts in advertisement’s slogans. The sources of the data were the Indonesian advertisement’s slogans from various types of product, which were aired or published in Indonesia. The forms of the morpho-phonemic changing were studied based on morphological processes in morphology. Slogans should attract the attention from public upon the product advertised to consume or to use it. It is hope that these findings will be useful for the development of language use especially in advertising.
The Consequences of the Identity Theft Fear in the Sport Products Online Shopping From the Perspective of Physical Education Students

The Consequences of the Identity Theft Fear in the Sport Products Online Shopping From the Perspective of Physical Education Students
Исследование методов подбора вакансий на основе ассоциативного анализа данных

Algorithms for the recommendatory selection of vacancies in accordance with the user's existing skills based on associative analysis satisfy a strictly defined value of supporting informative information features.Unlike the most common search engines today, the recommendation system does not imply a clear request to get the required answer.At the same time, recommendation systems in modern realities are becoming very popular, which is due to a rather noticeable decrease in the time to find the required information.The article discusses algorithms for recommendatory selection of vacancies according to available skills based on associative analysis.
The deep structure of mineragenetic zones in Southern Primorye

The deep structure of mineragenetic zones under the Spassk-Dalniy-Zerkalnaya Bay geotraverse has been derived from the integrated interpretation of seismic, density, magnetic, and geoelectric data. The studies demonstrate that geophysical methods based on different physical principles reflect various characteristics of lithosphere elements. Large sets of the obtained geophysical data provide crucial information on structural features and physical characteristics of mineragenetic objects to understand the genesis of endogenous mineralization of various types and develop new approaches to forecasting.
Information Assurance: Education at Doctoral Level—An International Look

Information is the driving force for the society and development of different kind.Due to the importance of information different organizations, institutions are giving priorities for information affairs.The field of Information is called Information Science.There are few various close disciplines of information viz.Information Studies, Information Management, Information Studies.The emergence and need of Information lead to different subfields and categories of Information Science.Information Privacy and Security is an important concept these days and as a result, this has also become a field of study.Information Assurance is an advanced interdisciplinary field responsible for secure information security and privacy management.Information Assurance in short known as IA.Information Assurance is the extension of Information Security.The field Information Security deals with both computational and manual information security related management.Though, Information Assurance is additionally responsible for dealing legal, managerial and social issues related to Information Privacy and Security.Due to the application and requirement of the Information Assurance, many universities around of the world have started the academic program on Information Assurance not only at Bachelors level but also at Masters and Doctoral level.Universities those who are offering Information Assurance Doctoral program comes with both only research approach and both research-skill (coursework) approach.This paper is a theoretical one but focused on case study related to the PhD and other doctoral degrees in Information Assurance and allied program in International Universities.
A program verification based approach to find data race vulnerabilities in interrupt-driven program

The data race problem is common in the interrupt-driven program, and it is difficult to find as a result of complicated interrupt interleaving. Static analysis is a mainstream technology to detect those problems, however, the synchronization mechanism of interrupt is hard to be processed by the existing method, which brings many false alarms. Eliminating false alarms in static analysis is the main challenge for precisely data race detection.
A Generic Conversion from Proxy Signatures to Certificate-based Signatures

The proxy signature ( PS ) and the certificate-based signature ( CBS ) are both popular cryptographic protocol. The former is a special signature which allows an entity to delegate his signing rights to another, while the later is another attractive cryptography primitive whose original motivation is to simplify certificate’s management and to eliminate key escrow problem. However, there is a drawback in the existing security model of PS, and there are something in common between the CBS and the PS. In the paper, we first analyze the drawback of the existing security model of PS. Secondly, we propose an improved security model for PS which is stronger than the existing one to overcome its drawback, new model allows an adversary of PS to issue both queries for different proxy signers but the same original signer. Thirdly, we proposed a new paradigm PS-2-CBS which is a generic conversion from an existing secure PS to a secure CBS after analyzed the relationship between the CBSs and the PSs. and prove that our PS-2-CBS is secure if the underlying PS is secure under improved security model of PS. Finally, an example of PS-2-CBS is gave.
ON THE CONCEPT OF CONTINUITY OF PROFESSIONAL PEDAGOGICAL AND GENERAL EDUCATION

Elaborating on the previously conducted studies into polar model of sense-making, the authors propose a trialectic model of the way meanings are born.The purpose of this article is to provide theoretical grounds for transition from the dual to trinal model of sense-making strategies and their description.The sense-making strategy determines the direction and quality content of the generated image-meaning.The authors find it expedient to consider trialectic level of the system of senses for more adequate modeling of possible strategies that reflect the key traits of a personality.The above mentioned triad contains hypo-adaptive, hyper-adaptive and pre-adaptive sense-making strategies.The proposed approach enables to identify a personal profile that acts as a multifactor model of possible states of value-notional sphere of an experiencing subject.In terms of modeling behavior patterns, a personal profile of such kind reveals the main direction and principal features of sense-making regulation.
ADMM-based Adaptive Sampling Strategy for Nonholonomic Mobile Robotic Sensor Networks

This paper discusses the adaptive sampling problem in a nonholonomic mobile robotic sensor network for efficiently monitoring a spatial field. It is proposed to employ Gaussian process to model a spatial phenomenon and predict it at unmeasured positions, which enables the sampling optimization problem to be formulated by the use of the log determinant of a predicted covariance matrix at next sampling locations. The control, movement and nonholonomic dynamics constraints of the mobile sensors are also considered in the adaptive sampling optimization problem. In order to tackle the nonlinearity and nonconvexity of the objective function in the optimization problem we first exploit the linearized alternating direction method of multipliers (L-ADMM) method that can effectively simplify the objective function, though it is computationally expensive since a nonconvex problem needs to be solved exactly in each iteration. We then propose a novel approach called the successive convexified ADMM (SC-ADMM) that sequentially convexify the nonlinear dynamic constraints so that the original optimization problem can be split into convex subproblems. It is noted that both the L-ADMM algorithm and our SC-ADMM approach can solve the sampling optimization problem in either a centralized or a distributed manner. We validated the proposed approaches in 1000 experiments in a synthetic environment with a real-world dataset, where the obtained results suggest that both the L-ADMM and SC- ADMM techniques can provide good accuracy for the monitoring purpose. However, our proposed SC-ADMM approach computationally outperforms the L-ADMM counterpart, demonstrating its better practicality.
A Review of Structure Optimization of Convolutional Neural Networks

In recent years, Convolutional neural network (CNNs) has gained a significant development in the industrial manufacturing process. However, its application in the environment with high real-time requirements and limited resources is restricted by its huge scale and complicated computing degree. The optimization of the structure of convolutional neural networks has become a research hotspot in the field of deep learning. In this paper, development history, research status and typical methods of structural optimization technology of convolutional neural networks are summarized, which are summarized into five aspects of pruning & sparsification, tensor factorization, knowledge transferring, compacting module designing and automatic design. And a more comprehensive discussion is carried out. Finally, the developments and difficult points of current research in this paper will be analyzed and summarized, and the future development direction and application prospect of network structure optimization field also will be forecast.
A Quantitative and Theoretical Investigation of Racism in Japan

This chapter focuses on hate speech in Japan from the perspective of social psychology and adjacent fields. First, the author discusses the definitions of and the debate around the social psychological concepts of stereotypes, prejudice, and discrimination, and how these concepts are interrelated. The importance of constructing social norms against hate speech and suppressing opportunities to deliver hate speech are discussed. Second, he discusses a series of applied research conducted mainly in Japan. Quantitative analyses of posts on social media reveal the characteristics of hate speech in Japan, as well as underling beliefs and attitudes. Specifically, themes such as ‘modern or symbolic racism’, ‘old-fashioned racism’, ‘historical revisionism’, and so on, were evident in these posts. Furthermore, these and other studies show that relatively few people are disproportionately able to bias the discourse on politically controversial topics. Questionnaire surveys show a consistent association between Internet usage and negative attitudes towards people from outside Japan. Together, these quantitative studies show how detrimental the information age can be in the absence of sufficient regulations.
Overcoming Stress: A Phenomenological Study of Field Missionaries Rescuing North Korean Defectors

In this study, the stress and experience of overcoming stress by on-site missionaries who rescue North Korean defectors was investigated in depth through a phenomenological approach. To this end, 7 missionaries with more than 5 years of experience in North Korean missionary mission were selected, and in-depth interviews and written interviews were conducted once. The collected data were analyzed through the phenomenological research methods of Giorgi and Colaizzi. The results were categorized into 'calling and obedience', 'mission and stress', 'anxiety situation and stress', 'stress situation and frustration', 'stress experience and psychological change', 'self insight and overcoming stress', 'resolution and recovery of stress', and ‘new determination and rededication'. By researching and analyzing the stress and stress experience of the missionaries who rescue North Korean defectors in the field, this study has significance for helping missionaries continue to help them through managing and overcoming their stress.
Innovative Acquisition Technique Based on Unmanned Aerial Vehicle Platform (UAV)

Summary Solving complex geological problems requires utilization of expensive and time consuming data acquisition technologies like 3D OBN surveys. Presented method uses Unmanned Aerial Vehicles (UAV) to deploy and retrieve autonomous floating recording devices. Using UAVs increase the deployment/retrieval speed compared to deep water OBN survey with Remote Operated Vehicle (ROV), shorten the duration of the survey and, accordingly, its cost. Embedded station-keeping system gives a precise and highly repeatable ground position of the recording units, providing the required acquisition geometry that allows their use for both exploration and reservoir monitoring surveys. Proposed technology presents an innovative way of long-offset full-azimuth seismic data acquisition considerably reducing operational costs, survey duration and environmental impact.
Privacy-preserving Channel Estimation in Cell-free Hybrid Massive MIMO Systems

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and $N_r< N_a$ radio frequency (RF) chains. When $K\ll M{N_a}$, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.
A tool for user friendly, cloud based, whole slide image segmentation

Convolutional neural networks, the state of the art for image segmentation, have been successfully applied to histology images by many computational researchers. However, the translatability of this technology to clinicians and biological researchers is limited due to the complex and undeveloped user interface of the code, as well as the extensive computer setup required. As an extension of our previous work (arXiv:1812.07509), we have developed a tool for segmentation of whole slide images (WSIs) with an easy to use graphical user interface. Our tool runs a state-of-the-art convolutional neural network for segmentation of WSIs in the cloud. Our plugin is built on the open source tool HistomicsTK by Kitware Inc. (Clifton Park, NY), which provides remote data management and viewing abilities for WSI datasets. The ability to access this tool over the internet will facilitate widespread use by computational non-experts. Users can easily upload slides to a server where our plugin is installed and perform human in the loop segmentation analysis remotely. This tool is open source, and has the ability to be adapted to segment of any pathological structure. For a proof of concept, we have trained it to segment glomeruli from renal tissue images, achieving an F-score &gt; 0.97 on holdout tissue slides.
PENGARUH MINAT MEMBACA DAN PENGUASAAN KOSAKATA TERHADAP KETERAMPILAN MENULIS NARASI SISWA KELAS V SDN SEKECAMATAN ALIAN TAHUN AJARAN 2019/2020

&lt;em&gt;Reading interest and vocabulary mastery affects the language style in writing narrative texts.&lt;/em&gt;&lt;em&gt; The study aimed to prove positive correlation of: reading interest on narrative writing skills, vocabulary mastery on narrative writing skills, reading interest and vocabulary mastery on narrative writing skills for fifth grade students of public elementary schools in Alian sub-district in academic year of 2019/2020. The research used quantitative research design with correlation method. Sampling method was cluster random sampling technique. SPSS version 18 was applied in analysing correlation data. The result indicated that the correlation coefficient between reading interest and vocabulary mastery on narrative writing skills was 0.169 with Sig. F Change = 0,000 &amp;lt;0.05 and the value of adjusted R-squared variables was 5.1%. It concludes that there are positive and significant effects between reading interest and vocabulary mastery on narrative writing skills to fifth grade students of public elementary schools in Alian sub-district.&lt;/em&gt;
LEARNABLE EMBEDDING SIZES FOR RECOMMENDER SYSTEMS

The embedding-based representation learning is commonly used in deep learning recommendation models to map the raw sparse features to dense vectors. The traditional embedding manner that assigns a uniform size to all features has two issues. First, the numerous features inevitably lead to a gigantic embedding table that causes a high memory usage cost. Second, it is likely to cause the over-fitting problem for those features that do not require too large representation capacity. Existing works that try to address the problem always cause a significant drop in recommendation performance or suffers from the limitation of unaffordable training time cost. In this paper, we proposed a novel approach, named PEP (short for Plug-in Embedding Pruning), to reduce the size of the embedding table while obviating a drop in accuracy and computational optimization. PEP prunes embedding parameter where the pruning threshold(s) can be adaptively learned from data. Therefore we can automatically obtain a mixed-dimension embedding-scheme by pruning redundant parameters for each feature. PEP is a general framework that can plug in various base recommendation models. Extensive experiments demonstrate it can efficiently cut down embedding parameters and boost the base model's performance. Specifically, it achieves strong recommendation performance while reducing 97-99% parameters. As for the computation cost, PEP only brings an additional 20-30% time cost compare with base models.
Special Issue: 2020 Research Data Access and Preservation Summit

The Journal of eScience Librarianship has partnered with the Research Data Access &amp; Preservation (RDAP) Association for a third year to publish selected conference proceedings. This issue highlights the research presented at the RDAP 2020 Summit and the community it has fostered.
Exploration of Visual Features and their weighted-additive fusion for Video Captioning

Video captioning is a popular task that challenges models to describe events in videos using natural language. In this work, we investigate the ability of various visual feature representations derived from state-of-the-art convolutional neural networks to capture high-level semantic context. We introduce the Weighted Additive Fusion Transformer with Memory Augmented Encoders (WAFTM), a captioning model that incorporates memory in a transformer encoder and uses a novel method, to fuse features, that ensures due importance is given to more significant representations. We illustrate a gain in performance realized by applying Word-Piece Tokenization and a popular REINFORCE algorithm. Finally, we benchmark our model on two datasets and obtain a CIDEr of 92.4 on MSVD and a METEOR of 0.091 on the ActivityNet Captions Dataset.
FedNS: Improving Federated Learning for collaborative image classification on mobile clients

Federated Learning (FL) is a paradigm that aims to support loosely connected clients in learning a global model collaboratively with the help of a centralized server. The most popular FL algorithm is Federated Averaging (FedAvg), which is based on taking weighted average of the client models, with the weights determined largely based on dataset sizes at the clients. In this paper, we propose a new approach, termed Federated Node Selection (FedNS), for the server's global model aggregation in the FL setting. FedNS filters and re-weights the clients' models at the node/kernel level, hence leading to a potentially better global model by fusing the best components of the clients. Using collaborative image classification as an example, we show with experiments from multiple datasets and networks that FedNS can consistently achieve improved performance over FedAvg.
Deductive and abductive argumentation based on information graphs

In this paper, we propose an argumentation formalism that allows for both deductive and abductive argumentation, where ‘deduction’ is used as an umbrella term for both defeasible and strict ‘forward’ inference. Our formalism is based on an extended version of our previously proposed information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. In the current version, we consider additional types of information such as abstractions which allow domain experts to be more expressive in stating their knowledge, where we identify and impose constraints on the types of inferences that may be performed with the different types of information. A new notion of attack is defined that captures a crucial aspect of abductive reasoning, namely that of competition between abductively inferred alternative explanations. Our argumentation formalism generates an abstract argumentation framework and thus allows arguments to be formally evaluated. We prove that instantiations of our argumentation formalism satisfy key rationality postulates.
GRIDSEARCH: Stata module to optimize tuning parameter levels with a grid search

gridsearch runs a user-specified statistical learning (aka machine learning) algorithm repeatedly with a grid of values corresponding to one or two tuning parameters. This facilities the tuning of statistical learning algorithms. Examples of statistical learning algorithms that require tuning include support vector machines, gradient boosting, k-nearest neighbors, random forests. After evaluating all combinations of values according to criterion, gridsearch lists the best combination and the corresponding value of the criterion.