Reviewerly/exHarmony huggingface dataset:
Environmental Factors Associated With Healthy Aging After Spinal Cord Injury

As the number of adults aging with spinal cord injury (SCI) grows, understanding the factors that contribute to healthy and positive aging is increasingly important. The objective of this research is to examine the role of environmental factors for participation in daily life among adults aging with SCI.
Discovering data sources in a distributed network of heritage information

The Netwerk Digitaal Erfgoed is a Dutch partnership that focuses on improving the visibility, usability and sustainability of digital collections in the cultural heritage sector. The vision is to improve the usability of the data by sur- mounting the borders between the separate collections of the cultural heritage in- stitutions. Key concepts in this vision are the alignment of the data by using shared descriptions (e.g. thesauri), and the publication of the data as Linked Open Data. This demo paper describes a Proof of Concept to test this vision. It uses a register, where only summaries of datasets are stored, instead of all the data. Based on these summaries, a portal can query the register to find what data sources might be of interest, and then query the data directly from the relevant data sources.
A Convolutional Neural Network with Two-Channel Input for Image Super-Resolution

In this work a convolutional neural network with two input channels is proposed for image super-resolution. Initially, the original image is downsampled with bicubic and nearest neighbour interpolation methods and the low-resolution image pair is used as the input to our network. Additionally, the input channels are randomly swapped as an augmentation method during training. The proposed network is a combination of a feedforward architecture and a residual network architecture, to provide a smooth image estimate. We show that the additional input image obtained with nearest neighbour interpolation improves image super-resolution performance with our network, since it acts as an image specific prior information and constrains the solution space for the model to learn. We train the network end-to-end for multiple upscale factors, evaluate the method on standard test datasets, and show that the proposed approach can produce state-of-the-art results.
Monitoring The Assessment Of Student Educational Performance: Information Aspects

The paper discusses information aspects of identifying student educational performance by means of monitoring helpful for assessing this performance within the scope of singled out content clusters focused on achieving set learning objectives. This gives a more comprehensive and objective information on student learning progress. Within the framework of this approach a universal procedure is proposed for conversion of objectives, which are usually not diagnosable, into requirements for educational outcomes. Since these requirements are diagnosable and verifiable, monitoring surveys can be launched. Presenting the monitoring results visually is helpful for identifying regularities in the obtained data array. With the assessment result obtained, managing inputs can be organized as an important element of educational management. There is quite a variety of monitoring tools to choose from, such as computer-assisted telecommunications enabling supervisory bodies to obtain information on specific learning progress of students as well as other information on the instructional process in a given institution of higher learning. Diagnostic data can be processed using Microsoft Office Excel spreadsheets including statistical treatment of obtained data. For the quality of monitoring the most important factor is congruence of the educational performance assessment procedure being used with real educational conditions or to put it in information language terms – the information quality factor. When all the mentioned factors are taken into account, the quality of student learning progress can be assessed more accurately and, accordingly, adequate effort can be put into managing the instructional process as part of educational management.
Korean National Healthcare-Associated Infections Surveillance (KONIS): Hand hygiene surveillance

Although hand hygiene (HH) monitoring by direct observation is considered to be a reliable method, it is labor intensive. The purpose of this study was to confirm regular HH use by collecting and analyzing HH data for Web- and mobile-application-based HH monitoring, as well as to establish a nationwide HH monitoring system. Users evaluated the Web-based report as being useful with regard to ease of access and use, data collection and analysis, and especially data comparison with other hospitals. Mobile applications were considered more difficult to use than the Web-based system. Although hand hygiene (HH) monitoring by direct observation is considered to be a reliable method, it is labor intensive. The purpose of this study was to confirm regular HH use by collecting and analyzing HH data for Web- and mobile-application-based HH monitoring, as well as to establish a nationwide HH monitoring system. Users evaluated the Web-based report as being useful with regard to ease of access and use, data collection and analysis, and especially data comparison with other hospitals. Mobile applications were considered more difficult to use than the Web-based system.
Using Dependency Information to Enhance Attention Mechanism for Aspect-Based Sentiment Analysis

Attention mechanism has been justified beneficial to aspect-based sentiment analysis (ABSA). In recent years there arise some research interests to implement the attention mechanism based on dependency relations. However, the disadvantages lie in that the dependency trees must be obtained beforehand and are affected by error propagation problem. Inspired by the finding that the calculation of the attention mechanism is actually a part of the graph-based dependency parsing, we design a new approach to transfer dependency knowledge to ABSA in a multi-task learning manner. We simultaneously train an attention-based LSTM model for ABSA and a graph-based model for dependency parsing. This transfer can alleviate the inadequacy of network training caused by the shortage of sufficient training data. A series of experiments on SemEval 2014 restaurant and laptop datasets indicate that our model can gain considerable benefits from dependency knowledge and obtain comparable performance with the state-of-the-art models which have complex network structures.
MGA-TSP: modernised genetic algorithm for the travelling salesman problem

This paper proposes a new enhanced algorithm called modernised genetic algorithm for solving the travelling salesman problem (MGA-TSP). Recently, the most successful evolutionary algorithm used for TSP problem, is GA algorithm. The main obstacles for GA is building its initial population. Therefore, in this paper, three neighbourhood structures (inverse, insert, and swap) along with 2-opt is utilised to build strong initial population. Additionally, the main operators (i.e., crossover and mutation) of GA during the generation process are also enhanced for TSP. Therefore, powerful crossover operator called EAX is utilised in the proposed MGA-TSP to enhance its convergence. For validation purpose, we used TSP datasets, range from 150 to 33,810 cities. Initially, the impact of each neighbouring structure on the performance of MGA-TSP is studied. In conclusion, MGA-TSP achieved the best results. For comparative evaluation. MGA-TSP is able to outperform six comparative methods in almost all TSP instances used.
Software tools for teaching numerical series at the university level

The Mathematical Analysis I course is part of the curriculum of all engineering careers offered at the Santa Fe Regional Faculty of the National Technological University (UTN-FRSF). Its minimum content includes the topic of numerical series and their convergence, concepts that, as it has been detected, are difficult for students to understand. This work describes a didactic experience mediated by technologies. The results obtained allow us to conclude that the software tools used not only contributed to improve the learning of the subject, but also favored the development of mathematical competencies and higher order thinking skills in university students.
The Formation Of Metasubject Skills Of Students In The Information Educational Space

The article discusses the methodological issues of targeted inclusion of the potential of the information space in the educational process. The understanding of the information educational space as an environment for the formation of meta-subject skills of students is substantiated. The information educational space is defined as the object world, which is created and filled with human by various objects and processes. This world is directly or indirectly connected with the sphere of education. He is the subject of human activity. This activity includes: the human perception of these objects, actions with them, the impact on them. The key principles of formation of meta-subject skills of studying youth by means of information educational space are defined. Among these principles are the following: the humanization and democratization of strategies and models of work of young people in the information educational space; adequacy and objectivity - the compliance of the educational and cognitive activity of students in the digital information space with the requirements of educational standards and the and sociocultural level of society; the combination of the individualized nature of learning and the unity of requirements for the class, group of students; regularity and systematicity, diversity of the organization of the work of students in the digital information space; comprehensiveness and effectiveness of training - the focus of the organization of search and cognitive activity of students on their personal development and etc.
A Novel Query Language for Data Extraction from Social Networks

Online Social Networks (OSNs) represent an important source of information since they manage a huge amount of data that can be used in many different contexts. Moreover, many people create and manage more than one social profile in the different available OSNs. The combination and the extraction of the set of data from contained in OSNs can produce a huge amount of additional information regarding both a single person and the overall society. Consequently, the data extraction from multiple social networks is a topic of growing interest. There are many techniques and technologies for data extraction from a single OSN, but there is a lack of simple query languages which can be used by programmers to retrieve data, correlate resources and integrate results from multiple OSNs. This work describes a novel query language for data extraction from multiple OSNs and the related supporting tool to edit and validate queries. With respect to existing languages, the designed language is general enough to include the variety of resources managed by the different OSNs. Moreover, thanks to the support of the editing environment, the language syntax can be customised by programmers to express searching criteria that are specific for a social network.
A Comparison Review based on Classifiers and Regression Models for the Investigation of Flash Floods

Several regions of the world have been affected by one of the natural disasters named as flash floods.Many villagers who live near stream or dam, they suffer a lot in terms of property, cattle and human lives loss.Conventional early warning systems are not up to the mark for the early warning announcements.Diversified approaches have been carried out for the identification of flash floods with less false alarm rate.Forecasting approaches includes some errors and ambiguity due to the incompetent processing algorithms and measurement readings.Process variables like stream flow, water level, water color, precipitation velocity, wind speed, wave's pattern and cloud to ground (CG) flashes have been measured for the robust identification of flash floods.A vibrant competent algorithm would be required for the investigation of flash floods with less false alarm rate.In this research paper classifiers have been applied on the collected data set so that any researcher could easily know that which classifier is competent and can be further enhanced by combining it with other algorithms.A novel comprehensive parametric comparison has been performed to investigate the classification accuracy for the robust classification of false alarms.For the better accuracy more than one process variables have been measured but still contained some false alarm.Appropriate combination of sensor was integrated to increase the accuracy in results as multi-modal sensing device has been designed to collect the data.Linear discriminant analysis, logistic regression, quadratic support vector machine, knearest neighbor and Ensemble bagged tree have been applied to the collected data set for the data classification.Results have been obtained in the MATLAB and discussed in detail in the research paper.The worst accuracy of the classification (62%) has been achieved by the coarse k-NN classifier that means coarse k-NN produced 38% false negative rate that is not acceptable in the case of forecasting.Ensemble bagged trees produced best classification results as it achieved 99 % accuracy and 1% error rate.Furthermore, according to the comprehensive parametric comparison of regression models Quadratic SVM found to be the worst with mean square error of 0.5551 and time elapsed 13.159 seconds.On the other hand, Exponential Gaussian process regression performed better than the other existing approaches with the minimum root mean squared error of 0.0002 and prediction speed of 35000 observations per second.
Proof Irrelevance in Type-Theoretical Semantics

Type theories have been used as foundational languages for formal semantics. Under the propositions-as-types principle, most modern type systems have explicit proof objects which, however, cause problems in obtaining correct identity criteria in semantics. Therefore, it has been proposed that some principle of proof irrelevance should be enforced in order for a type theory to be an adequate semantic language. This paper investigates how proof irrelevance can be enforced, particularly in predicative type systems. In an impredicative type theory such as UTT, proof irrelevance can be imposed directly since the type Prop in such a type theory represents the totality of logical propositions and helps to distinguish propositions from other types. In a predicative type theory, however, such a simple approach would not work; for example, in Martin-Löf’s type theory (MLTT), propositions and types are identified and, hence, proof irrelevance would have implied the collapse of all types. We propose that Martin-Löf’s type theory should be extended with h-logic, as proposed by Veovodsky and studied in the HoTT project, where proof irrelevance is built-in in the notion of logical proposition. This amounts to MLTT\(_h\), a predicative type system that can be adequately employed for formal semantics.
Investigating Factors Affecting the Business Management of Saudi Food Industry by SMART-PLS, Measurement, and Structural Equation Models: Moderating Role of Knowledge Management

The purpose of the study is to investigate the influential factors for organizational performance at Saudi food companies. The study investigated the relationship between information system usage, their efficiency and effectiveness, top management support and technical resources as determinants of performance of firms. The moderating role of knowledge management was investigated between the relationships of constructs. The study examined the relationship between constructs and examined moderating effect. The data was collected through questionnaire from Saudi food companies. The data was analyzed by using SMART-PLS, measurement model and structural equation model was used for analyzing data and determine the relationships. The study found positive significant relationship between direct links, but moderating effect between top management support and knowledge management was rejected on the basis of statistical indicators. The study explains the relationships for determination of organizational performance, the study suggests to invest on information system, encourage support from top management and technical resources. Direct effect of knowledge management and moderating role is also explained in the study which serves as contribution in the body of knowledge.
Research on DFF-TopK algorithm based on dynamic feature selection

The time series of water level are affected by rainfall, temperature, upstream and downstream nodes and other factors, which have time fluctuation and spatial complexity, and the interaction between nodes will lead to the uncertainty of the prediction effect. Existing time series prediction algorithms require complex data preprocessing and dynamic feature attribute changes are not supported in these. Based on the above problems, a Dynamic feature-filtering algorithm DFF-TopK (Dynamic Feature filter-TopK) was proposed to reduce the degree of fusion of prior knowledge and support the change of dynamic feature attributes. The algorithm firstly established the initial random forest classifier by directly using the data of all existing features, sorted according to the importance of features. Subsequently, the mapped features are set up as priority queues, and K features with higher priority are selected as input items. When the priority queue length is determined, the importance of input data features within a certain period will be dynamically adjusted along with the priority queue. Furthermore, the influence degree of the upstream and downstream nodes' opening and closing or the dry season and rainy season will be dynamically recognized with the algorithm, which reduces the time complexity and solves the uncertainty caused by a large number of characteristic stacking, and avoids the influence of the traditional prior knowledge division on the results. Compared with the existing global static RF and gradient lifting algorithm DS-TopK, the experiments show that the algorithm has a greater improvement in time complexity and prediction accuracy, which verifies the effectiveness of the algorithm.
Multi-Grained Cascade AdaBoost Extreme Learning Machine for Feature Representation

Extreme learning machine (ELM) has been well recognized for characteristics such as less training parameters, fast training speed and strong generalization ability. Due to its high efficiency, researchers have embedded ELMs into deep learning frameworks to address the problems of high time-consumption and computational complexities that are encountered in the traditional deep neural networks. However, existing ELM-based deep learning algorithms usually neglect the spatial relationship of original data. In this paper, we propose a multi-grained cascade AdaBoost based weighted ELM algorithm (gcAWELM) for feature representation. We use AdaBoost based weighted ELM as a basic module to construct cascade structure for feature learning. Different ensemble ELMs trend to extract varied features. Moreover, multi-grained scanning is employed to exploit the spatial structure of the original data. The gcAWELM can determine the number of cascade levels adaptively and has simpler structure and fewer parameters compared with the traditional deep models. The results on image datasets with different scales show that the gcAWELM can achieve competitive performance for different learning tasks even with the same parameter settings.
STAD: Stack Trace Based Automatic Software Misconfiguration Diagnosis via Value Dependency Graph

Configurable software allows users to customize software behaviors through configurations. However, software misconfigurations that lead to the hard-to-diagnose system crash failures could inflict enormous harm to users and should be diagnosed with a high priority. To address this problem, we present a systematic approach (and its tool implementation, called STAD) to diagnosing misconfigurations based on static code analysis. Our approach analyzes the value dependency between variables obtained by exploring the stack trace, generates the value dependency graph (VDG), recommends the root cause of a misconfiguration via the VDG, and utilizes the correlation between configuration options to improve our recommendation results. There are two advantages compared with existing approaches: STAD does not require software crash reproduction, and users do not need to provide configuration options and their option read points (i.e. the statements that access the values of configuration options). We evaluated STAD on 8 misconfigurations from JChord built on Java. STAD can successfully diagnose all misconfigurations with less average number of false positives compared with existing approaches. In addition, STAD runs in less than one minute for each misconfiguration, making debugging more efficient.
Text Classification using Gated Fusion of n-gram Features and Semantic Features

We introduce a novel method for text classification based on gated fusion of n-gram features and semantic features of the text. The parallel CNN network captures the n-gram relation between the words based on the filter size, primarily short distance multi-word relations. Whereas for semantic relation-ship, universal sentence encoder or BiLSTM is used. Gated fusion is used to combine n-gram and semantic features. The model is evaluated on 4 commonly used benchmark datasets (MR, TREC, AG-News and SUBJ), which includes sentiment analysis and question classification. The proposed method is able to surpass the existing state-of-the-art DNN architectures for text classification on these datasets.
Illocutionary Speech Acts on Tweets Posted by Public Figures: Pragmatics Study

The title of this research is
Formal Qualitative Spatial Augmentation of the Simple Feature Access Model

The need to share and integrate heterogeneous geospatial data has resulted in the development of geospatial data standards such as the OGC/ISO standard Simple Feature Access (SFA), that standardize operations and simple topological and mereotopological relations over various geometric features such as points, line segments, polylines, polygons, and polyhedral surfaces. While SFA's supplied relations enable qualitative querying over the geometric features, the relations' semantics are not formalized. This lack of formalization prevents further automated reasoning - apart from simple querying - with the geometric data, either in isolation or in conjunction with external purely qualitative information as one might extract from textual sources, such as social media. To enable joint qualitative reasoning over geometric and qualitative spatial information, this work formalizes the semantics of SFA's geometric features and mereotopological relations by defining or restricting them in terms of the spatial entity types and relations provided by CODIB, a first-order logical theory from an existing logical formalization of multidimensional qualitative space.
Expert System for Diagnosing Types of Diseases in Human Intestine Organs Using the Certainty Factor Method

Intestine is one of the organs of the digestive system in the human body which is shaped like pipes and act as gatekeeper food system for our bodies. Many people do not noticed his intestines health because they are too busy with his activity, or lazy to go to the doctor, even when hospital were many visitors there is also felt a lot of to waste time to queue up because they want to get their turn to be checked and a lot of costs to be incurred. This research discusses about creating expert system to diagnose intestinal diseases using certainty factor method. Applications developed with Visual Basic programming language with MySQL as its database. The results of this research able to do early diagnosis of the symptoms that is felt system users, and provide diagnostic results such as type of disease suffered, prevention and its information.