Miracle-dz/UniFAR-FTUs huggingface dataset:
0 The Joint Effect of Narrative Structure , Medium Interactivity , and Readability on Investors ' Investment Decisions

We conduct an experiment to investigate how narrative structure (the extent to which good news is dispersed), the interactivity of disclosure medium (interactive versus non-interactive), and readability (high versus low) jointly influence investors’ investment decisions. We find that a disclosure with good news that is more dispersed leads to a higher investment amount. This narrative-structure effect is evident when a disclosure is easy to read and non-interactive or when it is hard to read and interactive. This effect disappears when a disclosure is hard to read and noninteractive or when it is easy to read and interactive. Our study extends accounting literature by showing how interactivity and readability moderate the narrative-structure effect and how readability moderates the interactivity effect. Our findings suggest that managers’ discretions over narrative structure should be constrained and that high readability is essential for interactivity to benefit investors.
Research Progress on Surface Modification of Graphene

Graphene is a kind of two-dimensional carbon nanomaterial with excellent physical and chemical properties. The applications of graphene covered batteries, sensors, supercapacitors and composites. However, the strong π-π interaction between the graphene sheets and the high specific surface energy limit its excellent performance, because of the strong chemical inertness and aggregate. Therefore, graphene must be appropriately modified to promote its dispersion in a solvent or matrix resin, expanding the application. The surface modification methods of graphene are generally classified into chemical modification (covalent bond modification), physical modification (non-covalent bond modification), and element doping modification. In this paper, the preparation methods of graphene are reviewed from these three modification methods, and it prospected the future development.
Dynamic inverse problem and the practical computation procedure for the solution

As a new framework in EEG study we propose ’dynamic’ inverse problem where dynamical constraints for a solution is considered. This is an extension of instantaneous inverse problem which many researchers have been studying to obtain a solution with good localization ability. We introduce a model for solving the problem, what we call ”Dynamic LORETA”. The practical estimation procedure is also introduced. We conﬁrm that the lack of temporal information gives a spurious solution in the simulation study. The analysis of real data shows the possibility of obtaining the dynamical information from the estimated model which is a key point to our success.
DR-QPO: DISCRETE RANK BASED QUERY PATTERN OPTIMIZATION TOWARDS PARALLEL QUERY PLANNING AND EXECUTION FOR DISTRIBUTE TRIPLE STORES

This manuscript proposed and explored a novel strategy for query pattern optimization towards parallel query planning and execution in Distributed RDF environments. The critical objective of the proposal is to optimize the query patterns from the query chains initiated to execute parallel in distributed RDF environment, which is unique regard to the earlier contributions related to parallel query planning and execution strategies found in contemporary literature. All of these existing models aimed to notify the query patterns from the given query chain, which are less significant to optimize the parallel process of the query patterns that discovered from multiple query chains submitted in parallel in distributed environment (such as cloud computing) to query the distributed triple stores. In order to this, the Discrete Rank based Query Pattern Optimization (DR-QPO) strategy is proposed. The DR-QPO optimizes the query patterns from multiple query chains initiated in parallel. A novel scale called Discrete Rank Consistence Score (DRDCS) defined, which uses the order of other metrics query pattern occurrence count, search space utilization, and access cost as input. The experiments conducted on the proposed model and other benchmark models found in contemporary literature. The results obtained from the experimental study evincing that the proposed model is significant and robust to optimize the query patterns in order to execute distribute query chains in parallel. The comparative analysis of the results obtained from DR-QPO and other contemporary models performed using ANOVA standards like t-test, Wilcoxon signed rank test.
ANTIOXIDANT EFFECT OF TRIPHALA:CRITICAL REVIEW

Triphala is a popular polyherbal drug, which has been used to treat long list of diseases in the traditional systems from the ancient times. Acharya Charaka mentioned in his text as daily consumption of the triphala for a period of one year, is act like rasayana, makes a person live for hundred years. Triphala is a composite mixture of three herbs Amalaki (Emblica officinalis), Haritaki (Terminalia chebula) and Vibhitaki (Terminalia belerica) also known as the ‘three myrobalans’. Emblica officinalis Gaertn belongs to Euphorbiaceae, and Terminalia chebula Retz. Terminalia bellerica belongs to Combretaceae family. The generic name ‘Terminalia’ comes from Latin word ‘terminus’ or ‘terminalis’ (ending), and refers to the habit of the leaves being crowded or borne on the tips of the shoots. Throughout the world, there are lot of studies carried out on this, well established the knowledge and documented. Triphala is rich in Vitamin C, gallic acid, ellagic acid, chebulic acid, bellericanin, β-sitosterol and Flavonoids etc and a potent laxative, immumo modulator, antioxidant, antimicrobial, traditionally been used in eye diseases, stress, arthritis, colon diseases, etc. Present paper deals with antioxidant effect of individual plants and its combination (Triphala).
An Investigation into the Nature and Extent of Poor Performance of Girls in Mathematics in Chimanimani District in Zimbabwe’s Manicaland Province

The aim of this study was to investigate the nature and extent of poor performance of girls in mathematics in Chimanimani District in Zimbabwe‟s Manicaland Province. The population comprised all the teachers and pupils in the districts‟ secondary schools. The sample was made up of 50 forms one to four female pupils and thirty (30) teachers. The study employed the quantitative methodology and adopted the descriptive survey design. All the information was collected through a questionnaire. The study revealed that students performed badly in mathematics particularly in geometry and trigometry. A multiplicity of variables were responsible for the poor performance of girls in mathematics, these include phobia for mathematics, shortage of textbooks, poor methods of lesson delivery and large class sizes. The study recommends that more attention be paid to recruiting of teachers with proper qualifications to guide pupils on mathematics. Mathematics should be made a compulsory for all girls in secondary schools. Parental involvement, regular assessments tests, refresher courses and workshops for teachers‟ provision of adequate resources and career counseling of the girls should be prioritized in the schools.
Elastic Scaling in VectorH

Cloud infrastructures allow to dynamically adapt to work-load changes by provisioning additional resources or de-provisioning resources to reduce costs. This offers also opportunities for scalable distributed data management. However, elastic scaling in databases requires to migrate or even repartition data. In this work, we present an approach implemented in Actian’s MPP solution VectorH that speeds up the elastic resizing process by minimizing partition reassignments while still achieving load balancing. Moreover, we describe a buffer matching and pre-filling technique to further increase performance after the resize step. The experimental evaluation shows that our solution significantly outperforms the non-elastic way of scaling using a system restart by a factor of 2 up to 4 and reduces downtimes during resizing to less than one minute.
AGE DIFFERENCES IN RESPONSES TO MARKETING COMMUNICATION TECHNIQUES USED IN ONLINE SOCIAL NETWORKS

In order to demonstrate that there are age differences in the way online consumers react to online marketing communication techniques, the study builds on a previously tested and validated empirical model showing the influence of online marketing communication via social networks on behavioral intentions, by continuing in a structural equation modeling approach. Significant differences between users of different age categories are found and implications for online communication practitioners are discussed, with strategic proposals stemming from these results. The study addresses the manner in which potential consumers of different ages react to and examine online social media marketing communication efforts, and how their perceptions influence various intentions. By drawing from theories of consumer behavior, a previously confirmed model for online user behavior in response to online marketing messages is tested for each age group. The results demonstrate that direct and positive links between the user perceptions of online marketing communication, and direct and positive links between users’ attitudes towards online communication and their intentions vary in strength with different age groups. Conclusions also feature strategic communication proposals, based on the findings.
SlicerVR for Medical Intervention Training and Planning in Immersive Virtual Reality

Virtual reality (VR) provides immersive visualization that has proved to be useful in a variety of medical applications. Currently, however, no free open-source software platform exists that would provide comprehensive support for translational clinical researchers in prototyping experimental VR scenarios in training, planning or guiding medical interventions. By integrating VR functions in 3D Slicer, an established medical image analysis and visualization platform, SlicerVR enables virtual reality experience by a single click. It provides functions to navigate and manipulate the virtual scene, as well as various settings to abate the feeling of motion sickness. SlicerVR allows for shared collaborative VR experience both locally and remotely. We present illustrative scenarios created with SlicerVR in a wide spectrum of applications, including echocardiography, neurosurgery, spine surgery, brachytherapy, intervention training and personalized patient education. SlicerVR is freely available under BSD type license as an extension to 3D Slicer and it has been downloaded over 7,800 times at the time of writing this article.
Automated Classification of Radiographic Knee Osteoarthritis Severity Using Deep Neural Networks.

Purpose To develop an automated model for staging knee osteoarthritis severity from radiographs and to compare its performance to that of musculoskeletal radiologists. Materials and Methods Radiographs from the Osteoarthritis Initiative staged by a radiologist committee using the Kellgren-Lawrence (KL) system were used. Before using the images as input to a convolutional neural network model, they were standardized and augmented automatically. The model was trained with 32 116 images, tuned with 4074 images, evaluated with a 4090-image test set, and compared to two individual radiologists using a 50-image test subset. Saliency maps were generated to reveal features used by the model to determine KL grades. Results With committee scores used as ground truth, the model had an average F1 score of 0.70 and an accuracy of 0.71 for the full test set. For the 50-image subset, the best individual radiologist had an average F1 score of 0.60 and an accuracy of 0.60; the model had an average F1 score of 0.64 and an accuracy of 0.66. Cohen weighted κ between the committee and model was 0.86, comparable to intraexpert repeatability. Saliency maps identified sites of osteophyte formation as influential to predictions. Conclusion An end-to-end interpretable model that takes full radiographs as input and predicts KL scores with state-of-the-art accuracy, performs as well as musculoskeletal radiologists, and does not require manual image preprocessing was developed. Saliency maps suggest the model's predictions were based on clinically relevant information. Supplemental material is available for this article. © RSNA, 2020.
A MOBILE BASED APPLICATION ON ENVIRONMENTAL EDUCATION FOR PRIMARY SCHOOLCHILDREN IN MALAYSIA

The ever-rising environmental issues on industrial pollution and daily life activities are affecting our environment, and bringing negative implications globally. The Malaysian government has taken steps to overcome these problems through the implementation of 11th Malaysia Plan with focus to create green environment and sustainable nation. To do so, Malaysian government needs the cooperation and support from the society. An environmental caring society could only be established if the environmental caring habit is cultivated at early age. One of the methods is to nurture awareness on environmental friendly activities through early education. However, environmental education is still limited in the current syllabus of primary schools in Malaysia. The environmental-based contents are included in language textbooks, which with focus of educating the students on the languages and not on the importance of caring for the environment. Thus, teachers are experiencing insufficiency of appropriate teaching materials to deliver on environmental education. As one of the intervention and, to assist in teaching and learning of environmental education, a mobile based application incorporating basic environmental education topics has been developed. It is the first version of the prototype, “LOVE2GreenMY”, has been tested among Standard 4 primary schoolchildren from a few schools in Kuala Lumpur and Perak. The schoolchildren and teachers have provided positive feedbacks on the proposed solution, yet further improvements on the content and design will be carried out in the second version of the prototype. In the second version, greater scope of environmental topics and game-based activities will be included. LOVE2GreenMY aims to be the platform that educates environmental education, not only at schools but also at homes.
Development of a Dental Nutrition Attitudes, Beliefs, and Behaviors Scale for Low-income Women

Objective: Develop a valid and reliable scale to assess the attitudes, beliefs, and behaviors related to eating foods associated with the periodontal disease among low-income women in Texas. Study Design: This is a cross-sectional study. Methods: The study was conducted with 150 low-income women ages 18-50 years, residing in Central Texas. A 21-item Dental Nutrition Attitudes, Beliefs, and Behaviors Scale was developed. Content validity was assessed by a panel of experts. Construct validity was determined via Exploratory Factor Analysis; internal consistency of the scale was measured by Cronbach’s alpha. Results: The internal consistency for the final scale was 0.802, and the test-retest reliability was 0.99. Factor analysis generated three factors: Attitudes, Beliefs, and Behaviors, with an explained variance of 42.38. Conclusions: These results confirmed the validity and reliability of the new dental scale. This instrument may aid in the assessment of attitudes, beliefs, and behaviors regarding eating foods associated with periodontal disease in low-income women.
Relationship between Environmental Knowledge and Self Efficacy with Responsibility Environment Behavior

DOI: 10.21276/sb.2018. 4.4. 7 Abstract: The study is aimed at determining the relationship between environment knowledge and self-efficacy with responsibility environment behavior in the city of Gorontalo. The method used in this research is a survey with correlational techniques. This method describes the phenomena on the relationship between variables. This study consists of two independent variables, namely knowledge of the environment (X1) and self efficacy (X2) and one dependent variable that is responsibility environment behavior (Y). The result of the research is the relationship between knowledge of the environment with responsibility environment behavior. There is a relationship between self-efficacy with responsibility environment behavior. There is a relationship between environment knowledge and self-efficacy simultaneously with responsibility environment behavior. Based on the results of the research it can be concluded that responsibility environment behavior is depend on knowledge of the environment and self-efficacy both partial and simultaneous.
GARCH-control of covariance in dynamical estimation of inverse solutions

The problem of estimating unobserved states of high-dimensional dynamical systems poses an inverse problem, which can be solved by Kalman Filtering; in order to provide the model of the dynamics with more ﬂexibility with respect to space and time, we suggest to combine the concept of GARCH-control of covariance, well-known in econometrics, with Kalman Filtering. We formulate this algorithm for spatiotemporal systems governed by stochastic diﬀusion equations and present a numerical simulation designed to imitate the situation of the generation of electroencephalographic recordings from the human cortex
Understanding the Dynamics of Voting on Shareholder Proposals

The last two decades have seen an explosion of shareholder-sponsored proposals in US firms. This corporate governance mechanism allows the shareholder—the owner of the company—to initiate corporate action on various issues. It provides shareholders with a formal mechanism through which they can raise concerns about corporate governance, as well as the social and environmental performance of firms. Interestingly, beginning with the 1990s the increase in shareholder proposals submitted to a vote has been accompanied with a growth in the votes cast in favor of the proposals (Gillan and Starks, 2000; Thomas and Cotter, 2007). Another intriguing development is the emergence of institutional investors as major equity holders in financial markets. This new position has led them to switch from a traditional passive shareholder role towards a more active role. Since these investors’ holdings are often large, they cannot sell their shares in underperforming companies without driving the price down. Also, because the performance of portfolio managers in financial institutions is often compared to a benchmark, managers have an incentive to hold on to their portfolios. And this in turn increases their incentives to undertake management monitoring and control via the mechanism of shareholder proposals (shareholder engagement), instead of simply relying on the monitoring functions of the stock market itself or those of the takeover market. In this research project we aim at studying the time and industry dynamics of voting on shareholder-sponsored proposals on Environmental, Social and Governance (ESG) issues. Existing literature has mostly focused on examining voting determinants such as sponsor identity, the type of the ∗This PhD research project is supervised by Prof Sébastien Pouget (TSE).
Disseminating Results of Mining Ordinances and their Tweets by Android App Development

This paper focuses on how populations can get more aware, by the use of technology, more specifically, an app to understand how local ordinances are passed in a city, how these ordinances result in changing the city for the better and in extension, make it head towards becoming a Smart City or a leading Smart City The main goal of this project is to develop an Android-based Ordinance Tweet Mining App that shows how government ordinances contribute towards the making of a Smart City. In this project, we focus on Smart City Characteristics such as Smart Economy, Smart Mobility, Smart Governance, Smart Living, Smart Environment and Smart People. This app would be beneficial to various users such as environmental scientists, policy makers, city committees as well as the general public in becoming more aware of what committees to reach out to and different ways in contributing to making a region head towards a Smart City.
Exploring the limits of limited-angle computed tomography complemented with surface data

Computed tomography investigations are challenging for planar objects, such as printed circuit boards (PCB), microprocessors packaging, etc. A conventional scan with a full rotation around a fixed axis may be impossible for such samples due to potential collisions with the system’s components or due to the limited penetration capability of the source and the finite dynamic range of the detect or. Analytical reconstruction techniques are proved unsuitable given the incomplete projection data resulting from limited source trajectory coverage. Iterative algorithms with prior information offer a solution to this reconstruction problem. In this contribution, we demonstrate that, by incorporating surface data in the reconstruction, better contrast homogeneity and more reliable sample boundaries can be reconstructed. A maximum likelihood expectation maximization (MLEM) algorithm is employed in this work. The surface data of the object, which is preregistered to the reconstructed object, is added into the objective function of the reconstruction as prior information. The values of the pixels outside the object ’s surface, extracted from the surface scanning, are penalized during the iteration process of the optimization. The proposed algorithm is applied to the limited angle projection data of simulated benchmark phantoms. The contrast of the reconstructed image, and the rooted mean square error between the phantom and the reconstructed image, are calculated to evaluate the performance of the proposed algorithm. Compared to the reconstruction without integrating the surface data, the reconstruction with complementary data gives an 80% higher homogeneity of the contrast, which is important to distinguish different materials and resolve fine features.
LONG-TERM PROSPECTS FOR THE DEVELOPMENT ENERGY COMPLEX OF RUSSIA

The article discusses the long-term prospects for the development of the energy complex and its role in the Russian economy in the context of the restructuring of world energy markets and taking into account the draft Energy Strategy of the Russian Federation for the period up to 2035. Using the SCANER information complex, scenarios for the evolution of world energy markets and the corresponding scenarios for the development of the Russian economy and energy have been developed. The dynamics of domestic consumption of the main types of fuel and energy is analyzed taking into account energy conservation, as well as the effective size and direction of export of the main types of fuel. On this basis, the dynamics of the extraction and processing of the main types of fuel, the use of renewable energy resources and the production of electricity by different types of power plants were optimized, the sizes of capital investments necessary for this and the return on the dynamics of domestic prices for fuel and energy were determined. Corresponding to these scenarios, changes in the main macro-indicators of the contribution of the energy complex to the development of the Russian economy indicate the possibility of a relatively rapid reduction in its dependence on energy exports.
Memory Augmented Recurrent Neural Networks for Dense Video Captioning

Dense video captioning is a challenging computer vision task that involves effectively understanding long video sequences. In this work, we address this problem by augmenting recurrent neural network architectures with external memory. We propose a dense captioning model that incorporates external memory augmentation both to encode video and densely caption it. We demonstrate that recurrent video encoder and dense captioner networks augmented with external memory can be used to effectively encode frames based on the content of the entire video, as well as for generating dense cap-tions better than recurrent networks without external memory augmentation. We conduct experiments on the ActivityNet Captions and YouCook II datasets to demonstrate the potential of external memory augmentation.