Reviewerly/exHarmony huggingface dataset:
Analyzing Collective Knowledge Towards Public Health Policy Making

Nowadays there exists a plethora of diverse data sources producing tons of healthcare data, augmenting the size of data that finally is stored both in Electronic Health Records (EHRs) and in Personal Health Records (PHRs). Thus, the great challenge that emerges is not only to gather all this data in an efficient and effective manner, but also to extract knowledge out of it. The latter is the key factor that enables healthcare professionals to take serious clinical decisions both on individual and on collective level, finally forming representative public health policies. Towards this direction, the current paper proposes a system that supports a new paradigm of EHRs, the eXtended Health Records (XHRs), which include the majority of the health determinants. XHRs are then transformed into XHRs Networks that capture the clinical, social and human context of diverse population segmentations, producing the corresponding collective knowledge. By exploiting this knowledge, the proposed system is finally able to create multi-modal policies, addressing various facts and evolving risks that arise from diverse population segmentations.
Deep Generalized Convolutional Sum-Product Networks.

Sum-Product Networks (SPNs) are hierarchical, graphical models that combine benefits of deep learning and probabilistic modeling. SPNs offer unique advantages to applications demanding exact probabilistic inference over high-dimensional, noisy inputs. Yet, compared to convolutional neural nets, they struggle with capturing complex spatial relationships in image data. To alleviate this issue, we introduce Deep Generalized Convolutional Sum-Product Networks (DGC-SPNs), which encode spatial features in a way similar to CNNs, while preserving the validity of the probabilistic SPN model. As opposed to existing SPN-based image representations, DGC-SPNs allow for overlapping convolution patches through a novel parameterization of dilations and strides, resulting in significantly improved feature coverage and feature resolution. DGC-SPNs substantially outperform other SPN architectures across several visual datasets and for both generative and discriminative tasks, including image inpainting and classification. These contributions are reinforced by the first simple, scalable, and GPU-optimized implementation of SPNs, integrated with the widely used Keras/TensorFlow framework. The resulting model is fully probabilistic and versatile, yet efficient and straightforward to apply in practical applications in place of traditional deep nets.
Implementing a green co-learningcenter to support sustainable campus development

The paradigm of three pillars of mandatory tasks has shifted at Universitas Gadjah Mada (UGM), from former: education, research, and community service individually, became more integrated into research-based learning and community service. This paper will generally look at the initiative and strategy taken by UGM in planning and developing a sustainable campus. UGM developed the Sustainable Blue Campus program as an implementation of the Education for Sustainable Development (ESD) and Sustainable Development Goals (SDGs) program. The program was expected to be a milestone in increasing participation in sustainable development with a multi-disciplinary approach at UGM. Academic and non-academic atmosphere to support the integrated education, research, and community service processes in universities in the current era must be developed under the style of millennial students. Co-learning system 4.0 in the cyber campus for the millennial student in the era of destructive innovation with the support of sophisticated IT and significant data access seems to be the most appropriate learning media. The empowerment paradigm implements the principles of win-win solution, co-creation, co-finance, flexibility, sustainability. The program will be implemented through network collaboration between UGM and the regional government, industry, community, professional associations. Blue campus revitalization should be able to become the initial sustainable blue earth with a more dignified and sustainable environment and life.
Type-driven Neural Programming by Example.

In this thesis we look into programming by example (PBE), which is about finding a program mapping given inputs to given outputs. PBE has traditionally seen a split between formal versus neural approaches, where formal approaches typically involve deductive techniques such as SAT solvers and types, while the neural approaches involve training on sample input-outputs with their corresponding program, typically using sequence-based machine learning techniques such as LSTMs [41]. As a result of this split, programming types had yet to be used in neural program synthesis techniques. We propose a way to incorporate programming types into a neural program synthesis approach for PBE. We introduce the Typed Neuro-Symbolic Program Synthesis (TNSPS) method based on this idea, and test it in the functional programming context to empirically verify type information may help improve generalization in neural synthesizers on limited-size datasets. Our TNSPS model builds upon the existing Neuro-Symbolic Program Synthesis (NSPS), a tree-based neural synthesizer combining info from input-output examples plus the current program, by further exposing information on types of those input-output examples, of the grammar production rules, as well as of the hole that we wish to expand in the program. We further explain how we generated a dataset within our domain, which uses a limited subset of Haskell as the synthesis language. Finally we discuss several topics of interest that may help take these ideas further. For reproducibility, we release our code publicly.
Cistern Food Single And HCT Trailer For Multiplayer

Single , b-double and HTC Axles with Cistern Food trailer Includes trailers: Curtain, Refrigerator, Flatbed , Gooseneck(Container) And log(Carrying ...
Research Guides: Library and Learning Commons (LC) Item Requests: Get Help - Learning Commons Chat

This guide provides information on requesting, picking-up, setting-up, and returning borrowed items from the Library and Learning Commons.
ЦИФРЛЫҚ ЖАҺАНДАНУ ЖАҒДАЙЫНДА ЖОҒАРЫ ОҚУ ОРЫНДАРЫ КІТАПХАНАЛАРЫНЫҢ ДАМУ ҮРДІСТЕРІ

The article is devoted to automated information and library services in the libraries of higher educational institutionsof the Republic of Kazakhstan and the capabilities of libraries included in the Republican Interuniversity ElectronicLibrary System and highlights the ways of introducing information technologies into the work of libraries of highereducational institutions, replenishing electronic resources, entering domestic, world information resources. The main goalof the libraries of the republic is to increase information literacy, harmonious integration in the digital environment.Implementation of automated work in the library process, the implementation of library information programs. Modernlibraries are looking for ways to rationally combine new technologies with altered forms of life and pave the way for theright solution and embracing global problems. Combining national information, the library plays the role of a majorideological institute, exploring and disseminating, and providing services to consumers in accordance with modernrequirements.
Improving the Efficiency of Stand-Alone Solar PV Power Plants

As a source of alternative energy, solar energy has apparent advantages, including a renewable, inexhaustible, and environmentally friendly resource. However, it has not become widely spread in the Russian Federation. Among the disadvantages of using solar energy are high equipment cost, low efficiency of photovoltaic solar cells, the generated electrical energy instability. The spatio-temporal variability of solar access causes electrical energy instability. It is possible to increase solar photovoltaic plant efficiency by using a tracking system to change the plant sun's spatial orientation. The paper offers mathematical and simulation models of a solar photovoltaic plant with a solar tracking system that allows the plant to be automatically oriented to the sun by matching the production mode and the solar access level. The use of the azimuth plant control system on the sun will increase the power production of the solar PV plant by an average of 28%. The same value will increase by 40% when using the full plant control system.
Design and Implementation of GNSS Receiver Based on SoC Platform

Abstract Benefit from the developed of multi-channel and multi-mode navigation SoC devices, the satellite-borne GNSS receiver scheme with highly integrated SoC platform as the baseband core device has gradually become the mainstream. Based on the NS962 device developed by Space Star Technology CO., LTD, the redundancy design of hardware double backup is proposed. This design is composed of baseband processing module, power management module and interface module. Results show that: the prototype weighs about 3.15kg; size is 198mm × 190mm × 89mm; power consumption is about 17W. It has the capability to process BD B1I/B3I, GPS L1CA/L1P/L2P single and the reliability over 8 years meets the requirement of &gt;0.985. This design provides a new choice for the miniaturized and low-power GNSS receivers.
LibGuides: Children's Books About Feelings: Games & Activities

These are books that talk about various feelings and emotions that children might experience as they grow and develop. These books help children understand their emotions and often show healthy ways to express and work through what they are feeling.
A Framework for Scaling in Filtering and Linear Covariance Analysis

Scaling is used extensively for numerical optimization and trajectory optimization. Its use in the estimation community is almost nonexistent. This paper creates the framework for practical scaling in space navigation, in general, and linear covariance analysis, in particular.
A Novel Docking Algorithm Based On The LiDAR And The V-shape Features

This paper presents a feature based docking strategy with a LiDAR for a non holonomic mobile platform to be charged at the charging station.The docking station has only a V-shape structure to be detected from the LiDAR scans.The proposed docking algorithm consists of three main sections which are the localization of the V-Shaped feature with the LiDAR measurements, the path generation with a quintic polynomial and the Proportional Integral Derivative (PID) implementation to track the generated points of the path and correct the yaw angle along the way.Once the relative position of the docking station is detected by matching the synthetically generated Vshape feature with the current Lidar scans, the trajectories are generated by calculating the coeeficients of the quintic polynomial.Afterwards, the initial states, the states and the yaw angles at the points along the path are taken as inputs and the algorithm corrects the robot's yaw angle as it follows the path by a PID control.The algorithm is both tested within the simulation environment and the real test environment.The platform docks with a good accuracy which is around ± 2 cm in x and y, ± 1 o in .With the proposed strategy, additional sensors are not necessary on the platform or on the charging station.Moreover, the robot can navigate even in the absence of light with a good accuracy.Hence, a cheaper solution is obtained for an important problem of autonomous mobile robotic applications.
Ensemble Learning For Television Program Rating Prediction

Rating is one of the most frequently used metrics in the television industry to evaluate television programs or channels. This research is an attempt to develop a prediction model of television program ratings using rating data gathered from UseeTV (interned-based television service from Telkom Indonesia). The machine learning methods (Random Forest and Extreme Gradient Boosting) were tried out utilizing a set of rating data from 20 television programs collected from January 2018 to August 2019 (train dataset) and evaluated using September 2019 rating data (test dataset). Research results show that Random Forest gives a better result than Extreme Gradient Boosting based on evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). On the training dataset, prediction using Random Forest produced lower RMSE and MAE scores than Extreme Gradient Boosting in all programs, while on the testing dataset, Random Forest produced lower RMSE and MAE scores in 16 programs compared with Extreme Gradient Boosting. According to MAPE score, Random Forest produced more good quality prediction (4 programs in the training dataset, 16 programs in the testing dataset) than Extreme Gradient Boosting method (1 program in the training dataset, 12 programs in the testing dataset) both in training and testing dataset.
Internet of Things Access Control System Based on Smart Contract

The rising of digital economy is due to the rapidly development of the new generation of information technology, represented by Internet of Things (IoT) technology. However, the huge number of sensors have limited resources and lack robust security mechanism, which brings a great risk challenges for centralized access control system. In order to deal with these challenges, the paper proposes a novel Capability-Based Access Control Model (NCBAC), which makes use of the advantages of Capability-Based Access Control (CBAC) decision-making mechanism and introduces role sets and attribute set for smart contract. This model is built for providing a decentralized, flexible, expandable and high-granularity access control system. Additionally, token has been conducted in access control model for enhancing the system's capability. Finally, the simulation experiment results showed the feasibility and effectiveness of the system, which also demonstrates the token mechanism promoting the access control performance of the system effectively.
Research on Innovation Path of Higher Vocational English Teaching Method Based on New Media Technology

With the development of new media technology, English education in higher vocational colleges presents an open trend. The new media era not only affects the application of educational technology, but also triggers a revolution in the field of education. With the development of new media bringing more and more benefits, people realize the importance of new media and apply it in various fields to improve work efficiency. English is the most widely used language in the world, so English teaching is particularly important, and English teaching method directly affects the quality of English teaching. How to innovate English teaching methods in higher vocational colleges and improve English teaching level in higher vocational colleges is a new requirement for English teaching in higher vocational colleges in the new media era. Under the background of new media, English teaching in higher vocational colleges has been greatly influenced and played an important role in English teaching in higher vocational colleges. This paper discusses the influence of new media on English teaching in higher vocational colleges, and puts forward some innovative countermeasures to promote the development of English education in higher vocational colleges, so as to better accomplish the talent training goal of English teaching in higher vocational colleges.
Location-Based Service Recommendation for Cold-Start in Mobile Edge Computing

With the rapid development of the 5G and Internet of Things (IoT), mobile edge computing has gained considerable popularity in academic and industrial field, which provides physical resources closer to end users. Service recommendation in such a distributed environment is a hot issue. However, the cold-start problem for service recommendation in mobile edge computing is still urgent to be solved. In this paper, we propose a service recommendation method based on collaborative filtering (CF) and user location, by comprehensively considering the characteristic of services at the edge and mobility of users. In detail, we first synthesize the service characteristics of each dimension through multidimensional weighting method. We further introduce the idea of Inverse_CF_Rec to the traditional CF and then predict the lost QoS value to solve the problem of sparse data. Finally, a recommendation algorithm based on predicted QoS value and user geographic location is proposed to recommend appropriate services to users. The experimental results show that our multidimensional inverse similarity recommendation algorithm based on collaborative filtering (MDICF) outperforms Inverse_CF_Rec in terms of the accuracy of recommendation.
Comparative Analysis of Drift Detection Based Adaptive Ensemble Model with Different Drift Detection Techniques

Stream data mining is a popular research area these days. The concept drift detection and drift handling are the biggest challenges of stream data mining. Several drift detection algorithms have been developed which can accurately detect various drifts but have the problem of false-positive drift detection. The false-positive drift detection leads to the performance degradation of the classifier because of unnecessary training in between analyses. Classifier ensemble has shown its efficiency for drift detection, drift handling, and classification. But the ensemble classifiers could not detect the exact position of drift occurrence, so it has to update itself at some fixed interval, which leads to an unnecessary computational burden on the system. Combining the drift detection algorithm with an ensemble classifier can improve the performance and also solve the problems of false-positive drift detection and unnecessary updating of the ensemble classifier. In this paper, a model is proposed that creates a weighted adaptive ensemble classifier by updating it only when a drift detection signal is given by the used drift detection method. The proposed model is evaluated on text-based stream data for sentiment analysis and opinion mining with multiple drift detection algorithms and with multiple classification algorithms as base classifiers for the ensemble. A comparative analysis has been done, and the results have shown the efficiency of the proposed models.
Awareness and Utilization of Electronic Resources among Junior Research Fellows with Special Reference to Christian Medical College &amp; Hospital, Vellore, Tamil Nadu

Electronic is the electronic depiction of information. This information is available in various forms like e-books, e-journal, e-learning tutors, etc. The study aimed at finding the awareness and utilization of e-resources by the junior research fellows Christian Medical College and Hospital, Vellore. A questionnaire was distributed to the end- users to collect the data. The distribution was done to the selected samples; collect the sound samples. The study aims to find the awareness about the availability of e-resources. The result as reveal what type of e-resources preferred, what searching engine was used most, problem faced during accessing the e-resources, ranking of the available e-resources and to find the percentage of received e-resources from library services. The analysis also reveals few other data which should help to improve the library services.
An application of automatic event detection based on neural network at St Gallen (Switzerland) deep geothermal field

&amp;lt;p&amp;gt;In seismology, when dealing with low signal-to-noise recordings, traditional event detection methods are often unable to recognise all the weak events hidden within the seismic noise. We are interested in investigating how machine learning techniques can be a useful tool to improve automatic event detection by recognising the similarity between events. We are &amp;amp;#160;interested in studying areas where anthropogenic activity, related to the exploitation of subsoil resources, can generate induced seismicity. Therefore, it is essential to increase the detection of weak events to improve knowledge about the seismicity of the area and its related consequences.&amp;lt;br&amp;gt;The SOM (Self-Organizing Map) is an unsupervised machine learning approach that is widely used for clustering, visualization and data-exploration tasks in various applications. The SOM carries out a nonlinear mapping of data onto a two-dimensional map, preserving the most important topological and metric relationships of the data. One of the reasons for using SOM for clustering indeed is to benefit from its topological structure when interpreting the data clusters.&amp;amp;#160;&amp;lt;br&amp;gt;In the preprocessing stage, features extraction is done by using both the linear prediction coding (LPC) technique for coding the spectrograms, and a waveform parameterization for characterizing amplitude characteristics in the time domain, for each of the three components.&amp;lt;br&amp;gt;The SOM was trained on dataset, recorded at the St Gallen geothermal site, composed of 388 records of seismic noise and 347 earthquakes with magnitude (ML&amp;lt;sup&amp;gt;corr&amp;lt;/sup&amp;gt;) between -1.2 and 3.5 collected by the Swiss Seismological Service in 2013 while realizing well control measures after drilling and acidizing the GT-1 well.&amp;lt;br&amp;gt;We obtained promising first results as SOM strategy correctly discriminates all known earthquakes events, clustering them into different nodes, distant from the group of nodes where noise falls. We also jointly tested synthetic traces in which we have hidden events traces within seismic noise or noise artificially generated. We studied the signals of each cluster individually, assessing the similarities of the waveform and spectral characteristics for the three components. In addition, the results are also evaluated in terms of events location, hypocentral distance, magnitude, and origin time.&amp;lt;br&amp;gt;This work has been supported by PRIN-2017 MATISSE project, No 20177EPPN2, funded by the Italian Ministry of Education and Research.&amp;lt;/p&amp;gt;
Efficient Querying for Cooperative Probabilistic Commitments

Multiagent systems can use commitments as the core of a general coordination infrastructure, supporting both cooperative and non-cooperative interactions. Agents whose objectives are aligned, and where one agent can help another achieve greater reward by sacrificing some of its own reward, should choose a cooperative commitment to maximize their joint reward. We present a solution to the problem of how cooperative agents can efficiently find an (approximately) optimal commitment by querying about carefully-selected commitment choices. We prove structural properties of the agents' values as functions of the parameters of the commitment specification, and develop a greedy method for composing a query with provable approximation bounds, which we empirically show can find nearly optimal commitments in a fraction of the time methods that lack our insights require.
An Eye Tracking and EEG Based Human-Robotic Interactive System for Motion Impaired Patients

Amyotrophic lateral sclerosis (ALS) patients suffer from difficulties in grasping objects in daily life. To help them regain impaired functionalities at a certain level, in this article, we present a visual feedback based eye tracking and EEG based human-machine interactive technique, in which the eye tracking system is used for locating user desired objects, and the EEG system is for instructing corresponding operations. A robotic arm is employed here to grasp the targeted objects. Experimental results demonstrate that the developed system can accurately reflect user's intentions in terms of grasping surrounding objects.