Reviewerly/exHarmony huggingface dataset:
The Impact of Fiscal Conservatism ; A Case Study of the 2007 National Pension System Reform in Korea

This research intends to identify the influential factors in the 2007 National Pension System (NPS) reform in Korea (the Republic of Korea) which drove the NPS toward a structural transformation. This research also examines the applicability of the theory of Clemens and Cook (1999) to the Korean policy shift, who argue that the innate driving force of a policy, an internal contradiction, can be a critical source of structural policy change. A literature review based case study was carried out in this research. The findings are as follows. Firstly, rising fiscal conservatism was the main determinant of the 2007 NPS structural reform. The processes and conditions of the reform documented were: the fiscal conservatism embedded in NPS generated serious policy problems and led to an accumulation of the internal contradictions within NPS by raising the question on its fundamental policy goal. As time passed without any self-correction mechanism with respect to the problematic policy, the NPS lost credibility in the eyes of the public and also lost policy legitimacy. At the same time, there was a competing policy alternative to the NPS. This was the universalistic tax-based Basic Old-Age Pension System. This has been a challenge to NPSin that it had been designed based on the social insurance financing principle. The pre-conditions for the structural NPS reform were fully complete and they could be exploited by self-interested political parties in the following policy-making stages. Secondly, the theoretical assumption that the internal contradiction of a policy can be a decisive power for structural transformation, as suggested by Clemens and Cook (1999) among others, was proven to be theoretically and practically accurate in the Korean public pension reform case.
Readership Behaviour Analysis of Working Women

Readership survey benefits press to know the preference and reading behavior of readers by assessing reading frequency, reading pattern and reading response which is otherwise hard to measure. The study analyzed the working women’s reading behavior towards “Beti Bachao Beti Padhao” and also identified the recall ability of BBBP issues published in three major newspapers. Most of the respondents were 30-40 years in age (29%) belonged to high caste (50%) was educated up-to graduate level (38%) and were married (60%). Size of family was medium (38%), had farming (42%) as family occupation, monthly income of family was more than 75,000 (38%). Reading behaviour of majority of respondents was high (40%) followed by medium (32%) and low (28%). As recall ability is concerned after one week of news article publishing 44 % of the respondents completely recalled the BBBP issues with the help of newspaper cuttings. Education of respondents was significantly associated with reading behaviour at 5 % level of significance.
Ethical and Legal Aspects of Using Genome Editing Technologies in Medicine (Review)

According to many experts, the turning point in the development of genome editing technologies (GET) was 2012, when Feng Zhang and Jennifer Doudna independently proposed the adaptive bacterial immunity system CRISPR/Cas9 for editing the genome of living cells of eukaryotic organisms.Since then, the range of applications of CRISPR/Cas9 technology and related GET has continued to grow like an avalanche.Thus, new genetically modified microorganisms, plants, and animals have been created, the experimental studies on the genetic foundations of life have greatly expanded, and revolutionary approaches to therapy and prevention of incurable diseases have been developed.However, the indisputable advantages of GET are associated with high risks (real and potential) to the environment, human health, and society as a whole.Significant progress in the genome editing in eukaryotes has led to a rapid appearance of humans with an "improved" genome, despite the openly expressed opposition of leading scientists working in this field.Among them, David Baltimore, Paul Berg, Jennifer Doudna, George Church, and Martin Jinek are calling for a global suspension of work with human embryos until the technical, legal and ethical standards in this area are developed.There is an urgent need for the development of an unambiguous public position and improvement of the regulatory framework for the GET, including that in the Russian Federation; the present review attempts to address the urgent issue of GET-related regulations.We discuss various approaches to regulating the use of GET in medicine.We review legal acts and ethical recommendations around the world concerning the GET-mediated modification of the plant and animal genetic material for the purpose of creating medical products and drugs.We also address the sensitive issue of editing the genome of human cells (somatic or germ).Special attention is paid to the relevant legal and ethical standards exiting in the Russian Federation.The presented data allow for a better understanding of the current situation and the areas of further research into GET, where the development and implementation of regulatory standards are especially urgent.
DESAIN GAMBAR ALAT PELEPAS BAN SEPEDA MOTOR DENGAN SOFTWARE AUTOCAD

The development of science and technology covers all fields and has a huge impact on society. Technology demands will bring people to think of creating something new. One of the human creations that is an important part of human life is machines, these machines have many models, shapes and benefits, one of which is a motor vehicle tire removal and installation machine, in general the motorized tire removal and installation machines are widely used in small and medium enterprises, the main drivers are usually using air pressure or pneumatics. The objective to be achieved in this research is to realize the design of a motorcycle tire release tool with the AutoCAD 2012 software. By designing a drawing design to release a motorcycle tire, and a tool support frame. The Planning Stage by determining the title and subject matter and software needed and then making the plot, the implementation phase begins to determine the frame design, then designs the framework and documents the activities carried out during the research. Checking the validity of the data Checking the validity of the data is done by displaying the results of the design of motorcycle tire release machines in accordance with the research plan. Analysis of research data is by collecting data, reducing data, presenting data. Preparation of this stage report all documentation collected and compiled into a research report. The design results are chuck, bottom frame, release hook arm, release hook, release device design with installed wheels, conclusions Concave wheel rim clamp has three sides that serve to clamp to all sides of the rim, each clamping clamp has a stretch setting on each side.
Modeling the Personas of Primary Care Communication Modality Usage: Experiences from the R-Health Direct Primary Care Model.

Recent advancements in mediated communication technologies enable unfettered communication between patients and their providers. We explore patients' utilization of different direct and mediated communication modalities in a Direct Primary Care (DPC) clinic and the patient characteristics that predict their communication behavior patterns. Based on this knowledge, we developed 2 patient personas that explicate the nuances of patients who tend to prefer visiting the clinic in person versus those who use mediated modalities more often. We hope this study may inform future work in understanding and supporting patient-provider communication in a new technical environment. The results suggest that patients and their health team alike may be incentivized to voluntarily adopt and utilize multi-modality communication in a DPC setting.
How Does Informal Entrepreneurship Affect Innovation?

We analyze the impact of informal entrepreneurship on innovation in emerging economies. We build on agency theory to argue that formal new ventures that were informally created engage more in imitative new product development and less in innovative new product development, because they suffer from informality costs. We also propose that the agency costs of owners of the informally created formal new ventures modify informality costs. Specifically, we argue that foreign and private business group ownership compensates for informality costs while state ownership heightens them, altering the impact of informal creation on imitative and innovative new product development.
Towards a Unified Model Representation of Machine Learning Knowledge

Nowadays, Machine Learning (ML) algorithms are being widely applied in virtually all possible scenarios.However, developing a ML project entails the effort of many ML experts who have to select and configure the appropriate algorithm to process the data to learn from, between other things.Since there exist thousands of algorithms, it becomes a time-consuming and challenging task.To this end, recently, AutoML emerged to provide mechanisms to automate parts of this process.However, most of the efforts focus on applying brute force procedures to try different algorithms or configuration and select the one which gives better results.To make a smarter and more efficient selection, a repository of knowledge is necessary.To this end, this paper proposes (1) an approach towards a common language to consolidate the current distributed knowledge sources related the algorithm selection in ML, and (2) a method to join the knowledge gathered through this language in a unified store that can be exploited later on.The preliminary evaluations of this approach allow to create a unified store collecting the knowledge of 13 different sources and to identify a bunch of research lines to conduct.
Integrating Social Circles and Network Representation Learning for Item Recommendation

With the ever increasing popularity of social network services, social network platforms provide rich and additional information for recommendation algorithms. More and more researchers utilize the trust relationships of users to improve the performance of recommendation algorithms. However, most of the existing social-network-based recommendation algorithms ignore the following problems: (1) In different domains, users tend to trust different friends. (2) the performance of recommendation algorithms is limited by the coarse-grained trust relationships. In this paper, we propose a novel recommendation algorithm that integrates the social circles and the network representation learning for item recommendation. Specifically, we firstly infer the domain-specific social trust circles based on the original users’ rating information and the social network information. Next, we adopt the network representation technique to embed the domain-specific social trust circle into a low-dimensional space, and then utilize the low-dimensional representations of users to infer the fine-grained trust relationships between users. Finally, we integrate the fine-gained trust relationships with the domain-specific matrix factorization model to learn the latent user and item feature vectors. Experimental results on real-world datasets show that our proposed approach outperforms the traditional social-network-based recommendation algorithms.
Training Of Natural Science Teachers As Part Of Lifelong Learning

Teachers who are familiar with the basics of the theory and methodology of teaching academic subjects are the only ones who can provide high quality of graduates’ training and solid students’ achievements. Today, there is a growing interest in the training of highly qualified personnel, their long-term employment, professional development and motivation to work. The article studies the problem of chemistry and geography teachers’ professional training in the system of higher education, their further professional training and retraining as part of lifelong pedagogical learning that is relevant for the didactics of the natural science education. School practice shows that many teachers are still focused on the translation of subject knowledge; and its assimilation provides the results of the unified state exam and prepares for universities, which does not contribute to the development of students competences such as cognitive initiative, risk of choice, commitment to the realization of cognitive interest and novelty. In the process of studying natural sciences, a student master’s knowledge and skills in a particular subject area. Competence, in its turn, shows how students have mastered professional functions that they have to perform, that is, they master not only subject-related, but also meta-subject experience. Competence means a motivated sequence of actions in a specific context. As a result, such skills as observing, explaining, classifying, summarizing, recognizing, predicting, drawing conclusions can be developed through an experiment, modeling, design, construction; can develop and be filled with specific content. Universal ways of activity are assimilated on this basis.
HyperPS: A Hypervisor Monitoring Approach Based on Privilege Separation

In monolithic operating system (OS), any error of system software can be exploit to destroy the whole system. The situation becomes much more severe in cloud environment, when the kernel and the hypervisor share the same address space. The security of guest Virtual Machines (VMs), both sensitive data and vital code, can no longer be guaranteed, once the hypervisor is compromised. Therefore, it is essential to deploy some security approaches to secure VMs, regardless of the hypervisor is safe or not. Some approaches propose microhypervisor reducing attack surface, or a new software requiring a higher privilege level than hypervisor. In this paper, we propose a novel approach, named HyperPS, which separates the fundamental and crucial privilege into a new trusted environment in order to monitor hypervisor. A pivotal condition for HyperPS is that hypervisor must not be allowed to manipulate any security-sensitive system resources, such as page tables, system control registers, interaction between VM and hypervisor as well as VM memory mapping. Besides, HyperPS proposes a trusted environment which does not rely on any higher privilege than the hypervisor. We have implemented a prototype for KVM hypervisor on x86 platform with multiple VMs running Linux. KVM with HyperPS can be applied to current commercial cloud computing industry with portability. The security analysis shows that this approach can provide effective monitoring against attacks, and the performance evaluation confirms the efficiency of HyperPS.
Multi-Level Compare-Aggregate Model for Text Matching

Text matching is important for a variety of natural language processing tasks, such as paraphrase identification and natural language inference. Recent studies have achieved very promising results under the compare-aggregate framework. A limitation of previous approaches following this framework is that they solely conduct matching at word level. In this paper, we propose a multi-level compare-aggregate model (MLCA), which matches each word in one text against the other text at three different levels, word level (word-by-word matching), phrase level (word-by-phrase matching) and sentence level (word-bysentence matching). Then the results of these levels of matching are aggregated for making final matching decision. We evaluate our model on two different tasks: paraphrase identification and natural language inference. Experimental results show that our model achieves the state-of-the-art performance on both tasks.
Fruitful Synergies between Computer Science, Historical Studies and Archives: The Experience in the PRiSMHA Project

In this paper we present the mid-term results of the PRiSMHA project, aimed to contribute in building a digital "smart archivist", i.e., a web-based system providing an innovative access to historical archives.Such a system is endowed with a semantic layer over existing archival metadata, including computational ontologies and a knowledge base, containing a formal description of the content of the documents stored in the archives.The paper focuses on the fruitful synergies employed to reach its goal.In particular, it explains the steps of the "spiral" process implemented for creating a full-fledged formal semantic model, through the interaction between computer scientists, historians, and archivists.The paper also presents some "core sideeffects" of this process: an analytical card for each document has been produced, all selected documents have been digitized, OCR-ized (when possible), and linked to a record on the archival platform.This experience enabled us to define a virtuous procedural model, from the paper documents up to the digital "smart archivist", based on a close collaboration between universities and cultural and historical institutions.
An Empirical Study of Chunk Size Variation in DASH System

Most video players use Dynamic Adaptive Streaming over HTTP (DASH) to ensure high Quality of Experience (QoE) in varying network conditions. To improve the encoding efficiency, streaming providers use Variable BitRate (VBR) encoding for video contents, where the video chunks with the same nominal bitrate (video quality) have different instantaneous bitrates and chunk sizes. However, the chunk size variation is assumed to be trivial compared with the network bandwidth variation, and existing bitrate adaptation algorithms simply use the nominal information to make bitrate decisions. In this paper, we present an empirical study of chunk size variation in DASH to explore whether the algorithm can ignore this stochastic factor. We measure the characteristics of chunk size variation based on a rich set of videos and analyze its impact on QoE by trace-driven experiments. Analysis results show that all the buffer-aware algorithms suffer from degraded QoE if they do not consider the chunk size variation. To eliminate the negative impact, we suggest that the bitrate adaptation algorithm should rely on exact chunk size to adjust bitrate instead of the nominal bitrate. To confirm our insights and suggestion, a preliminary solution is proposed and validated based on a real video player and realistic bandwidth traces.
Multitask Learning or Transfer Learning? Application to Cancer Detection

Multitask Learning (MTL) and Transfer Learning (TL) are two key Machine Learning (ML) approaches which have been widely adopted to improve model’s performance. In Deep Learning (DL) context, these two learning methods have contributed to competitive results in various areas of application even if the size of dataset is relatively small. While MTL involves learning from a key task and other auxiliary tasks simultaneously and sharing signals among them, TL focuses on the transfer of knowledge from already existing solution within the same domain. In this paper, we present MTL and TL based models and their application to Invasive Ductal Carcinoma (IDC) detection. During training, the key learning task in MTL was detection of IDC whereas skin and brain tumor were auxiliary tasks. On the other hand, the TL-based model was trained on skin cancer dataset and the learned representations transferred in order to detect IDC. The accuracy difference between MTL-based model and TL-based model on IDC detection was 8.6% on validation set and 9.37% on training set. On comparing the loss metric of the same models, a cross entropy of 0.18 and 0.08 was recorded on validation set and training set respectively.
Finding Persistent Items using Invertible Bloom Lookup Table

With the rapid development of information technology, the age of big data requires new data processing methods with higher time and space efficiency. The method of finding persistent items in big data stream becomes an important research direction with promising application field, such as network security or sensor data mining. In this paper, the data processing algorithm is designed to quickly record and accurately identify the persistent items from the big data stream with relatively small memory occupation cost. This algorithm combines the data structures of Invertible Bloom Lookup Table (IBLT) and Bloom Filter (BF) to guarantee the reliability. By simulation and comparison, we prove the low false rate of the algorithm.
Are Two Opposite Points Better Than One?

Various opposition-based learning (OBL) strategies were proposed to further improve solution quality since 2005. However, in all existing OBLs, only one opposite point is generated for a given candidate solution in population. There is the old saying that there is strength in numbers. The purpose of this study is to examine the feasibility and effectiveness of double-points opposition-based learning (DOBL). The basic concept of DOBL is first proposed and then some viable forms are introduced in this paper. In addition, previous evaluation function and calculation method are highly complex, which block its application in DOBL. Based on a new evaluation function, another approach is presented to calculate its mathematical expectation more easily. The results by theoretical analysis and simulation experiment over sampling problems indicate that DOBL is better than the conventional OBL. In engineering application, double-points opposition-based learning differential evolution (DODE) is first developed to accelerate its convergence speed. Experiment results over 58 optimization problems show that DODE has an eclectic convergence speed in a fair competitive environment. Furthermore, the contribution of opposite points and the effect of jumping rate are also discussed in detail. When both considering algorithm convergence and reliability, a small jumping rate is generally recommended for an unknown optimization problem.
Reliable Automatic Organ Segmentation from CT Images using Deep CNN

In the field of biomedical image analysis, segmentation is critical for disease diagnosis and treatment planning. While manual segmentation is tedious, time consuming and subjective due to the large shape and appearance variance among different subject, accurate and reliable segmentation is very challenging for automatic segmentation methods at the same time. In this paper, we present our recent effort on developing a reliable segmentation algorithm in the form of a convolutional neural network. Our network architecture is inspired by the popular U-Net and its variation (3D U-Net) and has been carefully modified to maximize bladder and rectum segmentation performance. We transfer our data to four channels to augment the data and prevent overfitting. The Dice similarity coefficient (DSC) is used to evaluate the network's performance. The outcome of experiments demonstrates the superiority of the proposed method.
Features of Inclusive Culture in the Engineering Environment: Studies of Lower Volga Cities

features of inclusiVe culture in the engineering enVironment: studies of lower Volga citiesThe idea of inclusive culture as a principle of implementing everyday social policy, cultural and educational activities is demonstrated.The necessity for inclusive culture of professionals' representatives of different directions is justified.