Reviewerly/exHarmony huggingface dataset:
LibGuides: Agricultural Science Collection: Searchable Fields

A guide to all aspects of Agricultural Science Collection including content, searching, and viewing results on the new ProQuest platform.
Online marks entry system

This paper describes the entry of student’s oral, practical, term work marks manually. After checking the answer sheet and taking oral and practical, faculties have to fill up marks in paper or sheet provided by the exam cell manually. Therefore there are possibilities of mistake happening during entering these marks. To minimize this problem we are going to make one system which allows the staff to enter the marks and verify the marks and then generate the report of this mark sheet. We are developing a system i. e ONLINE MARKS ENTRY SYSTEM to reduce the efforts of entering marks manually by the institute staff. Here in this system, the college department staffs are provided with the user name and password through the mail, where they will log in and subjects will be allocated to the staff by the head of the department and according to their subjects, they will enter the internal marks of their subjects. They can verify the marks and then generate the report of the sheet.
Session details: Theme: AI and agents: KLP - Knowledge and language processing track

The ACM SAC2019 special track on Knowledge and Language Processing (KLP) (http://klp.fbk.eu/) aims to promote a forum for scientists, engineers and practitioners, in academia and industry, to investigate techniques and application of knowledge engineering and natural language processing, focusing in particular on approaches combining them. This is an extremely interdisciplinary emerging research area, at the core of Artificial Intelligence, combining and complementing the scientific results from Natural Language Processing and Knowledge Representation and Reasoning.
LibGuides: Environmental Science Collection: Advanced Search

A guide to all aspects of Environmental Science Collection including content, searching, and viewing results on the new ProQuest platform.
LibGuides: Advanced Technologies & Aerospace Collection: ProQuest Results

A guide to all aspects of the Advanced Technologies & Aerospace Collection including content, searching, and viewing results on the new ProQuest platform.
Improving Knowledge Base Construction from Robust Infobox Extraction

Boya Peng, Yejin Huh, Xiao Ling, Michele Banko. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Industry Papers). 2019.
Research on the Educational Mode of Chinese Piano Works in Piano Teaching in Colleges and Universities

For a long time, western works have dominated the piano teaching in colleges and universities, while Chinese piano works have received a cold reception.In recent years, along with the overall progress and development of the piano industry, the value of Chinese piano works and their significance in teaching have also been re-recognized, and they play an irreplaceable role in students' playing concepts and techniques.Therefore, it is necessary to strengthen the learning status of Chinese-style piano works in teaching, so that students can learn and understand Chinese-style piano works on their own initiative.This paper systematically analyzes the significance of Chinese-style piano works in the piano teaching in colleges and universities, and discusses the application of the education mode of Chinese-style piano works in the piano teaching in colleges and universities, so as to strengthen students' learning of Chinese piano music, carry
STRATEGIC PROJECTS IN EDUCATION: SOCIO-PHILOSOPHICAL ANALYSIS

The article explores the issues of modern higher education in relation to the state transition to the knowledge-based economy. Considering the issues within the socio-philosophical aspect results in understanding a phenomenon that prolonged «obsolescence»of knowledge, separation of theory from practice overextend the measure of sustainability of an educational organization and methodically generate the necessity to modernize its basic processes: education, science and innovations. The research educes the differences between the operational management, focused on performing quantitative indexes, and strategic management, targeted to qualitative changes of the basic processes. The article investigates the processes of forming development strategies and strategic projects of universities. It reveals that strategic projects, focused on strategic management, can provide major breakthroughs of the universities unlike the development programme, realized within the operational management of a university. The article demonstrates the activity results in forming a development programme and experience in realizing strategic projects by Federal State Budget Educational Establishment of Higher Education «Reshetnev Siberian State University of Science and Technology» example.
Acquisition of 3-dimensional stereoscopic images under a microscope by a swinging glass stage

To execute efficient and precise handling tasks in micro world, a bilateral system which connects the micro and macro worlds in both directions has been developed. In order to improve the work efficiency, 3-dimensional observation of objects in the micro world is necessary. Two images with parallax are necessary for the 3-D observation, but it is difficult to obtain these images under a microscope. In this research, we proposed a method of obtaining parallax images by inserting a glass plate under the microscope, tilting the plate and thus changing the optical path by a small distance. In order to electrically control the tilt of the glass plate, we developed a swinging actuator driven by voice coils. In addition, mechanical characteristics of the swinging glass stage and characteristics of the feedback control system were confirmed.
Adaptive Scheduling Algorithm for Hadoop Node Capability in Heterogeneous Resource Environment

In order to overcome the shortcomings of the existing scheduling allocation methods for Hadoop clusters in heterogeneous resource environments, an adaptive scheduling algorithm NCAS (node capacity adaptive scheduling) based on node capability is proposed. Firstly, NCAS algorithm calculates the scheduling factor according to the node performance and task characteristics; then, the scheduling factor determines the amount of data and task slots that each node should share; finally, the data and tasks are distributed more to fast nodes and less to slow nodes. The experimental results show that compared with the traditional scheduling algorithm, NCAS algorithm greatly reduces the number of backup tasks to start, significantly reduces job completion time, and improves the efficiency of task execution.
Review on underwater image enhancement using multiscale fusion

We introduce an effective technique to enhance the images captured underwater and degraded due to the medium scattering and absorption. Our method is a single image approach that does not require specialized hardware or knowledge about the underwater conditions or the scene structure. It builds on the blending of two images that are directly derived from a color compensated and white balanced version of the original degraded image. The two images to fusion, as well as their associated weight maps, are defined to promote the transfer of edges and color contrast to the output image. To avoid that the sharp weight map transitions create artifacts in the low-frequency components of the reconstructed image, we also adopt a multiscale fusion strategy. Our extensive qualitative and quantitative evaluation reveals that our enhanced images and videos are characterized by better exposedness of the dark regions, improved global contrast, and edges sharpness. Our validation also proves that our algorithm is reasonably independent of the camera settings, and improves the accuracy of several image processing applications, such as image segmentation and keypoint matching.
A Study of Web Based Information Sources and Services

The purpose of this study is to show the importance of web services towards libraries. A brief description of Web Services, different resources of web based services has been discussed followed by the present scenario and future of Web Based Services in India. Web services provide many benefits to library community, but majority of these benefits need standardisation of web-based services and active involvement/participation of library staff. The findings of this paper make library community aware of available web resources for libraries and influence them to actively involve, and participate in the orientation programmes and thereby enhance the quality of library activities and develop the productivity of research work.
Memory-Efficient Backpropagation for Recurrent Neural Networks

Recurrent Neural Networks (RNN) process sequential data to capture the time-dependency in the input signal. Training a deep RNN conventionally involves segmenting the data sequence to fit the model into memory. Increasing the segment size permits the model to better capture long-term dependencies at the expense of creating larger models that may not fit in memory. Therefore, we introduce a technique to allow designers to train a segmented RNN and obtain the same model parameters as if the entire data sequence was applied regardless of the segment size. This enables an optimal capturing of long-term dependencies. This technique can increase the computational complexity during training. Hence, the proposed technique grants designers the flexibility of balancing memory and runtime requirements. To evaluate the proposed method, we compared the total loss achieved on the testing dataset after every epoch while varying the size of the segments. The results we achieved show matching loss graphs irrespective of the segment size.
Money for Votes: The Causes and Consequences of Electoral Clientelism in Africa. By Eric Kramon. Cambridge: Cambridge University Press, 2017. 244p. $99.99 cloth.

An abstract is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Kashagan Oilfield Microseismic Monitoring

Summary Güralp has successfully installed 11 stations comprising borehole seismometers broadband around the Kashagan oilfield in the Caspian Sea in order to monitor both naturally occurring seismic events in the area and possible earthquakes induced by production activities managed by NCOC. The network operates in real-time by using digitisers connected to satellite modems capable of providing connectivity in order to remote control of the instrumentation. Güralp continues to produce quarterly reports for NCOC containing detailed information about the network state-of-health, list of detected events and their nature.
Scientometric Assessment of Publication Output of Open Access E-Books on Chemical Technology in DOAB

Abstract Open access initiatives are gaining momentum in order to cater the information needs of the information society. Like DOAJ, DOAB is a popular directory. The paper aims to highlight open access E-Books on Chemical Technology available in DOAB. Requisite data is harvested from DOAB Database. Subsequently data analysis is done to know the status of registration of available books under BY-NC-ND Licenses. The study reveals that 36% of the total books published are co-authored by two authors and the highest publications, 36.6%, is published by MPDI followed by Springer. Analysis is made to know the year wise publication of E-Books in which the year 2018 shows the highest number of publications on chemical technology by contributing about 27.28% e-Book on chemical technology. The highest number (70%)) of e-books are published in English language. Keywords: Open access (OA), e-books, Creative Commons (CC), Directory of Open Access Books (DOAB), Licenses, Chemical Technology. Cite this Article Amir Amanullah, Gajanan P. Khiste. Scientometric Assessment of publication output of Open Access E-Books on Chemical Technology in DOAB. Journal of Advancements in Library Sciences. 2019; 6(Special Issue 1): 15s–21s.
Solving Regression Models with First Order Stochastic Based Optimizers

Most of the regression-based Deep Learning (DL) algorithms which were recently proposed are based on Convolutional Neural Networks (CNN) trained by using l 2 loss function. In order to avoid the vulnerability of it to outliers, some authors propose to combine regression methods with a classification function like combination of l 2 loss function and softmax or a strategy based on bounding boxes what improves the performance of the DL neural network.In this research we propose using a combination of first order Stochastic Gradient Descent (SGD) optimizer for Artificial Neural Networks (ANNs) with Deep Learning (DL) and Deep Learning Multilayered Perceptron DLMLP with Back Propagation (BP). We focus on optimization of stochastic objectives, parameters and implementation of different activation functions in solving medical regression problems in high-dimensional space.
Data governance in smart factory: Effective metadata management

Most of the enterprises recognized the importance of Data governance and started data governance programs either at the Enterprise at the individual Business units level. Data governance councils at various levels in an enterprise define and enforce data quality and security, using policies, standards, and Procedures. The success of Data governance program is heavily relying on people or team who is governing the data governance council. However, Data governance is best implemented by leveraging people, process, and Technology. In the past, subject matter experts of individual data domains maintained the metadata, which is a critical component of data governance. Efficient and automated metadata management, which can be established today by leveraging technology and process not just SMEs, has the potential to mitigate the risk of people dependency. Smart factories, which are heavily automated, generates data at a scale and speed which were never seen before by manufacturing industry, and face the crisis to maintain the data quality, and to make the data available for analytics for any further use. Data generated by smart factories are diverse and are mostly stored in distributed systems, which further increases the complexity of data governance through metadata management. Efficient metadata management, mostly automated, can help smart factories to achieve Data governance goals, and help to provide data as a service. This paper discusses the shortcomings of Hybrid data governance model – acknowledged as the better model for data governance by industry – and proposes a system architecture which has all the benefits of the hybrid model in addition to improvements that are necessary for a smart factory.
Low-cost multiple object tracking for embedded vision applications

This paper presents a low-cost multiple object tracking (MOT) technique by employing a novel appearance update model for object appearance modeling using K-means. The state-of-the-art work has attained a very high accuracy without considering the real-time aspects necessitated by currently trending embedded vision platforms. The major research on multiple object tracking is used to update the appearance model in every frame while discounting its persistent nature. The proposed appearance update model reduces the computational cost of the state-of-the-art MOT 6-fold by exploiting this facet of persistent appearance over the sequence of frames. To ensure accuracy, the proposed model is tested on different publicly available standard datasets with challenging situations for both indoor and outdoor scenarios. The experimental results illustrate that our model successfully achieves multiple object tracking while coping with long-term and complete occlusion. The proposed method achieves the same accuracy in comparison with the state-of-the-art baseline methods. Moreover, and most importantly, the proposed method is cost-effective in terms of computing and/or memory requirements in comparison to the state-of-the-art techniques. All these traits make our design very suitable for real-time and embedded video surveillance applications with low computing/memory resources.
The Gift Exchange Game: Managing Opponent Actions

Interacting with an opponent is a fundamental concern in multi-agent systems. In this work, we consider ways in which an agent can manipulate an opponent to adopt a preferred strategy. This difficult problem is often further complicated by the difficulty of analyzing the game. We have developed the Gift Exchange game, a sequential game that is deliberately simplified to focus on how to interact with an opponent. In this paper we describe the game and discuss different methods an agent might use to influence its opponent to select a preferred action. We show results from using simulated annealing to find optimal strategies to use against a learning opponent.
Analisis Pengaruh Konteks dan Klien & Koalisi terhadap Kinerja Implementasi Gerakan Literasi Sekolah di Kota Semarang

This study aims to describe the position and role of implementation performance, context and client & coalition in the course of School Literacy Movement Program as well as analyze the effect of policy context and client & coalition on the implementation performance of School Literacy Movement in Semarang City. Method used in this study is quantitative explanatory by collecting data through questionnaires, interviews and docmentation studies. Processing techniques use simple and multiple linear regression data analysis. The results showed that implementation performance, context and client & coalition were quite good according to respondents’ assessments. Partially, context affects implementation performance of School Literacy Movement significantly and positively by 45,1% and client & coalition affect the implementation performance of School Literacy Movement by 49,7%. Simultaneously, policy context and client & coalition affect the implementation performance of School Literacy Movement by 60,4%. In th future, schools need to enrich reading facilities and strengthen collaboration with supporting parties.
Depth Estimation on Underwater Omni-directional Images Using a Deep Neural Network

In this work, we exploit a depth estimation Fully Convolutional Residual Neural Network (FCRN) for in-air perspective images to estimate the depth of underwater perspective and omni-directional images. We train one conventional and one spherical FCRN for underwater perspective and omni-directional images, respectively. The spherical FCRN is derived from the perspective FCRN via a spherical longitude-latitude mapping. For that, the omni-directional camera is modeled as a sphere, while images captured by it are displayed in the longitude-latitude form. Due to the lack of underwater datasets, we synthesize images in both data-driven and theoretical ways, which are used in training and testing. Finally, experiments are conducted on these synthetic images and results are displayed in both qualitative and quantitative way. The comparison between ground truth and the estimated depth map indicates the effectiveness of our method.
A New Topology-Preserving Distance Metric with Applications to Multi-dimensional Data Clustering

In many cases of high dimensional data analysis, data points may lie on manifolds of very complex shapes/geometries. Thus, the usual Euclidean distance may lead to suboptimal results when utilized in clustering or visualization operations. In this work, we introduce a new distance definition in multi-dimensional spaces that preserves the topology of the data point manifold. The parameters of the proposed distance are discussed and their physical meaning is explored through 2 and 3-dimensional synthetic datasets. A robust method for the parameterization of the algorithm is suggested. Finally, a modification of the well-known k-means clustering algorithm is introduced, to exploit the benefits of the proposed distance metric for data clustering. Comparative results including other established clustering algorithms are presented in terms of cluster purity and V-measure, for a number of well-known datasets.
Long-term Autonomous Mobile Manipulation under Uncertainty

We describe a hierarchical belief space planning framework to achieve robust long-term autonomous mobile manipulation behavior under uncertainty. The approach relies on condensing belief distributions across different abstractions to simultaneously suppress uncertainty and mitigate risk at run-time. We evaluate this system in an experimental domain that requires a robot to monitor and clean a dynamic unstructured environment, executing hundreds of physical actions without failures that require human intervention. Results indicate that this framework provides a sound basis for cognitive robots in uncertain and dynamic environments.
KAJIAN METODE FUZZY K-RATAAN DAN FUZZY K-MEDOIDS (STUDI KASUS: PENGELOMPOKAN DESA DI KABUPATEN SORONG TAHUN 2016 BERDASARKAN STATUS KETERTINGGALAN)

The developments research in the cluster analysis using the fuzzy method. The fuzzy method allocates to each group with membership value located at interval [0, 1], showing the magnitude of the possibility of an object being a member into a particular group. Outlier in data very important known before grouping, because affect the final result. Grouping by using the mean value as the center of the group will be more sensitive than using the median value, so this research applies fuzzy c-means and fuzzy c-medoid method to the grouping of villages in Sorong Regency Year 2016 based on the underdevelopment status and examine the goodness of both methods. There are 23.2% of villages that do not change when done grouping with both methods. Overall average distance of group center object and varians in the resulting group the two methods are the same, the varins between groups of fuzzy c-means is greater than the fuzzy c-medoid method.
The Relationship of Leadership Dtyles Towards Employees’ Creativity: Mediating Effect of Employees’ Empowerment in UAE Manufacturing Companies

The purpose of this study is to empirically investigate impact of transformational and transactional leadership on employees’ creativity and employee empowerment in this association. Over the past many decades, employees have become an organizing way of coordinating work within organization. This trend creates new challenges for leaders, as they are expected to improve their creativity simultaneously. This study proposes a conceptual framework for understanding leadership styles and employees’ empowerment to individual employees which would influence creativity. The conceptual framework includes employees’ empowerment and support for creativity as mediating variables in the effect of leadership styles on employees’ creativity. The study also validates the proposed conceptual framework using a second-order confirmatory factor analysis technique, namely, structural equations modeling, with the data collected from 597 followers from four e-government organizations in the UAE. The findings suggest that transformational and transactional leadership has significant positive impact on employees’ creativity. Moreover, employees’ empowerment have partial mediation effect in the relationship between leadership styles and employees’ creativity.This study contributes to the research that identifies employees’ creativity influenced by transformational and transactional leadership that can affect employee success and performance, particularly in the context of UAE-based egovernment organizations.