Reviewerly/exHarmony huggingface dataset:
Novel Idea of Unique Key Generation and Distribution Using Threshold Science to Enhance Cloud Security

In cloud environment, the owner may store insightful data of their organization in cloud storage. The cloud service provider should guarantee integrity, security, access control and confidentiality about the stored data at cloud. Most of the organizations pay no attention to the insider attack because it is harder to detect and mitigate. This is a major emerging problem in organizations. In cryptographic systems, an important element is threshold cryptography which secures the keys. Rather storing it at a single server, it is divided and distributed among the different entities of the system. As cloud security has still open challenges toward file sharing and data protection, this paper aims to design a new automated keyword formation mechanism using various techniques of natural language processing. The secret key is defined by this keyword. This secret key is then distributed among the users. The designed mechanism ensures the uniqueness of the key share and reconstruction process of secret using appropriate threshold value so that the authorized user can only decrypt and access the file.
Sinopia: A New Linked-Data Editing Environment Designed for Libraries

Linked Data for Production (LD4P): Pathway to Implementation is a grant funded by the Mellon Foundation to begin the implementation of the cataloging community’s shift to linked data for the creation and manipulation of their metadata. Although LD4P has seven overarching goals, the heart of the project will be the creation of a cloud-based, communal editing environment called Sinopia. Sinopia will support the efforts of a cohort of academic libraries in the creation and reuse of their resource metadata as linked data structured according to the BIBFRAME ontology. And by making use of another service created by the grant called Questioning Authority (QA), cohort members will be able to bring in additional identifiers for entities contained in data stores such as the RDA Vocabularies or Wikidata. The initial release of Sinopia will be in July of 2019 with a final release due at the close of the grant in June of 2020.
Adaptive Neuro-Fuzzy Approach for Forecasting of Solar Power Generation

This paper highlights the prediction of power generation from a solar panel with the help of the ANFIS. Solar irradiance and atmospheric temperature in a place are not even throughout the year. It depends on geographical position (i.e., longitude and latitude) and the environmental situation of the place. Power generation from a PV panel is highly subjective by solar irradiance and temperature. Pollution, fog, smoke dust cloud, etc. may also affect these two. So, changes in environmental conditions may affect the prediction of power generation from solar PV panel. In this paper, power generation is predicted for different irradiances and temperatures with the help of the ANFIS. ANFIS results are validated with the real-time experimental value which gives an improvement in predictive accuracy. This also signifies the ANFIS capability to estimate power in a different situation which could be helpful for maximum power point tracking.
On Deriving Nested Calculi for Intuitionistic Logics from Semantic Systems

This paper shows how to derive nested calculi from labelled calculi for propositional intuitionistic logic and first-order intuitionistic logic with constant domains, thus connecting the general results for labelled calculi with the more refined formalism of nested sequents. The extraction of nested calculi from labelled calculi obtains via considerations pertaining to the elimination of structural rules in labelled derivations. Each aspect of the extraction process is motivated and detailed, showing that each nested calculus inherits favorable proof-theoretic properties from its associated labelled calculus.
Privacy Preservation in Cloud Based Cyber Physical Systems

The Cyber physical systems (CPS) are integration of tightly coupled resource constrained physical and computational systems,which have several real time critical care applications. These systems depend on sensors to sense the physical environment. To overcome the limitations of computational power, CPS are supported by cloud computing systems as their backend. The enablement of cloud support to cyber physical cloud computing systemshave seamless advantages of improved efficiency, in same way this leads to major security and privacy concerns. These systems have decentralized and heterogeneous infrastructure and to fulfill all the security requirements, it is certainly a very complex task. In this paper we propose a generalized system model for securing different types of cloud based cyber physical systems, adversary model and holistic literature review of various CPS services, attacks, security challenges and the security schemes that can be applied to overcome a given attacks. At the end of the paper we discuss a case study on securing Medical CPS and compare the applicability of security schemes in this scenario.
Security improvements of several basic quantum private query protocols with O(log N) communication complexity

New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(logN) communication complexity of known protocols for the same task, but achieve several significant improvements in security, especially concerning user privacy. For example, the randomized form of our protocol has a cheat-sensitive property - it allows the user to detect a dishonest database with a nonzero probability, while the phase-encoded private query protocols for the same task do not have such a property. Moreover, when the database performs the computational basis measurement, a particular projective measurement which can cause a significant loss of user privacy in the previous private query protocols with O(logN) communication complexity, at most half of the user privacy could leak to such a database in our protocol, while in the QPQ protocol, the entire user privacy could leak out. In addition, it is proved here that for large N, the user could detect a cheating via the computational basis measurement, with a probability close to 1/2 using O(\sqrt{N}) special queries. Finally, it is shown here, for both forms of our protocol, basic and randomized, how a dishonest database has to act in case it could not learn user's queries.
BAR — A Reinforcement Learning Agent for Bounding-Box Automated Refinement

Research has shown that deep neural networks are able to help and assist human workers throughout the industrial sector via different computer vision applications. However, such data-driven learning approaches require a very large number of labeled training images in order to generalize well and achieve high accuracies that meet industry standards. Gathering and labeling large amounts of images is both expensive and time consuming, specifically for industrial use-cases. In this work, we introduce BAR (Bounding-box Automated Refinement), a reinforcement learning agent that learns to correct inaccurate bounding-boxes that are weakly generated by certain detection methods, or wrongly annotated by a human, using either an offline training method with Deep Reinforcement Learning (BAR-DRL), or an online one using Contextual Bandits (BAR-CB). Our agent limits the human intervention to correcting or verifying a subset of bounding-boxes instead of re-drawing new ones. Results on a car industry-related dataset and on the PASCAL VOC dataset show a consistent increase of up to 0.28 in the Intersection-over-Union of bounding-boxes with their desired ground-truths, while saving 30%-82% of human intervention time in either correcting or re-drawing inaccurate proposals.
Beyond the Grounding Bottleneck: Datalog Techniques for Inference in Probabilistic Logic Programs

State-of-the-art inference approaches in probabilistic logic programming typically start by computing the relevant ground program with respect to the queries of interest, and then use this program for probabilistic inference using knowledge compilation and weighted model counting. We propose an alternative approach that uses efficient Datalog techniques to integrate knowledge compilation with forward reasoning with a non-ground program. This effectively eliminates the grounding bottleneck that so far has prohibited the application of probabilistic logic programming in query answering scenarios over knowledge graphs, while also providing fast approximations on classical benchmarks in the field.
VERGE in VBS 2020

This paper demonstrates VERGE, an interactive video retrieval engine for browsing a collection of images or videos and searching for specific content. The engine integrates a multitude of retrieval methodologies that include visual and textual searches and further capabilities such as fusion and reranking. All search options and results appear in a web application that aims at a friendly user experience.
Polarization-Independent Coherent Spatial-Temporal Interface with Low Loss

A coherent interface between temporal and spatial degrees of freedom (DOFs) empowers photons to take advantage of both DOFs in quantum information processing. Such interfaces have either suffered from a typical loss of 1--3 dB or disturbed the polarization DOF in the conversion process. The authors realize a low-loss, polarization-independent, coherent interface between temporal and spatial DOFs (specifically time-bin and path modes), which promises applications like the postselection-loophole-free Bell inequality test, and can be put into a cavity to boost the generation rates of multiphoton polarization-entangled sources, or to enhance the scalability of sequential quantum metrology.
LDA and Deep Learning: A Combined Approach for Feature Extraction and Sentiment Analysis

As everything is available in online, had made the lives of common people to look towards the e-commerce applications for purchasing their general needs. People now-a-days want to share their preferences after receiving the goods as reviews. These reviews have a direct impact on the product for the customers to decide whether to buy or not. Besides, the huge information available in user reviews it is very difficult for the user to identify the exact preferences of good or bad based on the feature for what he is searching. So, extracting user preferences and item properties is very useful. This paper introduces an approach to extract the most important aspects from the opinions expressed in the input text using various machine learning and deep learning techniques. Further, it describes the topic modelling technique like LDA for extracting topics and uses deep learning for integrating the topics/features from topic modeling which is a meaningful approach for better understanding of reviews.
Vision/UWB/IMU sensor fusion based localization using an extended Kalman filter

Most positioning technologies require some information about the immediate environment (e.g., whether the sensor is indoors or outdoors). Typical global positioning system (GPS) sensors cannot function inside buildings, so ultra-wideband (UWB) sensors may be used to determine location. However, beacons must be installed in at least three predetermined positions if UWB devices are to be used indoors. UWB sensors may also be used outdoors, but their operating range is limited as accuracy decreases with distance from the beacons. We propose a method for the fusion of UWB and other sensor data to calculate location with a low error rate both indoors and outdoors. UWB beacons were installed 1-m-apart to aid detection. The allowable separation between the tags and beacons was increased through use of a vision sensor, and colored balls were attached to the beacons to ease identification. The colored balls were detected using the circle Hough Transform (CHT) algorithm and a UWB tag. The CHT algorithm performs well when used to detect circles, but is computationally expensive. Therefore, we present a method that reduced this load by assigning regions of interest when UWB or inertial measurement unit (IMU) sensors were used. The tag position was calculated from the coordinates of the UWB beacons captured in an image and other positional data measured with the UWB sensor. Our proposed method, which includes the application of an extended Kalman filter (EKF), successfully calculated position with a greater accuracy than UWB alone. We intend to extend this concept and develop a method that will allow measurement of the tag position when the UWB beacons are moving.
ASSESSMENT OF SOLAR PV POWER POTENTIAL IN THE ASIA PACIFIC REGION WITH REMOTE SENSING CONSIDERING THE EFFECTS OF HIGH TEMPERATURE, DUST AND SNOW

Abstract. The last half century has witnessed the increasing trend of renewable energy utilization with solar photovoltaic (PV) systems as one of the most popular option. Solar PV continues to supplement the main grid in powering both commercial establishments (mainly for reduced electricity expense) as well as residential houses in isolated areas (for basic energy requirement such as for lighting purposes). The objective of this study is to assess the available solar PV power (PPV) potential considering the effects of high temperature, dust and snow in the Asia Pacific region. The PPV potential was estimated considering the effects of the said meteorological parameters using several satellite data including shortwave radiation from Advanced Himawari Imager 8 (AHI8), MOD04 aerosol data from Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation rate from Global Satellite Mapping of Precipitation (GSMaP), air temperature from NCEP/DOE AMIP-II Reanalysis-2 data, and snow water equivalent (SWE) from Microwave Scanning Radiometer for the Earth Observing System (AMSR-E). The model is validated by comparing its outputs with the measured PV power from two solar PV installations in Bangkok, Thailand and Perth, Australia. Results show that maximum PPV is estimated at 2.5 GW (cell efficiency of 17.47%) for the region with the maximum decrease in PPV estimated to be about &lt; 2%, 22% and 100% due to high temperature (temperature coefficient of power = 0.47%/K), dust and snow, respectively. Moreover, areas in India and Northern China were observed to experience the effects of both dust and temperature during March-April-May (MAM) season. Meanwhile, countries located in the higher latitudes were severely affected by snow while Australia by high temperature during Dec-Jan-Feb (DJF) season. The model has a mean percentage prediction error (PPE) range of 5% to18% and 7% to 23% in seasonal and monthly estimations, respectively. Outputs from this study can be used by stakeholders of solar PV in planning for small-scale or large-scale solar PV projects in the solar rich region of Asia Pacific.
Representation Learning with Multiple Lipschitz-Constrained Alignments on Partially-Labeled Cross-Domain Data

The cross-domain representation learning plays an important role in tasks including domain adaptation and transfer learning. However, existing cross-domain representation learning focuses on building one shared space and ignores the unlabeled data in the source domain, which cannot effectively capture the distribution and structure heterogeneities in cross-domain data. To address this challenge, we propose a new cross-domain representation learning approach: MUltiple Lipschitz-constrained AligNments (MULAN) on partially-labeled cross-domain data. MULAN produces two representation spaces: a common representation space to incorporate knowledge from the source domain and a complementary representation space to complement the common representation with target local topological information by Lipschitz-constrained representation transformation. MULAN utilizes both unlabeled and labeled data in the source and target domains to address distribution heterogeneity by Lipschitz-constrained adversarial distribution alignment and structure heterogeneity by cluster assumption-based class alignment while keeping the target local topological information in complementary representation by self alignment. Moreover, MULAN is effectively equipped with a customized learning process and an iterative parameter updating process. MULAN shows its superior performance on partially-labeled semi-supervised domain adaptation and few-shot domain adaptation and outperforms the state-of-the-art visual domain adaptation models by up to 12.1%.
Modifications of the Czech Morphological Dictionary for Consistent Corpus Annotation

Abstract We describe systematic changes that have been made to the Czech morphological dictionary related to annotating new data within the project of Prague Dependency Treebank (PDT). We bring new solutions to several complicated morphological features that occur in Czech texts. We introduced two new parts of speech, namely foreign word and segment. We adopted new principles for morphological analysis of global and inflectional variants, homonymous lemmas, abbreviations and aggregates. The changes were initiated by the need of consistency between the data and the dictionary and of the dictionary itself.
Photon-matter quantum correlations in spontaneous Raman scattering

We develop a Hamiltonian formalism to study energy and position (momentum) correlations between a single Stokes photon and a single material excitation that are created as a pair in the spontaneous Raman scattering process. Our approach allows for intuitive separation of the effects of spectral linewidth, chromatic dispersion, and collection angle on these correlations, and we compare the predictions of the model to experiment. These results have important implications for the use of Raman scattering in quantum protocols that rely on spectrally unentangled photons and collective excitations.
IDT and Color Transfer-Based Color Calibration for Images Taken by Different Cameras

Images of the same object taken by multiple different cameras should have the same color reproduction. However, the images sometimes show different color reproduction due to the individual differences of cameras or internal camera parameters automatically determined when the images are taken. Conventional color transfer methods can be used for unifying the color reproduction of images by transforming the color distribution of an image to that of a reference image. However, conventional methods do not always lead to a good color reproduction and sometimes result in the loss of color impression of original images. In the present paper, we propose a color calibration method for images of the same object taken by different cameras. Two color transfer methods are combined to realize color calibration without the loss of color impression of an original image. Resultant images obtained by the color transfer methods are appropriately mixed into an output image. In experiments, the proposed method is applied to a variety of images and the effectiveness of the proposed method is confirmed by subjective and objective evaluations.
Subjective annotation for a frame interpolation benchmark using artefact amplification

Current benchmarks for optical flow algorithms evaluate the estimation either directly by comparing the predicted flow fields with the ground truth or indirectly by using the predicted flow fields for frame interpolation and then comparing the interpolated frames with the actual frames. In the latter case, objective quality measures such as the mean squared error are typically employed. However, it is well known that for image quality assessment, the actual quality experienced by the user cannot be fully deduced from such simple measures. Hence, we conducted a subjective quality assessment crowdscouring study for the interpolated frames provided by one of the optical flow benchmarks, the Middlebury benchmark. We collected forced-choice paired comparisons between interpolated images and corresponding ground truth. To increase the sensitivity of observers when judging minute difference in paired comparisons we introduced a new method to the field of full-reference quality assessment, called artefact amplification. From the crowdsourcing data, we reconstructed absolute quality scale values according to Thurstone's model. As a result, we obtained a re-ranking of the 155 participating algorithms w.r.t. the visual quality of the interpolated frames. This re-ranking not only shows the necessity of visual quality assessment as another evaluation metric for optical flow and frame interpolation benchmarks, the results also provide the ground truth for designing novel image quality assessment (IQA) methods dedicated to perceptual quality of interpolated images. As a first step, we proposed such a new full-reference method, called WAE-IQA. By weighing the local differences between an interpolated image and its ground truth WAE-IQA performed slightly better than the currently best FR-IQA approach from the literature.
Utilizing Informative Missingness for Early Prediction of Sepsis

Aims: Physicians have to routinely make crucial decisions about patients’ health in the ICU. Sepsis affects about 35% of ICU patients, killing approximately 25% of the afflicted. In this paper, we aim to predict the occurrence of sepsis early by studying the missingness of physiological variables and using it with the overall trends in data.Methods: We chose XGBoost as our base model and tried several variations by changing hyperparameters, window sizes and imputation methods. To further improve the model, we used masking vectors to represent the missingness of features in the dataset. Additional modifications include shifting the Sepsis Label to earlier time steps and tuning the classification probability threshold to further improve the model’s performance.Results: The XGBoost model with a sliding window of size 5, no imputation, utilizing informative missingness of all temporal variables and trained on labels shifted by 3 hours before t optimal , achieved a Utility Score of 0.337 on the full test set. We identified as CTL-Team in the challenge and were officially ranked 5th on the basis of this score.
Knowledge-Based Multi-sequence MR Segmentation via Deep Learning with a Hybrid U-Net++ Model

The accurate segmentation, analysis and modelling of ventricles and myocardium plays a significant role in the diagnosis and treatment of patients with myocardial infarction (MI). Magnetic resonance imaging (MRI) is specifically employed to collect imaging anatomical and functional information about the cardiac. In this paper, we have proposed a segmentation framework for the MS-CMRSeg Multi-sequence Cardiac MR Segmentation Challenge, which can extract the desired regions and boundaries. In our framework, we have designed a binary classifier to improve the accuracy of the left ventricles (LVs). Extensive experiments on both validation dataset and testing dataset demonstrate the effectiveness of this strategy and give an insight towards the future work.