Reviewerly/exHarmony huggingface dataset:
Mixing of quantum states under Markovian dissipation and coherent control

Given any two quantum states $\ensuremath{\rho}$ and $\ensuremath{\sigma}$ in Hilbert spaces of equal dimension satisfying the majorization condition $\ensuremath{\rho}\ensuremath{\succ}\ensuremath{\sigma}$, it is always possible to transform $\ensuremath{\rho}\ensuremath{\mapsto}\ensuremath{\sigma}$ by a unital quantum map. In fact, any such transformation can be achieved just by means of noisy operations, i.e., by access to maximally mixed ancillary states and unitary transformations that act jointly in the system-ancilla space. Here, we investigate the possible transitions between states (i.e., the induced preorder of states) when one restricts the unitary control to the quantum system alone and replaces the maximally mixed ancillas with a Markovian master equation, represented by a unital Lindbladian. As a main result, we find necessary and sufficient conditions for the Lindbladian dissipation to have the same converting power as that of noisy operations, i.e., any transformation $\ensuremath{\rho}\ensuremath{\mapsto}\ensuremath{\sigma}$ is possible if and only if $\ensuremath{\rho}\ensuremath{\succ}\ensuremath{\sigma}$.
India: Cyber censorship should be opposed – South Asia Citizens Web

t is sad that the Central government has enacted moved to censor Facebook posts, Skype conversations, monitor tweets, blogs, private photographs and documents stored online and track even mobile…
Moment Indonesian quake struck worshippers praying in mosque

Surveillance footage from an Indonesian mosque captured the terrifying moment a deadly earthquake struck just as worshippers were praying.
Preface of the "The Second Symposium on Information, Intelligent, Education and Engineering Application (IIEEA 2019)"

First Page
Context-aware Modeling for Spatio-temporal Data Transmitted from a Wireless Body Sensor Network

Context-aware systems must be interoperable and work across different platforms at any time and in any place. Context data collected from wireless body area networks (WBAN) may be heterogeneous and imperfect, which makes their design and implementation difficult. In this research, we introduce a model which takes the dynamic nature of a context-aware system into consideration. This model is constructed according to the four-dimensional objects approach and three-dimensional events for the data collected from a WBAN. In order to support mobility and reasoning on temporal data transmitted from WBAN, a hierarchical model based on ontology is presented. It supports the relationship between heterogeneous environments and reasoning on the context data for extracting higher-level knowledge. Location is considered a temporal attribute. To support temporal entity, reification method and Allen’s algebra relations are used. Using reification, new classes Time_slice and Time_Interval and new attributes ts_time_slice and ts_time_Interval are defined in context-aware ontology. Then the thirteen logic relations of Allen such as Equal, After, Before is added by OWL-Time ontology to the properties. Integration and consistency of context-aware ontology are checked by the Pellet reasoner. This hybrid context-aware ontology is evaluated by three experts using the FOCA method based on the Goal-Question-Metrics (GQM) approach. This evaluation methodology diagnoses the ontology numerically and decreases the subjectivity and dependency on the evaluator’s experience. The overall performance quality according to completeness, adaptability, conciseness, consistency, computational efficiency and clarity metrics is 0.9137.
Effect of context in swipe gesture-based continuous authentication on smartphones

This work investigates how context should be taken into account when performing continuous authentication of a smartphone user based on touchscreen and accelerometer readings extracted from swipe gestures. The study is conducted on the publicly available HMOG dataset consisting of 100 study subjects performing pre-defined reading and navigation tasks while sitting and walking. It is shown that context-specific models are needed for different smartphone usage and human activity scenarios to minimize authentication error. Also, the experimental results suggests that utilization of phone movement improves swipe gesture-based verification performance only when the user is moving.
A Natural Language Corpus of Common Grounding under Continuous and Partially-Observable Context

Common grounding is the process of creating, repairing and updating mutual understandings, which is a critical aspect of sophisticated human communication. However, traditional dialogue systems have limited capability of establishing common ground, and we also lack task formulations which introduce natural difficulty in terms of common grounding while enabling easy evaluation and analysis of complex models. In this paper, we propose a minimal dialogue task which requires advanced skills of common grounding under continuous and partially-observable context. Based on this task formulation, we collected a largescale dataset of 6,760 dialogues which fulfills essential requirements of natural language corpora. Our analysis of the dataset revealed important phenomena related to common grounding that need to be considered. Finally, we evaluate and analyze baseline neural models on a simple subtask that requires recognition of the created common ground. We show that simple baseline models perform decently but leave room for further improvement. Overall, we show that our proposed task will be a fundamental testbed where we can train, evaluate, and analyze dialogue system's ability for sophisticated common grounding.
Hierarchical Photo-Scene Encoder for Album Storytelling

In this paper, we propose a novel model with a hierarchical photo-scene encoder and a reconstructor for the task of album storytelling. The photo-scene encoder contains two sub-encoders, namely the photo and scene encoders, which are stacked together and behave hierarchically to fully exploit the structure information of the photos within an album. Specifically, the photo encoder generates semantic representation for each photo while exploiting temporal relationships among them. The scene encoder, relying on the obtained photo representations, is responsible for detecting the scene changes and generating scene representations. Subsequently, the decoder dynamically and attentively summarizes the encoded photo and scene representations to generate a sequence of album representations, based on which a story consisting of multiple coherent sentences is generated. In order to fully extract the useful semantic information from an album, a reconstructor is employed to reproduce the summarized album representations based on the hidden states of the decoder. The proposed model can be trained in an end-to-end manner, which results in an improved performance over the state-of-the-arts on the public visual storytelling (VIST) dataset. Ablation studies further demonstrate the effectiveness of the proposed hierarchical photo-scene encoder and reconstructor.
Deep Bayesian Optimization on Attributed Graphs

Attributed graphs, which contain rich contextual features beyond just network structure, are ubiquitous and have been observed to benefit various network analytics applications. Graph structure optimization, aiming to find the optimal graphs in terms of some specific measures, has become an effective computational tool in complex network analysis. However, traditional model-free methods suffer from the expensive computational cost of evaluating graphs; existing vectorial Bayesian optimization methods cannot be directly applied to attributed graphs and have the scalability issue due to the use of Gaussian processes (GPs). To bridge the gap, in this paper, we propose a novel scalable Deep Graph Bayesian Optimization (DGBO) method on attributed graphs. The proposed DGBO prevents the cubical complexity of the GPs by adopting a deep graph neural network to surrogate black-box functions, and can scale linearly with the number of observations. Intensive experiments are conducted on both artificial and real-world problems, including molecular discovery and urban road network design, and demonstrate the effectiveness of the DGBO compared with the state-of-the-art.
On the Expressiveness of Joining and Splitting

An ongoing theme of the work of Bernhard Steffen has been the bringing together of different components in a coordinated manner and with a unified language. This paper explores this approach applied to process calculi that account for coordination of different kinds of workflows. Coordination here extends binary interaction to also account for joining of multiple outputs into a single input, and splitting from a single output to multiple inputs. The results here formalise which process calculi can and cannot be encoded into one another, and thus which language has the required expressiveness for given workflow properties. The combination of with other features of interaction allows for the representation of many systems and workflows in an appropriate calculus.
Prevalence and seasonal trends of malaria at general hospital, Palanpur Banaskantha, Western part of India

Aim: Malaria is one of the known endemic disease in India.Malaria is transmitted by infected female anopheles mosquito vector transmitting mainly two Plasmodial species named P. falciparum and P. vivax.Prevalence of malaria is affected by atmospheric and socioeconomic condition of geographical area.Aim of the study is to know the prevalence of malaria and the seasonal trend of malaria at general hospital Palanpur, Banaskantha in western part of India.Material and Methods: This study was carried out at Central Laboratory, General Hospital, Palanpur, Banaskantha over period of July 2014 to June 2018.41660 samples from fever patients were collected.We used Microscopic examination for the diagnosis of malaria by preparing thick and thin smears from these 41660 samples and stained using field stain.Results: We had collected total 41660 samples of fever patients.Out of 41660 samples, 688 samples were detected positive for malaria.So prevalence of malaria in our hospital was 1.65%.Prevalence of P. Vivax, P. Falciparum and mixed (infection of P. Falciparum and P. Vivax at a same time) were found 77.90%, 21.80% and 0.30% respectively.Malaria was seen relatively higher in males and affecting 15-30 years of age group more.Most cases of malaria were found in months of September and October near the end of monsoon and post monsoon season. Conclusion:From our study we concluded that there was high prevalence of P. vivax than P. Falciparum infection.Maximum numbers of cases were after major rainfall, reported in month of September and October.Morbidity and mortality of malaria must be reduced by strengthening Malaria Surveillance, risk factor assessment during pre and postmonsoon period.Premonsoon preventive actions need strengthening and strict monitoring of all these actions are needed.
The strength of stillness

Abstract The article discusses the strength of stillness both at a narrative level related to Rosana’s appearance and at an aesthetic level related to the soundtrack. The quiet sound design communicates intensity and meaning, as does Rosana, who is largely ignored by the family but is an eminently strong character.
2D Image Deformation Based on Guaranteed Feature Correspondence and Mesh Mapping

Image deformation has ubiquitous usage in multimedia applications. It morphs one image into another through a seamless transition. Existing techniques either mainly focus on the correspondence mapping of interior features of the objects in two images, without considering object contours, or sketch contours manually, resulting in tedious work for users. Thus, we propose a 2D image deformation method, which extracts object contours automatically, considers both inner features and contours as constraints and preserves image features in terms of visual importance. Our method first automatically extracts the object contours in the source and target images and then allows users to sketch some interior features in both the images. Then, our method tessellates two images to generate two triangular meshes and builds a guaranteed bijective mesh mapping between them. We also prove the bijectivity of our mesh mapping and discuss its other desirable properties. Then, our method generates the intermediate images between the source and target images by calculating the intermediate meshes and pixels of each intermediate image. Our method realizes automatic contour extraction, provides an intuitive user interface and utilizes harmonic maps to establish a bijective mesh mapping. Therefore, it preserves more significant features with less distortion and works well for many image deformation cases in real time.
EVALUATING THE UTILITY OF GROSS ANATOMY INSTRUCTIONAL VIDEOS IN DISSECTION HALL: A PILOT OBSERVATIONAL STUDY

Introduction: Anatomy, being a highly visual and dynamic-experiential course, favours video based teaching to a larger extent.On the other hand, preparing a gross anatomy video is challenging, considering the cognitive load it imposes.Aim: To investigate the utility and acceptance of video-based learning approach among the first year medical students and its optimal curricular implementation. Materials and Methods:In this pilot initiative, we designed a two-stage revision method in head and neck regional anatomy teaching.To reinforce gross anatomy knowledge, we displayed the videos prepared by us in concordance with the learning objectives.We conducted a questionnaire based survey to determine the effects of these videos on anatomy learning.Descriptive statistical analysis was used to quantify the received responses.Results: Out of 100 students, who gave feedback on voluntary basis, 48 students agreed and 24 strongly agreed that the videos were adequately representing the structures they have seen in the dissection hall.40 students felt that displaying the structures along with description provided by the teacher after completion of dissection of that region, has affected their positive learning.28 agreed and 21 strongly agreed that the videos helped them in repeating and reinforcing the structures.79 students agreed that the quality of the video demonstrated were good to appreciate the structures and 91 students accepted to extend the video-based demonstration to other regions of gross Anatomy as well. Conclusion:Incorporating videos would contribute to the success of a subset of students by reinforcing gross anatomy learning.We suggest that developing user friendly videos which maintains optimal cognitive load is critical in enhancing student engagement and knowledge retention.
Hematological Parameters of Patients Suffering from Megaloblastic anemia and Aplastic anemia, Attending in Tertiary Care Hospital at Betiah, West Champaran, Bihar

Objective: The present study was undertaken to evaluate the role of red cell indices in differentiation between macrocytosis of aplastic anemia and megaloblastic anemia.Materials and Methods: A total of 36 patients each of bone marrow biopsy proven, megaloblastic anemia and aplastic anemia were included in the study.From all the patients of both the group's CBC were done by fully automated, five part CBC machine supplied by Mindray.All the parameter was noted and comparative study were evaluated.Results: It was observed that MCV was greater than 97 fl. in 14 cases of aplastic anemia (Mean MCV 106.8 fl) and 22 cases of megaloblastic anemia (mean MCV 111.82 fl).Hb, MCV, and MCHC were comparable in the two groups.However mean RDW (Red cell width) in megaloblastic anemia (mean 86.8 fl) was significantly higher than those in aplastic anemia (mean 70.4 fl).The difference in RDW of patients with megaloblastic anemia and aplastic anemia was statistically significant.Conclusion: RDW can be of help to differentiate between the megaloblastic anemia and aplastic anemia.
The Impact Of Selective Human Resources Management Practices On The Employee’s Competences At Jordanian Private Universities:, Case Study On Applied Science, Philadelphia, And Al-Zaytoneh Universities

This study aimed to explore the impact of the selective Human Resources Management Practices (Recruitment & Selection, Training, Performance Appraisal, and Compensation) on the employee’s competences (Knowledge, Skills, and Attitudes) at Jordanian private universities (Applied science, Philadelphia, and Alzaytoneh). The study population consisted of the human resources working at the Jordanian private universities researched were (1478); samples of (104) employees were selected. The researcher distributed the questionnaires to collect the primary data, and the SPSS statistical program of social studies was used. The study findings revealed that the selective Human Resources Management Practices (HRMP, s) are widely used and had appositive strong size effect on the employee’s competences (knowledge, skills, and attitudes) at Jordanian private universities (Applied science, Philadelphia and Alzaytoneh). Based on the study results the researcher recommends that Jordanian private universities have to take the necessary actions to adapt appropriate Human Resources Management Practices (Recruitment & Selection, Training, Performance Appraisal, and Compensation) that develop the employees competences (Knowledge, Skills, and Attitudes) to achieve the competitive advantages for their universities.
Blind Motion Deblurring with Cycle Generative Adversarial Networks

Blind motion deblurring is one of the most basic and challenging problems in image processing and computer vision. It aims to recover a sharp image from its blurred version knowing nothing about the blur process. Many existing methods use Maximum A Posteriori (MAP) or Expectation Maximization (EM) frameworks to deal with this kind of problems, but they cannot handle well the figh frequency features of natural images. Most recently, deep neural networks have been emerging as a powerful tool for image deblurring. In this paper, we prove that encoder-decoder architecture gives better results for image deblurring tasks. In addition, we propose a novel end-to-end learning model which refines generative adversarial network by many novel training strategies so as to tackle the problem of deblurring. Experimental results show that our model can capture high frequency features well, and the results on benchmark dataset show that proposed model achieves the competitive performance.
The Development of Musical Abilities as a Value and as the Outcome of a Strategy for Supporting a Child with Special Educational Needs

This article discusses the practical approaches to organising and implementing activities aimed at supporting musical abilities in the pre-school educational process. It presents the issues of the relevance and necessity of selecting educational tasks which take into account the interests, musical talents and individuality identified for a particular child. The progress of an individual in terms of musical skills, constituting a specific value of the musical abilities of a child, results from implementing the described organisational and methodological improvements, which create opportunities to experience a variety of forms of musical activity. An analysis of the author’s own research, demonstrating children’s musical achievements and skills, is used in the article to point out the value of pre-school education in this process.
Cross Domain Recommendation System Using Ontology and Sequential Pattern Mining

Recommendation system is very helpful to filter the information according to the user interest and provide user personalized suggestion. Recommendation system is emerging now-a-days in many social networks like Facebook, Twitter, e-commerce etc. Cross domain recommendation system is one of the method to develop the recommendation where we can gather the knowledge from different domains and recommend most similar items related to the user search term. In this work, we try to extend cross domain recommendation by finding semantic similarity of items in Ontology, applying Collaborative Filtering and recommending user preferred items using PrefixSpan algorithm. The similarity between items can be achieved through modified Wpath method. Finally, we can recommend the most preferred items and evaluate using performance measures like F-score.
NVS Machines: Learning Novel View Synthesis with Fine-grained View Control.

We present an approach that learns to synthesize high-quality, novel views of 3D objects or scenes, while providing fine-grained and precise control over the 6-DOF viewpoint. The approach is self-supervised and only requires 2D images and associated view transforms for training. Our main contribution is a network architecture that leverages a transforming auto-encoder in combination with a depth-guided warping procedure to predict geometrically accurate unseen views. Leveraging geometric constraints renders direct supervision via depth or flow maps unnecessary. If large parts of the object are occluded in the source view, a purely learning based prior is used to predict the values for dis-occluded pixels. Our network furthermore predicts a per-pixel mask, used to fuse depth-guided and pixel-based predictions. The resulting images reflect the desired 6-DOF transformation and details are preserved. We thoroughly evaluate our architecture on synthetic and real scenes and under fine-grained and fixed-view settings. Finally, we demonstrate that the approach generalizes to entirely unseen images such as product images downloaded from the internet.
Graph Neural Networks with distributed ARMA filters

Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better captures the global graph structure. We propose a graph neural network implementation of the ARMA filter with a recursive and distributed formulation, obtaining a convolutional layer that is efficient to train, localized in the node space, and can be transferred to new graphs at test time. We perform a spectral analysis to study the filtering effect of the proposed ARMA layer and report experiments on four downstream tasks: semi-supervised node classification, graph signal classification, graph classification, and graph regression. Results show that the proposed ARMA layer brings significant improvements over graph neural networks based on polynomial filters.