Reviewerly/exHarmony huggingface dataset:
Bottom-up Approach of System Requirement Specification of Mobile Apps for shrimp farming in Shyamnagar of Bangladesh

The rural poor in Bangladesh typically lack access to information vital to their lives and livelihoods.The information could be disseminated via web, mobile app, Internet of Things, and ubiquitous networks, etc.The mobile is widely used in many countries for the fish farming.Though frozen food is one of the most important export products in Bangladesh which contains more than 80% of shrimp.The most shrimp cultivation area is the southern part in Bangladesh.Shrimp farming is pressurized by lack of proper information on diseases identification and its management.Mobile app is considered as information provider.But it is necessary to find out the system requirement specification to provide required information for the farmers.The survey conducted in Shyamnagar, Satkhira district of Bangladesh and total 40 female respondents were involved in interview.The research tried to find out the common problems and requirements that focused mobile-app development.It was found that disease (82.86%) and water quality (17.14%) is common problem.Again, the most common disease was White Spot Syndrome Virus and Yellow Head Virus Disease followed by Black Gill disease.The research found it would be better developing voice-based mobile apps with local language.
A Multi-criteria Approach for Fast and Outlier-aware Representative Selection from Manifolds

The problem of representative selection amounts to sampling few informative exemplars from large datasets. This paper presents MOSAIC, a novel representative selection approach from high-dimensional data that may exhibit non-linear structures. Resting upon a novel quadratic formulation, Our method advances a multi-criteria selection approach that maximizes the global representation power of the sampled subset, ensures diversity, and rejects disruptive information by effectively detecting outliers. Through theoretical analyses we characterize the obtained sketch and reveal that the sampled representatives maximize a well-defined notion of data coverage in a transformed space. In addition, we present a highly scalable randomized implementation of the proposed algorithm shown to bring about substantial speedups. MOSAIC's superiority in achieving the desired characteristics of a representative subset all at once while exhibiting remarkable robustness to various outlier types is demonstrated via extensive experiments conducted on both real and synthetic data with comparisons to state-of-the-art algorithms.
Reduction of Movie Piracy using an Automated Anti-piracy Screen Recording System: Anti-piracy Screen Recording System

Movie theft has a profound impact on the economy of a country and films have always been one major entertainment factor in our lives. Movies have a tremendous influence in the realm of entertainment, movie makers have always strived very hard to drive the impact on people in the form of motion picture. All the efforts go in vain when movies get pirated which also brings about huge financial loss to the owners, and curbing this has been one of the major problems in the film industry. The people involved in pirating movies capture the video using the portable recording devices and then upload it on internet sites. In this paper, a methodology has been proposed to overcome piracy thus increasing the attendance of the audience in theatres and as a result decreasing the economic losses occurred. In the proposed method an antipiracy screen recording system is designed. An invisible IR ray is made to fall behind the screen which sensitize recording gadgets and recede the image on the captured screen. Several experiments have been carried out on the designed system by increasing the number of IR transmitters and the system is tested for various ranges of frequency and distance. It is observed that the system is capable of distorting the camera on the recording devices, thus preventing movie piracy.
Library of the “Society for Mutual Assistance of Salesmen in Tomsk”: History and Key Performance Indicators (1894—1913)

Study of the history of particular libraries is the important trend of modern domestic and foreign research works, since it contributes to the solution of not only scientific, but also local history and education tasks. Due to the lack of complex research studies on the history of library of the “Society for Mutual Assistance of Salesmen in Tomsk”, this article for the first time considers in detail the activities of library for all twenty years — from its establishment to closure. The purpose of the article is to reconstruct the history of the Society’s library, including its structure, collections, functioning and future destiny. The author collected the data from a wide range of historical sources: the Charter and reports of the Society for the entire period of its existence, anniversary historical essays summarizing the results of 10—20 years of its activity, the printed catalogue of the library, the pre-revolutionary magazine “Bibliotekar” [Librarian]. Information contained in one of them is confirmed directly or indirectly in the other. All this makes it possible to characterize the source base as sufficiently complete and representative. The principles of historicism, objectivity and consistency formed the methodological basis of the research. The article uses the methods of comparative analysis, statistical, chronological and source studies. The article reveals the dependence of the amount of allocations for the library on the financing of other activities of the Society. For the first time, the author compiled the rating of writers among the library’s users, which was headed by both classic writers and authors who are almost forgotten today. The article reconstructs the repertoire of periodicals that were available in the collections. The obtained results reveal the close interrelation and interdependence of all indicators of the library’s activity, including the time of its operation, the amount of financing, the number of users, visits, titles of books and periodicals. This study expands the idea of public initiative in the arrangement of libraries in pre-revolutionary Siberia, the demand for these cultural institutions among the lower strata of Siberian society and their reader’s preferences.
Research on Fully Automatic 2D to 3D Method Based on Deep Learning

Aiming at the problem that the traditional 2D to 3D method has excessive dependence on manual operation and complicated input, this paper proposed a fully automatic 2D to 3D method based on deep learning. First, a frame sequence is obtained according to the original 2D video, and a depth map corresponding to each frame image is obtained by using a convolutional neural network; then the left and right views are respectively drawn by using the original frame and the corresponding depth map according to the improved two-view DIBR algorithm. Finally, the improved Criminisi algorithm is used to fill the holes in the left and right views and to synthesize the final 3D video. The experimental results show that this method can not only obtain the ideal effect of 2D to 3D, but also the PSNR and SSIM value of the virtual viewpoint images are improved compared with other algorithms, and greatly improve the automation of 2D to 3D.
A New Distributed Reinforcement Learning Approach for Multiagent Cooperation Using Team-mate Modeling and Joint Action Generalization

This paper focuses on the issue of distributed reinforcement learning (RL) for decision-making in cooperative multi-agent systems.Although this problem has been a topic of interest to many researchers, results obtained from these works aren't sufficient and several difficulties have not yet been resolved, such as, the curse of dimensionality and the multi-agent coordination problem.These issues are aggravated exponentially as the number of agents or states increases, resulting in large memory requirement, slowness in learning speed, coordination failure and even no system convergence.As a solution, a new distributed RL algorithm, called the ThMLA-JAG method, is proposed here.Its main idea is to decompose the coordination of all agents into several two-agent coordination and to use a team-mate model for managing other agents' experiences.Validation tests on a pursuit game show that the proposed method overcomes the aforementioned limitations and is a good alternative to RL methods when dealing with cooperative learning in dynamics environments while avoiding collisions with obstacles and other learners.
Prediction of Solar Resource and Photovoltaic Energy Production through the Generation of a Typical Meteorological Year and Meso-NH Simulations: Application to the South of Portugal

The goal of this work is the generation of solar resource and photovoltaic (PV) energy maps based on simulations of a Numerical Weather Prediction model (NWP) for a Typical Meteorological Year (TMY). This approach is applied to the South of Portugal. Sixteen years of observations were used to determine a TMY for the city of Évora, which is located near the center of the region, through the Sandia method and the Finkelstein-Schafer statistics. The NWP Meso-NH model, with the ecRad radiation scheme integrated, is used to simulate the TMY, allowing for the generation of solar resource and air temperature maps that are helpful in predicting PV potential, thus constituting a useful tool for planning and designing this type of solar systems.
An Interactive Approach Deployed for Rhinoplasty using Mixed Reality

Mixed reality is one of the trending technology. It provides an excellent platform to the users, as it includes good graphics and animations. Mixed reality provides many applications in gaming, education, healthcare etc. This domain, can be considered for medical purposes i.e., rhinoplasty, which is a surgical procedure used to correct the nose and other parts of the face [1]. Sometimes the result obtained after the surgery, is not convincing. This paper illustrates the method to overcome this problem by using Mixed Reality Toolkit(MRTK) over Unity gaming platform. Therefore the patient and the doctor can have discussion and edit according to the patient's convenience and further proceed to surgery.
Optimal exploitation of the resource in remote state preparation

Transmission efficiency (TE) of remote state preparation (RSP) with a shared quantum state and one bit of classical communication is considered. Following [B. Daki et al., Nat. Phys. 8, 666 (2012)], the encoding and decoding strategies of the protocol are restricted to the physically relevant classes of projective measurements and unitary operators, respectively. It is shown that contrary to the previous arguments, the quadratic fidelity as well as the linear fidelity could be a valid figure of merit to quantify the TE of RSP. Then, the TE of the protocol in terms of both linear and quadratic fidelities is evaluated in a fully optimized scenario which includes the maximization over the encoding parameters as well as a meaningful maximization over the decoding parameters. The results show that in this scenario, the TE scales with the sum of the two largest eigenvalues of the squared correlation matrix of the resource state that is zero only for product states. This approach successfully quantifies the performance of the protocol in terms of the resource state parameters and provides a means to compare the usefulness of any two resource states for RSP.
Digital inclusion programs: the case of the Basque Country

The progressive development of the Digital Age has given rise to a growing number of concepts related to individuals’ experiences with their digital literacy, from the level of competence or lack thereof conceptualized in the digital divide, to the very concept of digital citizenship, essential today for participation in democratic societies. This article analyses the different interpretations made of these concepts with a view to clarifying their appropriate use according to the situations to be studied, understood as an intellectualized evolution of the realities and experiences lived by individuals and society. Since the beginning of the millennium, several national and regional digital inclusion projects began, encouraged by the European policies that aimed to alleviate the already existing digital gap. In this sense, the KZgunea program, the Basque government’s digital inclusion project is analyzed as a paradigmatic case of the evolution of government programs and policies in favour of an adequate level of digital literacy that allows individuals to fully participate in all fields of today’s increasingly digitized society. This report is part of a PhD thesis that analyzes digital literacy as an instrument of empowerment and social inclusion, to improve employability, and as a key to the construction of digital citizenship in the 21st century. Keywords: digital citizenship; digital literacy; digital divide; digital competence; digital inclusion programs.
Anfis Based Thrust Estimation of a Small Rotary Wing Drone

Unmanned Aerial Vehicles (UAV) has an increasingly application for military and civilian fields. Currently, UAVs can perform many tasks such as search-rescue, surveillance by safely. UAVs are specifically designed for their using purpose. The designs of UAVs are an important and long process. It is necessary to evaluate many parameters in the thrust system design. Because of thrust system is the most important system of UAVs. Traditional thrust system design is a trial and error method that is costly and ineffective. In this study, we examined that uav which use brushless motor in thrust system. The force generated by the thrust system has been estimated by using the Adaptive Neuro-Fuzzy Inference System (ANFIS). In the ANFIS model, the thrust force estimation was made by using propeller and motor information. RCbenchmark 1580 model dynamometer was used to measure the accuracy of the ANFIS estimates. Mean square error (MSE) was used to compare test data and ANFIS model. Low MSE ratio shows that ANFIS model is near to real data.
A Systematic Analysis of Task Scheduling Algorithms in Cloud Computing

Today is an era of the fastest technology which is growing in every field such as medical, marketing, aerospace and high-level computing. Cloud computing is new area of research which is used in every IT industry. It is basically on demand of resources via Internet. Here, resource can be storage, server, networks, etc. Task scheduling is NP-complete problem, and it is a mechanism to allocate the tasks on available resources. So that it can be minimized the following parameters such as execution time, cost and maximized the utilization of resources. In this paper, we have surveyed various task scheduling algorithms with their brief description, scheduling parameter and tools used. Also, we have discussed various basic tasks scheduling models and scheduling attributes.
Using of Social Media in School Management: Experience of Ukraine and United States of America

The article presents the results of the analysis of using of social media in school management in Ukraine and United States of America. Social media is broadly defined as a lot of relatively inexpensive and widely available electronic instruments that allow any person to publish and receive information, collaborate and build relationships with other people. The authors of the article break up social media into social networks, blogs, content hosting. There are some constructive conclusions made in the article. The educational opportunities of social media among Ukrainian specialists are very underestimated. The value of social media as an instrument of the educational process is undeservedly belittled. Many educators treat them with neglect and skepticism, considering social media exclusively as an entertaining resource. Using of social media for educational purposes is perceived by American students, teachers, researchers as self-evident and inalienable function. The American didactic experience reveals that social media can be successfully used to arrange the work of the teaching staff and students, hold individual and collective consultations enhancing intellectual and creative potential of students.
Learning Memory-guided Normality for Anomaly Detection

We address the problem of anomaly detection, that is, detecting anomalous events in a video sequence. Anomaly detection methods based on convolutional neural networks (CNNs) typically leverage proxy tasks, such as reconstructing input video frames, to learn models describing normality without seeing anomalous samples at training time, and quantify the extent of abnormalities using the reconstruction error at test time. The main drawbacks of these approaches are that they do not consider the diversity of normal patterns explicitly, and the powerful representation capacity of CNNs allows to reconstruct abnormal video frames. To address this problem, we present an unsupervised learning approach to anomaly detection that considers the diversity of normal patterns explicitly, while lessening the representation capacity of CNNs. To this end, we propose to use a memory module with a new update scheme where items in the memory record prototypical patterns of normal data. We also present novel feature compactness and separateness losses to train the memory, boosting the discriminative power of both memory items and deeply learned features from normal data. Experimental results on standard benchmarks demonstrate the effectiveness and efficiency of our approach, which outperforms the state of the art.
A Mixed Information Source Approach for Biomedical Question Answering: MindLab at BioASQ 7B

This paper describes the participation of the MindLab research group in the BioASQ 2019 Challenge for task 7b, document retrieval and snippet retrieval. For document retrieval, Elastic Search was used for the initial document retrieval step with BM25 as a scoring function. In the second stage, the top 100 retrieved documents were re-ranked with several strategies to exploit embedding semantic similarity. For the snippets retrieval subtask, the proposed approach was based on textual and conceptual information similarity patterns that were combined into a feature matrix that was subsequently processed by a convolutional neural network architecture. Our approach reached the third and second positions for the document retrieval and snippet retrieval task respectively.
On the Validity of Bayesian Neural Networks for Uncertainty Estimation

The 27th AIAI Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2019) Galway, Ireland, 5-6 December 2019
Flow-Guided Temporal-Spatial Network for HEVC Compressed Video Quality Enhancement

In this paper, a flow-guided temporal-spatial network (FGTSN) is proposed to enhance the quality of HEVC compressed video. Specifically, we first employ a robust motion estimation subnet via trainable optical flow module to estimate the motion flow between the target frame and its adjacent frames, and these adjacent frames are pre-warped guided by the predicted motion flow. Then, a temporal encoder is proposed to fuse the related information between the target frame and its pre-warped frames. Finally, a quality enhancement subnet with multi-scale encoder-decoder structure is designed to generate high quality frame by training the network in a multi-supervised fashion. Experimental results show the superior performance of our proposed FGTSN method for the reconstruction quality of HEVC compressed frames, much better than the state-of-the-art quality enhancement methods. In addition, our FGTSN method can also effectively mitigate the quality fluctuation of adjacent frames.
Improving Audio Steganalysis Using Deep Residual Networks

In this paper, we propose an effective audio steganalysis scheme based on deep residual convolutional networks in the temporal domain. Firstly, considering the weak difference between cover and stego, a high pass filter is adopted in the proposed network which is used to calculate the residual map of the audio signal. Then, comparing with convolutional neural networks (CNNs) based audio steganalysis in recent studies, the deeper network structure and complicated convolutional modules are considered to capture the complex statistical characteristic of steganography. Finally, batch normalization layers and shortcut connections are applied to decrease the dangers of over-fitting and accelerate the convergence of back-propagation. In the experiments, we compared the proposed scheme with CNNs based and hand-crafted features based audio steganalysis methods to detect the various steganographic algorithms on speech and music audio clips respectively. The experimental results demonstrate that the proposed scheme is able to detect multiple state-of-the-art audio steganographic schemes with different payloads effectively and outperforms several recently proposed audio steganalysis methods.
Fast-MVSNet: Sparse-to-Dense Multi-View Stereo With Learned Propagation and Gauss-Newton Refinement

Almost all previous deep learning-based multi-view stereo (MVS) approaches focus on improving reconstruction quality. Besides quality, efficiency is also a desirable feature for MVS in real scenarios. Towards this end, this paper presents a Fast-MVSNet, a novel sparse-to-dense coarse-to-fine framework, for fast and accurate depth estimation in MVS. Specifically, in our Fast-MVSNet, we first construct a sparse cost volume for learning a sparse and high-resolution depth map. Then we leverage a small-scale convolutional neural network to encode the depth dependencies for pixels within a local region to densify the sparse high-resolution depth map. At last, a simple but efficient Gauss-Newton layer is proposed to further optimize the depth map. On one hand, the high-resolution depth map, the data-adaptive propagation method and the Gauss-Newton layer jointly guarantee the effectiveness of our method. On the other hand, all modules in our Fast-MVSNet are lightweight and thus guarantee the efficiency of our approach. Besides, our approach is also memory-friendly because of the sparse depth representation. Extensive experimental results show that our method is 5$\times$ and 14$\times$ faster than Point-MVSNet and R-MVSNet, respectively, while achieving comparable or even better results on the challenging Tanks and Temples dataset as well as the DTU dataset. Code is available at https://github.com/svip-lab/FastMVSNet.
Factors Limiting the Adoption of Cloud Computing in Teleradiology

Cloud-based medical data sharing continues to change the way healthcare is conducted in hospitals.It is significantly supporting the 'patient-centricity' trend in providing medical services, and provides clinical team-based care delivery, clinical research, and point-of-care access to demographic and medical information, regardless of the location of the patient and the medical practitioners.Despite these benefits of cloud-based medical data sharing, the adoption in healthcare seems rather limited mainly due to security concerns.The significant contribution of this paper focuses on the identification and comparison of technical security and organizational factors that aids limited adoption of Cloud in teleradiology.Data was collected from relevant healthcare practitioners through interview conducted over the telephone and supported by a form of open-ended questions.The analysis revealed that against popular opinion, organizational factors such as Economics and Adoption Costs, Cultural Resistance, and Legal Rules, were major limiting factors.Future research will focus on proposing a cloud medical data sharing maturity model, which would support healthcare organizations to benchmark, assess and eventually improve the related services.