Reviewerly/exHarmony huggingface dataset:
Creating Partnerships between Health Innovators and Librarians: A Story

What sets health sciences librarians apart from other academic librarians when it comes to partnering with health-focused innovators? Do health-related innovators have different information needs or space requirements? This chapter illustrates some of the major issues and topics health sciences librarians consider as they offer information services to entrepreneurs and innovators. Health sciences innovators must be aware of relevant policies and laws such as HIPAA, the Health Insurance Portability and Accountability Act of 1996. They also need to meet federal safety regulations required by the Food and Drug Administration; moreover, device materials must be biocompatible. Those developing therapeutic games and apps in this arena need to ensure their products are supported by current literature and scientific evidence. In many cases, these new technologies require clinical trials and testing to ensure their safety and efficacy. Health sciences librarians guide innovators to relevant resources, knowledge, and experts on these and other topics. This navigator role is extremely valuable to students, who may not understand the healthcare landscape and its processes. Additionally, librarians assist innovators with identifying dissemination venues for their scholarly output. They provide instruction and guidance on how to write and tailor conference proposals to meet specific professional association criteria. A retired health sciences library director shares her experiences. Tips and lessons learned are highlighted so others may gain an understanding of the unique information needs of health innovators.
Comfort degree of patients’ rehabilitation training based on DSmT theory

This paper aims to solve the problem that most rehabilitation robots lack to integrate their physiological functions and psychological states into closed controlling loop in rehabilitation training of stroke patients. The application of fusion technology of multi-electromechanical sensor signal and DSmT signal is used to achieve patients' comfort judgement. Mean value and variance are used to extract physiological information such as heart rate, temperature and skin resistance. Sensor information of fusion technology of rehabilitation robot based on classical DSmT theory can realize on-line diagnosis of multi-comfort degree of patients' coupling.
Semantic SLA for Selecting SAAS ERP

Abstract Service Level Agreement (SLA) is a contract between a service provider and a service consumer. It consists of quality of service (QoS) property and the business properties. In a business environment using Software as a Service (SaaS), SLA and QoS play an essential role in their promising service performance. In this case, the attributes of QoS specified in SLA should show the non-functional attributes of a web service; yet, there, so far, have not been any QoS statement in web service definition language (WSDL) file. This research proposes the selection of SaaS ERP based on the semantic SLA developed using ontology. Analytic Hierarchy Process (AHP) method is employed to establish the rank of a tenant’s SLA. Subsequently, to create the rank of SLA which has the same AHP scores, the weighted tree algorithm is used. The results show that the combined method (AHP-Ontology) can provide precision reaching at 0.792, better recall at 0.795 and better f-measure at 0.793. Also, the best accuracy result through this method can reach 86.23%, better than AHP-Euclidean method - only reaching the best accuracy level at 59.03%.
Binary Classification Only from Unlabeled Data by Iterative Unlabeled-unlabeled Classification

Unlabeled-unlabeled (UU) classification (du Plessis et al. 2013) allows us to train a binary classifier from two sets of unlabeled data with different class priors. In this paper, we go beyond this scenario and try to train a binary classifier only from a single set of unlabeled data. Our key idea is to iteratively perform UU classification: We initially split the original single unlabeled dataset into two disjoint datasets and perform UU classification. We then split the original unlabeled dataset in a different way based on the obtained classifier, perform UU classification, and repeat this process until convergence. We numerically show that the classification accuracy tends to be improved over iterations. Finally, we apply our iterative UU classification method to a realworld drowsiness prediction problem and demonstrate its usefulness.
Flood Disaster in Sutojayan Sub-district, Blitar Regency, 2004

This study aims to answer the question of what factors are caused the flood disaster in Sutojayan District in 2004, how the process of the flood occurred and how the government responded to the incident. The used of the historical method of descriptive writing with analytical models. The Data used the study are documents and other sources related to the subject matter. The results of study indicate the flood occurred in Sutojayan 2004 was the biggest flood Compared to previous floods. This flood was caused by prolonged rain, Topographic forms and the river which was unable to a accommodate flood water and mud from the mountains, due to the destruction of the Kepek forest. This flood of the give impacts on economic, social problems, and psychologys. This condition invites responses from various parties, especially the government who then takes action in order to recover repairing the damage to both infrastructure due to floods and anticipation so that no similar events will occur in the future. The conclusion of this study is that flood can be caused by both natural and human factors. Natural factors including prolonged rain, soil topography, etc., and human factors roomates are illegal logging results in damage to the forest, thereby reducing the water infiltration and Unable to withstand water and mud from the mountains.
Visual Analytics for Supporting Conflict Resolution in Large Railway Networks

Train operators are responsible for maintaining and following the schedule of large-scale railway transport systems. Disruptions to this schedule imply conflicts that occur when two trains are bound to use the same railway segment. It is upon the train operator to decide which train must go first to resolve the conflict. As the railway transport system is a large and complex network, the decision may have a high impact on the future schedule, further train delay, costs, and other performance indicators. Due to this complexity and the enormous amount of underlying data, machine learning models have proven to be useful. However, the automated models are not accessible to the train operators which results in a low trust in following their predictions. We propose a Visual Analytics solution for a decision support system to support the train operators in making an informed decision while providing access to the complex machine learning models. Different integrated, interactive views allow the train operator to explore the various impacts that a decision may have. Additionally, the user can compare various data-driven models which are structured by an experience-based model. We demonstrate a decision-making process in a use case highlighting how the different views are made use of by the train operator.
Designing an Intervention for Creating Awareness in Motorists About Vehicle Emission Consequences on Human Health

Exhaust emissions from motorized vehicles are not only harmful to the environment but also to human health. However, motorists are not necessarily aware of the adverse health effects resulting from their emissions. In this work, we use the health aspect as a primary motivation factor in the design of an intervention targeted at reducing exhaust emissions. Based on research into the problem domain and the target group, we propose a design for a behavior-change intervention consisting of an infrastructure of large public displays and a mobile application. In a design prototype, we incorporate two approaches, shaming and empowerment, designed to engage motorists with the intervention. An experimental evaluation of the prototype suggests that shaming can have a lot of potential in providing motivation for change, while empowerment is also needed inside the application for helping the drivers reduce their emissions by means of more efficient traveling. Based on the findings, we discuss the role of personal data in the intervention and outline possibilities for realizing the design as part of the built environment.
A discrete information coding matrix algorithm based on image features for tracking circulation of commodities

Abstract For fast‐consumed‐commodities industry, it is usually difficult for producers to track the commodities in their circulation process, especially, when their labels have been damaged. In this paper, we develop a novel method based on channel coding theory of forward error correction (FEC) to prevent the flooding of counterfeit products with a very low cost and easy implementation. This anti‐interference matrix information coding method has a strong ability to resist damage and high recovery rate, and can produce a large number of nonrepeated codes in terms of different characteristics of demand, yielding a great enhancement of anti‐counterfeiting trace capacity. However, the process of reading information strongly relies on artificial means that requires a high cost and reduces the efficiency. For this reason, we propose an efficient approach for image feature extraction based on discrete information coding matrix. It takes the advantage of wearing perspective clipping method to obtain the revised image with printed matrix information and then extract the regions of interest (ROI) that carry the information about the coding matrix by working out its row and column coordinates with the center of gravity method. The matrix code recognition software is implemented. The simulation and experimental results show that the method is effective and robust.
Comparing the differences of using mobile devices for learning between Filipino schools and Chinese schools in the Philippines

With the fast development of technology, mobile devices offer tremendous opportunities for students. With this new and exciting era in education, students with different cultural background from different community may have different experiences and perceptions for learning with mobile devices. This study explored the differences of using mobile devices for learning between Filipino schools and Chinese schools in the Philippines. A total of 529 senior high school students from three Filipino schools and three Chinese schools in Metro Manila participated in this study. Results indicated that students from Chinese schools have a comparative advantage in terms of mobile technical infrastructures, while students from Filipino schools were more frequently users of mobile devices for learning activity. Although students from Filipino schools found greater challenges in using mobile devices for learning activity, they were also more optimistic toward the adoption of mobile devices in the formal learning environment compared with their counterparts from Chinese schools. Additionally, students from both schools were highly concerned about issues like personal data protection, cheats and cyber bullying. Findings in this study suggest that students' social cultural background, together with their previous experience need to be carefully considered in order to successfully adopt mobile devices in the local context.
The Enlightenment of Management Teaching Method of Queensland University of Technology

After China's entry into WTO, the importance and urgency of developing and promoting bilingual teaching in colleges and universities is becoming increasingly prominent.In order to promote the reform of bilingual teaching in colleges and universities in Liaoning Province, this paper uses case law, taking the Advanced Higher Education Teaching practice of Queensland University of Technology as a case, to provide reference for English teaching in various colleges and universities in Liaoning province.Among them, the advanced teaching method of this school includes dozens of kinds, all from the teaching practice of colleges and universities, which has certain research value for the innovation of teaching methods in English instruction in colleges and universities in our province during the "Thirteen-Five" period.Practice has proved that the reform of teaching method is the key and is important to promote the reform of bilingual teaching in colleges and universities in Liaoning province.
MOBILE LEARNING: A (GOOD) TEACHING AND CONTROL AID?

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 1567-1575ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.0474Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
Dynamic Path Planning Algorithm Based on an Optimization Model

Unmanned surface vessels (USVs) have been extensively employed in the past few decades. Traditional path planning algorithm assumes that obstacles remain stationary, and the USV dynamic constraints is not taken into account. In this paper, a path planning algorithm that considers time dimension and the dynamic performance of a USV is proposed. The algorithm abstracts the kinematics constraints and obstacle distance constraints into nonlinear constraints and abstracts the path planning problem into a nonlinear optimization model. The nonlinear model is approximated to a least squares model to improve the speed of solution. The experimental results show that this algorithm is reasonable and advantageous.
Cross-Domain Change Object Detection Using Generative Adversarial Networks

Image change detection is a fundamental problem for robotic map maintenance and long-term map learning. Local feature-based image comparison is one of the most basic schemes for addressing this problem. However, the local-feature approach encounters difficulties when the query and reference images involve different domains (e.g., time of the day, weather, season). In this paper, we address the local-feature approach from the novel perspective of object-level region features. This study is inspired by the recent success of object-level region features in cross-domain visual place recognition (CD-VPR). Unlike the previous contributions of the CD-VPR task, in the cross-domain change detection (CD-CD) tasks, we consider matching a small part (i.e., the change) of the scene and not the entire image, which is considerably more demanding. To address this issue, we explore the use of two independent object proposal techniques: supervised object proposal (e.g., YOLO) and unsupervised object proposal (e.g., BING). We combine these techniques and compute appearance features of their arbitrarily shaped objects by aggregating local features from a deep convolutional neural network (DCN). Experiments using a publicly available cross-season NCLT dataset validate the efficacy of the proposed approach.
Deep Counting Model Extensions with Segmentation for Person Detection

Applications like autonomous driving, surveillance, or any application that demands scene analysis requires object detection, semantic segmentation and instance segmentation. In this paper, we focus on the problem of detecting each instance of a specific category of objects, specifically persons. A novel method for object detection is proposed based on a deep counting model. The feature extractor of the deep counting model is extended with additional layers for segmenting specific instances. While the feature extractor of the deep counting model already focuses on the persons in the scene, the segmentation layers help to get a more accurate estimation of the foreground with persons and the instance segmentation is able to estimate separate instances of persons. Our proposed method outperforms other methods on the CUHK08 dataset with an Average Miss Rate (AMR) of 14% and on the PETS09 dataset with an AMR of 41%.
Jargon Content and Readability of Health Service Policy Documents

Background/Aims Readability of written material is essential for effective communication. The readability of 40 healthcare policies published by the NHS or the Health Service Executive were compared and contrasted against 40 newspaper articles regarding healthcare from the Irish Times or the Irish Mirror. Methods Readability was measured using the Flesch reading ease score and the standardised measure of gobbledygook grade. Findings Flesch and the standardised measure of gobbledygook were significantly worse in health policies than in newspaper articles: none of the healthcare policies met a minimum standard of a Flesch score of 50 or more (P&lt;0.0001); 60% of health policies and no newspaper article had a standardised measure of gobbledygook grade of 13 or more, suggesting a need for a university level education for comprehension (P&lt;0.0001). Conclusions The readability of healthcare policies in Britain and Ireland is poor. A stronger emphasis on the use of plain language is needed.
Scalable text semantic clustering around topics

Detection of topics in Natural Language text collections is an important step towards flexible automated text handling, for tasks like text translation, summarization, etc. In the current dominant paradigm to topic modeling, topics are represented as probability distributions of terms. Although su ch models are theoretically sound, their high computational complexity makes them difficult to use in very large scale collections. In this work we propose an alternative topic modeling paradigm based on a simpler representation of topics as overlapping clusters of semantically similar documents, that is able to take advantage of highly-scalable clustering algorithms. Our Query-based Topic Modeling framework (QTM) is an information-theoretic method that assumes the existence of a “golden” set of queries that can capture most of the semantic information of the collection and produce models with maximum “semantic coherence”. QTM was designed with scalability in mind and was executed in parallel using a Map-Reduce implementation; further, we show complexity measures that support our scalability claims. Our experiments show that the QTM can produce models of comparable or even superior quality than those produced by state of the art probabilistic methods.
Atoms in Light: Semiclassical Theory

Abstract The atom-field interaction is treated in semiclassical radiation theory, starting from the transformation from the minimal coupling Hamiltonian to the electric dipole form used extensively in quantum optics. The Heisenberg and density matrix approaches are developed and applied to two-state atoms, Bloch equations, Rabi oscillations, Maxwell-Bloch equations, and transition rates for absorption and emission. Einstein’s theory of blackbody radiation based on momentum fluctuations and dissipation is reviewed. The Einstein fluctuation formula is derived and used to introduce wave-particle duality, Hanbury Brown-Twiss correlations, and photon bunching.
An Adaptive SVD based De-Noising Filtering Scheme for parallel MRI

Magnetic Resonance Imaging (MRI) usually encounters noise during data acquisition and reconstruction process which not only contaminates the quality of reconstructed images but also leads to poor diagnostic interpretations in clinical setting.
Using Transfer Learning in an Ad Hoc Team

In a practical scenario, we have a myriad of robotic systems; single and multi agent, not operating under any standard communication protocols. This is eminent from a point of view where independent robots can come together to achieve goals as a team. This problem is well defined in the domain of Ad Hoc Teamwork(AHT) which strives for a MAS wherein agents are heterogeneous, independent in their own respect and, as a whole accomplish goals that may be above any individual's capability. A key aspect is transfer learning which allows on the fly addition of team members. This paper shares development in transfer learning in the field of Ad Hoc Teamwork.
Investigation of Sheltering Effect on Global Solar Radiation Data Measured by Weather Stations

In instalment of pyranometers at the weather stations for measuring global solar radiation, it often cannot avoid appearance of obstacles in their surroundings. Investigation of sheltering effect on measurement of global solar radiation is performed through introducing the shelter view factor. Evaluation of the shelter view factor is made by means of a fisheye-lens photograph together with the calculation method developed by Steyn (1980). Taitung weather station (TWS) is chosen for the study. The shelter view factor for the pyranometer installed at TWS is estimated to be 11.8%. Measurements of global solar radiation are conducted at a place where is located nearby TSW but with the condition of zero shelter view factor. Comparison of the data measured at these two sites indicates 4% - 25% deviations of global solar radiation observed in different months of 2017. It suggests a need of the correction to the sheltering effect in the measuring process of global solar radiation at the weather stations except those who are not subject to surrounding obstacles in the sky dome.
Improving Programmability and Efficiency of Large-Scale Graph Analytics for FPGA Platforms

Large-scale graph analytics has gained importance due to emergence of new applications in different contexts such as web, social networks, and computational biology. It is known that typical CPU/GPU implementations for sparse graph applications cannot efficiently utilize the available compute resources. In our previous work, we have shown that significant performance and energy efficiency improvements can be achieved using custom hardware accelerators for graph applications. On the other hand, designing application-specific hardware is expensive in terms of engineering, manufacturing, and maintenance costs. Since FPGAs are known to provide a good tradeoff between customizability and efficiency, several prominent vendors have started offering data-center solutions with FPGAs.
Dealing with Uncertainty in Binary Logistic Regression Fault-proneness Models

Background Binary Logistic Regression is widely used in Empirical Software Engineering to build estimation models, e.g., fault-proneness models, which estimate the probability that a given module is faulty, based on some measures of the module. Fault-proneness models are then used to build faultiness model, i.e., models that estimate whether a given module is faulty or non-faulty.
Research on Automatic Proofreading Method of Sensitive Information in Content Security

Aiming at the problem of automatic proofreading of sensitive information in mass text content, an automatic proofreading method based on the combination of rule and SVM (Support Vector Machine) is proposed. To classify sensitive information based on important sensitive information provided in the "Newly Prohibited Texts and Cautions in Xinhua News Reports"(newest revision) and related central and online texts.According to the different categories, the paper constructs the classification processing rule base, designs the corresponding rules automatic Processing algorithm, and realizes the sensitive information automatic proofreading, At the same time, using the SVM model to analyze the result of the rule processing with emotion, which greatly reduces the false alarm rate. The test result shows that the recall rate of method is 89.98%, the accuracy rate is 98.31%, and 100, 000 + text content is processed per second, which solves the key difficult problems in the practical engineering application.