Reviewerly/exHarmony huggingface dataset:
Social Data: CESSDA Best Practices

The European Commission report “Turning FAIR into reality” provides an index of 27 FAIR Action Plan recommendations. This index is used for a self-assessment on CESSDA, the Consortium of European Social Science Data Archives. CESSDA is performing well on “Concepts for FAIR implementation”, “Skills for FAIR”, and “Investment in FAIR”; there is work in progress on “FAIR culture”, and work to start up on “FAIR ecosystem” and especially on “Incentives and metrics for FAIR data and services”. Next, an analysis on the FAIR components, reveals that CESSDA has accomplished the “F”, is working on the “A” – considering the sensitivity and security requirements of social data, just started on “I”, and that there is lack of clarity on what should be in “R”. On Findability, the CESSDA Data Catalogue is explained, showing the building blocks that need to be in place before one can produce a catalogue. The article ends with a forward look on CESSDA's deployment on the FAIR principles.
Leveraging Hierarchical Category Knowledge for Data-Imbalanced Multi-Label Diagnostic Text Understanding

Clinical notes are essential medical documents to record each patient's symptoms.Each record is typically annotated with medical diagnostic codes, which means diagnosis and treatment.This paper focuses on predicting diagnostic codes given the descriptive present illness in electronic health records by leveraging domain knowledge.We investigate various losses in a convolutional model to utilize hierarchical category knowledge of diagnostic codes in order to allow the model to share semantics across different labels under the same category.The proposed model not only considers the external domain knowledge but also addresses the issue about data imbalance.The MIMIC3 benchmark experiments show that the proposed methods can effectively utilize category knowledge and provide informative cues to improve the performance in terms of the top-ranked diagnostic codes which is better than the prior state-of-the-art.The investigation and discussion express the potential of integrating the domain knowledge in the current machine learning based models and guiding future research directions.
A Data Preparation Approach for Cloud Storage Based on Containerized Parallel Patterns

In this paper, we present the design, implementation, and evaluation of an efficient data preparation and retrieval approach for cloud storage. The approach includes a deduplication subsystem that indexes the hash of each content to identify duplicated data. As a consequence, avoiding duplicated content reduces reprocessing time during uploads and other costs related to outsource data management tasks. Our proposed data preparation scheme enables organizations to add properties such as security, reliability, and cost-efficiency to their contents before sending them to the cloud. It also creates recovery schemes for organizations to share preprocessed contents with partners and end-users. The approach also includes an engine that encapsulates preprocessing applications into virtual containers (VCs) to create parallel patterns that improve the efficiency of data preparation retrieval process. In a study case, real repositories of satellite images, and organizational files were prepared to be migrated to the cloud by using processes such as compression, encryption, encoding for fault tolerance, and access control. The experimental evaluation revealed the feasibility of using a data preparation approach for organizations to mitigate risks that still could arise in the cloud. It also revealed the efficiency of the deduplication process to reduce data preparation tasks and the efficacy of parallel patterns to improve the end-user service experience.
Mutually Unbiased Measurement Based Entanglement Witnesses

We study entanglement witness and present a construction of entanglement witnesses in terms of the mutually unbiased measurements (MUMs). These witnesses include the entanglement witnesses constructed from mutually unbiased bases (MUBs) as a special case. Comparing with the dimension dependence of MUBs, the witnesses can be always constructed from a complete set of d + 1 MUMs for any dimension d. We show that our witness can detect entanglement better than previous separability criterion given also by MUMs. And the approach can be experimentally implemented.
Peking at MRP 2019: Factorization- and Composition-Based Parsing for Elementary Dependency Structures

We design, implement and evaluate two semantic parsers, which represent factorizationand composition-based approaches respectively, for Elementary Dependency Structures (EDS) at the CoNLL 2019 Shared Task on Cross-Framework Meaning Representation Parsing.The detailed evaluation of the two parsers gives us a new perception about parsing into linguistically enriched meaning representations: current neural EDS parsers are able to reach an accuracy at the interannotator agreement level in the same-epochand-domain setup.
INTERNET OF THINGS (IOT), SECURITY ISSUES AND ITS SOLUTIONS

Internet of things (IoT) plays a great role in the 21 st century than a couple of years ago.Internet of things (IoT) provides us a technologically and optimistic image of the future where several computing objects are linked to the internet and they can recognize themselves with additional computing strategies.In this paper detail review of security issues over different layers of IoT has been covered.This paper also discusses the solutions possible to overcome those security issues.In this paper the brief discussion on security threats and how to measure it respectively to protect information or confidential information of the users IoT accentuations on controlling different assignments and attempt to approve the physical things to act denied of any obstruction of the human.The up and coming and existing IoT introductions are much guaranteed to expand the solace level, effectiveness, and robotization of the clients.The objects can represent digitally because IoT is very significant.
IMPROVING STUDENTS’ WRITING SKILL USING BRAINSWRITING STRATEGY

Writing is one of important skill in English subject beside the other skills. To improve the students’ writing skill, we have to know what strategy is the most suitable for the students’. This research used Classroom Action Research. The research was on two cycles with total meetings were seven; three meetings were for tests, and four meetings were for treatment. Each cycle covered the step of planning, acting, observing, and reflecting. The population of this research was the Seventh Grade Students of SMP Pandu Nusantara in the academic year of 2018/2019 which consisted of 30 students. The average score of pretest was 45. After the implementation of brainswriting strategy in every cycle, the students’ writing skill score were getting better. It can be seen from the research of students’ average score Cycle 1 was 50 and Cycle was 71. It can be concluded that brainswriting strategy can improve students’ writing skill and improve writing classroom into a better situation.
Image watermarking using discrete wavelet-tchebichef transform

&lt;span&gt;Image watermarking is one of the most popular techniques for authenticating copyright on the digital image. Many research on image watermarking has proved that the joint of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) combinations can improve both imperceptibility and robustness when compared to DCT or DWT only. Discrete Tchebichef Transform (DTT) denotes an alternative transformation that has a similarity property with DCT. DTT has an advantage in reducing memory requirements during computing, so the calculation speed is much faster than DCT. This study tested the performance of DTT and DCT on non-blind image watermarking method, where DTT and DCT are performed after DWT. Based on the experimental results, this research proved that the DTT was combined successfully with DWT and very potential for further investigation because it has a computing performance much better than DCT. While the image watermarking quality, both the imperceptibility and robustness aspects were completely identical with the combination of DCT and DWT transformation.&lt;/span&gt;
Context-Aware Ranking by Constructing a Virtual Environment for Reinforcement Learning

Result ranking is one of the major concerns for Web search technologies. Most existing methodologies rank search results in descending order according to pointwise relevance estimation of single results. However, the dependency relationship between different search results are not taken into account. While search engine result pages contain more and more heterogenous components, a better ranking strategy should be a context-aware process and optimize result ranking globally. In this paper, we propose a novel framework which aims to improve context-aware listwise ranking performance by optimizing online evaluation metrics. The ranking problem is formalized as a Markov Decision Process (MDP) and solved with the reinforcement learning paradigm. To avoid the great cost to online systems during the training of the ranking model, we construct a virtual environment with millions of historical click logs to simulate the behavior of real users. Extensive experiments on both simulated and real datasets show that: 1) constructing a virtual environment can effectively leverage the large scale click logs and capture some important properties of real users. 2) the proposed framework can improve search ranking performance by a large margin.
Unsupervised Dense Light Field Reconstruction with Occlusion Awareness

Abstract Light field (LF) reconstruction is a fundamental technique in light field imaging and has applications in both software and hardware aspects. This paper presents an unsupervised learning method for LF‐oriented view synthesis, which provides a simple solution for generating quality light fields from a sparse set of views. The method is built on disparity estimation and image warping. Specifically, we first use per‐view disparity as a geometry proxy to warp input views to novel views. Then we compensate the occlusion with a network by a forward‐backward warping process. Cycle‐consistency between different views are explored to enable unsupervised learning and accurate synthesis. The method overcomes the drawbacks of fully supervised learning methods that require large labeled training dataset and epipolar plane image based interpolation methods that do not make full use of geometry consistency in LFs. Experimental results demonstrate that the proposed method can generate high quality views for LF, which outperforms unsupervised approaches and is comparable to fully‐supervised approaches.
A Contradiction Separation Dynamic Deduction Algorithm Based on Optimized Proof Search

S-CS rule Heuristic strategy A B S T R A C TMost of the advanced first-order logic automated theorem proving (ATP) systems adopt binary resolution methods as the core inference mechanism, where only two clauses are involved and a complementary pair of literals are eliminated during each deduction step.Recently, a novel multi-clause inference rule is introduced along with its soundness and completeness, which is called as standard contradiction separation rule (in short, S-CS rule) and allows multiple (two or more) clauses to be involved in each deduction step.This paper introduces and evaluates the application of S-CS rule in first-order logic ATP.Firstly, it analyzes several deduction methods of S-CS rule.It is then focused on how this multi-clause deduction theory can be achieved through forming a specific and effective algorithm, and finally how it can be applied in the top ATP systems in order to improve their performances.Concretely, two novel multi-clause S-CS dynamic deduction algorithms are proposed based on optimized proof search, including related heuristic strategy, then the application method applied in the state of the art ATP system Eprover (the version of Eprover 2.3) is introduced.Eprover with the proposed multi-clause deduction algorithms are evaluated through the FOF division of the CASC-J9 (in 2018) ATP system competition.Experimental results show that Eprover with the proposed multi-clause deduction algorithms outperform the plain Eprover itself to a certain extent.
User requirements dynamic elicitation of complex products from social network service

In the early stages of a product development, it is critical to understand and elicit the requirements of stakeholders. Complex products have multiple stakeholders' requirements, including buyers, end users, operators, suppliers, and government agencies, etc. However, the end users' requirements have received scant attention compared to the other stakeholders', especially buyers and operators. In addition, the elicitation of emerging requirement items and the identification of requirements' preferences are seldom studied in an automated and dynamic way. This paper proposes a data mining driven methodology to elicit users' requirements of complex products from Social Network Service (SNS) by considering the dynamic natures of requirements. The proposed method starts with collecting users' opinion data from SNS based on Python. Next, the raw opinion data containing dominant and recessive noise is filtered based on filtering rules and support vector machine. Afterward, de-noised opinion data is automatically classified into topics (i.e., requirement item candidates) based on K-means and silhouette method, and the attention degree of each topic is calculated based on statistical analysis of the SNS information of forwards, likes, and comments, etc. Finally, the emerging requirement items and time-varying requirements' preferences are identified based on the attention degree of each topic. The proposed method has been verified by a case study via a metro vehicle.
A two-step feature selection method for quranic text classification

Feature selection is an integral phase in text classification problems. It is primarily applied in preprocessing text data prior to labeling. However, there exist some limitations with the FS techniques. The filter-based FS techniques have the drawback of lower accuracy performance while the wrapper-based techniques are highly computationally expensive to process. In this paper, a two-step FS method is presented. In the first step, chisquare (CH) filter-based technique is used to reduce the dimensionality of the feature set and then wrapper correlation-based (CFS) technique is employed in the second step to further select most relevant features from the reduced feature set. Specifically, the ultimate aim is to reduce the computational runtime while achieving high classification accuracy. Subsequently, the proposed method was applied in labeling instances of the input data (Quranic verses) using standard classifiers: naïve bayes (NB), support vector machine (SVM), decision trees (J48). The results report the proposed method achieved accuracy result of 93.6% at 4.17secs.
Impact of institutional quality on economic performance of Eastern Africa: a panel data analysis

Nowadays, studies argued that international difference in prosperity across a country is the matter institutional quality. Thus, the poor economic performance of African's is linked to their weak institutional quality. The aim of this study is to examine the extent to which institutional quality affect economic performance of 14 selected East African Countries; Burundi, Comoros, Djibouti, Ethiopia, Kenya, Madagascar, Malawi, Mozambique, Mauritius, Rwanda, Tanzania, Uganda, Zambia and Zimbabwe, over the period 2005-2016, using fixed effect and System GMM methods. The finding of this study confirms with the existing empirical study that economic institutions matter for economic performance among which control of corruption and government effectiveness has positive impact on economic performance, while rule of law has adverse impact. The finding of this study implies that that Eastern Africa with better institutions has a higher economic performance. Therefore, the Eastern Africa countries should improve those institutions that have positive impact, and promote and change those institutions that have adverse effect in way that it can promote economic development.
Study of Kinematic System for Solar Tracking of a Linear Fresnel Plant to Reduce End Losses

Linear Fresnel Plants are composed of flat mirrors that reflect direct radiation towards a fixed receiver tube parallel to the reflectors.In traditional plants, mirrors are able to rotate around a single axis.This type of solar tracking system generates high losses at its ends.In the paper a new configuration is studied in which the mirrors placed at the ends are moved around two axes.The proposed system is thus able to recover the direct radiation which, otherwise, would not reach the receiver tube.The rotation of the mirrors is managed by stepper servomotors.They allow a robust solar tracking, not influenced by external forces.The work presents the kinematic system for moving the field of mirrors actuated with open loop control.Stepper motors generate a motion by steps; in order to execute each step at the exact moment, the logic of movement is properly defined.It is necessary to know in detail the temporal position and the law of motion for each rotation to identify the driving logic of the motors.The behaviour of the "electric motor -mechanical system" is numerically modelled, evaluating the torque and the errors in the motion.
Multiview Clustering via Robust Neighboring Constraint Nonnegative Matrix Factorization

Many real-world datasets are described by multiple views, which can provide complementary information to each other. Synthesizing multiview features for data representation can lead to more comprehensive data description for clustering task. However, it is often difficult to preserve the locally real structure in each view and reconcile the noises and outliers among views. In this paper, instead of seeking for the common representation among views, a novel robust neighboring constraint nonnegative matrix factorization (rNNMF) is proposed to learn the neighbor structure representation in each view, and L 2,1 -norm-based loss function is designed to improve its robustness against noises and outliers. Then, a final comprehensive representation of data was integrated with those representations of multiviews. Finally, a neighboring similarity graph was learned and the graph cut method was used to partition data into its underlying clusters. Experimental results on several real-world datasets have shown that our model achieves more accurate performance in multiview clustering compared to existing state-of-the-art methods.
A Hybrid Approach for Skew Detection and Correction in the Multi-script Scanned Document

Skew detection and correction of a scanned document is a very important step in Optical Character Recognition because skew of scanned document is reducing the accuracy of text line approach for skew detection and correction to calculate the skew angle on multi-script scanned document using Radon transform, Hough transform, Harries corner, Wiener filter and smearing algorithm. In this paper, a proposed approach is compared existing skew detection and correction techniques for printed documents having different scripts: English, Tamil, Sinhala and mixed-script. A proposed hybrid method is tested on 160 documents. The overall testing results is 90.62% for skew detection and correction.
WordleNet: A visualization approach for relationship exploration in document collection

Document collections do not only contain rich semantic content but also a diverse range of relationships. We propose WordleNet, an approach to supporting effective relationship exploration in document collections. Existing approaches mainly focus on semantic similarity or a single category of relationships. By constructing a general definition of document relationships, our approach enables the flexible and real-time generation of document relationships that may not otherwise occur to human researchers and may give rise to interesting patterns among documents. Multiple novel visual components are integrated in our approach, the effectiveness of which has been verified through a case study, a comparative study, and an eye-tracking experiment.
Rapid serial blinks: An index of temporally increased cognitive load

In recent years, natural viewing settings with video presentation have been used in neurological and psychological experiments. However, the experienced cognitive load may differ among participants. In this study, we show that rapid serial blinks (RSB) can indicate temporally increased cognitive load with high temporal resolution. We proposed a method to create a personal criterion for respective participants by using empirical blink intervals. When we focused on more than four serial blinks (i.e., three inter-blink intervals), an increased number of RSB detect participants who felt hard to understanding, indicating a poor understanding of the subject matter. By contrast, a constant criterion across participants used in previous study could not detect participant's understanding. These results suggest that individual differences in cognitive trait of each participant may skew the results of experiments. To avoid biases, we recommend researchers to perform an operational check on individually different temporally increased cognitive loads among experimental groups.
T-SVD-Based Robust Color Image Watermarking

In order to protect the copyright of the color image, a robust color image watermarking method based on Tensor-Singular Value Decomposition (T-SVD) is proposed. In order to preserve strong correlations of the RGB three channels of the color image, the color image is considered as the third-order tensor, which is transformed by using T-SVD to obtain two orthogonal tensors and one diagonal tensor. Since the diagonal tensor includes the main energies of the color image, it can be exploited to embed watermark for robustness. Firstly, the three channels of the color image are decomposed by using discrete wavelet transform (DWT) into four subbands, respectively, and the LL subband of each channel is used to construct an approximation image. Secondly, the approximation image is divided into non-overlapping blocks with the size of 4 × 4, and the TSVD is applied in each block to compute the first, the second and the third diagonal matrices. Finally the grayscale watermark is decomposed by using Singular Value Decomposition (SVD). The principal components of the gray-scale watermark are embedded by modifying the first diagonal matrix of each block, since the first diagonal matrix is more robust than other two diagonal matrices. Experiment results show that the proposed method can resist a variety of image attacks, and is more robust than the existing watermarking methods.
Integration of Relational and Graph Databases Functionally

Abstract In today’s multi-model database world there is an effort to integrate databases expressed in different data models. The aim of the article is to show possibilities of integration of relational and graph databases with the help of a functional data model and its formal language – a typed lambda calculus. We suppose the existence of a data schema both for the relational and graph database. In this approach, relations are considered as characteristic functions and property graphs as sets of single-valued and multivalued functions. Then it is possible to express a query over such integrated heterogeneous database by one query expression expressed in a version of the typed lambda calculus. A more user-friendly version of such language could serve as a powerful query tool in practice. We discuss also queries sent to the integrated system and translated into queries in SQL and Cypher - the graph query language for Neo4j.