Reviewerly/exHarmony huggingface dataset:
Privacy Preserving Model using Homomorphic Encryption

Privacy preserving is utmost important in medical applications.Cryptography has numerous techniques to safe guard the privacy of the data.It is practice to use private key for encryption and public key for decryption in the area of cryptography.Conventionally, without decryption, data usability is difficult.However, the complications outweigh the private and public keys.This paper presents privacy preserving model based on Homomorphic encryption technique and model evaluation using classification technique.The homomorphic model highlights usability of the data without decryption.The objective of this paper is to show how the encrypted data is preserving underlying relations through classification tree.This paper presents two parts: Part-I describes the model building on medical data using PSO optimization and filer based co-efficient matrix (for encryption) to protect privacy of the data and part-II describes model evaluation using classification tree and clustering technique.The performance of the encryption is tested using predictive modelling technique (classification tree technique) and K-Means clustering technique, to assess whether the underlying relations are preserved in the encrypted data.The experimental results show that the underlying classification accuracy of encrypted data and source data (non-encrypted) is just varying by +/-5%.
Semantic and Syntactic Fraction Understanding

This study begins by connecting semantic elaboration with conceptual understanding and syntactic elaboration with procedural understanding in the context of fractions.Through case studies and discourse analysis, the work and communication of students in fourth through sixth grade is analyzed to determine the extent of their semantic and syntactic elaboration regarding fractions and fraction operations.Findings are that, while some students emphasized one form of elaboration over the other, some students demonstrated use of both forms of elaboration.Indeed, it is wondered if semantic and syntactic elaboration should be seen as more complementary than adversarial.
Adaptive digital medical image watermarking approach in 2D-wavelet domain using speed-up robust feature method

In the recent trends, the utilisation of digital images had got their importance in many fields. One of the major fields which had the more prominence utilisation of digital images is health care. The patient's information is stored in digital images for maintaining privacy and there is a need for preserving the content of the images. Watermarking serves well for protecting digital images. In this paper, Adaptive Digital Medical image watermarking approach is proposed. This proposed method utilises the 2D-Wavelet domain for converting the medical image in to number of sub bands. The interesting points are identified using the Speed-Up Robust Feature (SURF) method. The scaling parameter is calculated for the digital images using the bipolar sigmoid function. The control parameter is introduced to adjust the scaling parameter and it plays a crucial role in deciding the strength of the watermark. Experimental evaluation is carried out using three parameters, Peak Signal to Noise Ratio (PSNR), Structure SImilarity Measure (SSIM) and Normalised Correlation Coefficient (NCC). The experimental results proved that the proposed method had superior performance in both visible and invisible watermarking.
Artificial Bee Colony Algorithm Based on Adaptive Local Information Sharing Meets Multiple Dynamic Environments

This paper focuses on the artificial bee colony (ABC) algorithm as one of swarm optimization methods and proposes ABC-alis (ABC algorithm based on adaptive local information sharing) by improving the ABC algorithm for dynamic optimization problems (DOPs). ABC-alis is applied to various types of dynamic changes embedded in DOPs to verify its tracking ability in such dynamic environments. Concretely, the following five types of dynamic changes and one of the high-dimensional problem are employed as the different environments: (A) a periodic change of evaluation values of local optima; (B) a random change of evaluation values of local optima; (C) a random change of local optima coordinates; (D) a combination of two kinds of random changes (B+C); (E) a random speed change of local optima in the environment (D); and (F) a high-dimensional problem in the environment (E). In these experiments, the following three methods are compared: ABC-alis as the proposed method, ABC-lis as our previous method of ABC-alis, and speciation-based particle swarm optimization (SPSO) as the conventional method. The experimental result revealed that the following implications: (1) ABC-alis and ABC-lis can capture the optimal solution more quickly and keep a better solution than SPSO in the various types of dynamic changes; (2) from environments C, D, and E, ABC-alis can adapt to the random change of local optima from the viewpoint of the evaluation value, coordinates, speed, and all of them; and (3) ABC-alis can maintain its performance even in the high dimensional environment F.
Augmented Reality applications in modern day library: A study

The twenty-first-century library is akin to a laboratory of experimentation and prototyping. Augmented Reality (AR) breaks new ground in the library field. It has gained wide public appeal in recent years and carved a niche in mainstream society with the global Pokemon GO craze of 2016. Although AR has been around for more than sixty years the term was not officially coined until 1990. It has come a long way from a science-fiction concept catapulting to a science-based reality. It is a cutting-edge technology that allows smart device users for a digitally enhanced view of the real world. Augmented Reality works in sync with headsets and/or digital devices such as tablets, smartphones and even PCs. The devices themselves contain software, sensors and digital projectors that trigger digital displays onto physical objects.  AR’s ability to enhance what already exists is what makes it a perfect fit for libraries. Keeping in view of the modern-day expectations of users from a library, the library professionals should dedicate de novo in dealing with Augmented Reality applications more effectively and efficiently under the rubric of the library – AR binary.
Some new constructions of isodual and LCD codes over finite fields

This paper presents some new constructions of LCD, isodual, self-dual and LCD-isodual codes based on the structure of repeated-root constacyclic codes. We first characterize repeated-root constacyclic codes in terms of their generator polynomials and lengths. Then we provide simple conditions on the existence of repeated-root codes which are either self-dual negacyclic or LCD cyclic and negacyclic. This leads to the construction of LCD, self-dual, isodual, and LCD-isodual cyclic and negacyclic codes.
THE USE OF MIND MAPPING TO IMPROVE MOTIVATION IN LEARNING VOCABULARY OF SECOND GRADE STUDENTS AT MTs NW MISPALAH PRAYA

This research was aimed to describe the use of mind mapping technique be able to improving students’motivation.The subject of this study was 37 students at second grade students of MTs. NW Mispalah Praya. In which consist of 25 males and 12 females. It is was classroom action research with one cycle, each cycle of four steps: planning, acting, observing, and reflecting. The data gathering used observation sheet and test and analyzed by using quantitative and qualitative approach. At the result of pre-test was failed where the mean score of pre-test was 65 and class percentage was 52% and the result of the first cycle that the mean score was 70,4 and the class percentage was 100% indicating the target of 70 of the minimum criterion has been achieved, it means that the action was stopped
Multiple Color Channel Local Extrema Patterns for Image Retrieval

A novel feature descriptor, multiple color channel local extremal pattern (MCLEP) is proposed in this manuscript. MCLEP combines the key features of local binary and local quantized extrema patterns in a specified neighborhood. Multi-distance information computed by the MCLEP aids in robust extraction of the texture arrangement. Further, MCLEP features are extracted for each color channel of an image. Retrieval performance of the MDLP is evaluated on benchmark datasets for CBIR, namely Corel-5000, Corel-10000, and MIT-Color Vistex, respectively. Proposed technique exhibits substantial improvement as compared to other recent feature descriptors in terms of ARP and ARR on standard databases.
The nested joint clustering via Dirichlet process mixture model

This article focuses on the clustering problem based on Dirichlet process (DP) mixtures. To model both time invariant and temporal patterns, different from other existing clustering methods, the proposed semi-parametric model is flexible in that both the common and unique patterns are taken into account simultaneously. Furthermore, by jointly clustering subjects and the associated variables, the intrinsic complex shared patterns among subjects and among variables are expected to be captured. The number of clusters and cluster assignments are directly inferred with the use of DP. Simulation studies illustrate the effectiveness of the proposed method. An application to wheal size data is discussed with an aim of identifying novel temporal patterns among allergens within subject clusters.
A Collaborative Filtering Algorithm Based on Mixed Similarity

The traditional collaborative filtering algorithm ignores the influence of user interest and item popularity in the calculation of similarity, which will lead to inaccurate calculation of similarity in terms of sparse data. This paper proposes a collaborative filtering algorithm which is based on mixed similarity algorithm. Both user interest similarity and item popularity similarity are taken into consideration. Results show that the proposed algorithm significantly reduces the calculation error comparing with the traditional algorithm. At the same time, the problem of data sparsity has been alleviated.
Two-Sided Trees for Sentential Logic, Predicate Logic, and Sentential Modal Logic

This paper will present two contributions to teaching introductory logic. The first contribution is an alternative tree proof method that differs from the traditional one-sided tree method. The second contribution combines this tree system with an index system to produce a user-friendly tree method for sentential modal logic.
Semi-parametric Bayesian regression for subgroup analysis in clinical trials

Determining whether there are differential treatment effects in subgroups of trial participants remains an important topic in clinical trials as precision medicine becomes ever more relevant. Any assessment of differential treatment effect is predicated on being able to estimate the treatment response accurately while satisfying constraints of balancing the risk of overlooking an important subgroup with the potential to make a decision based on a false discovery. While regression models, such as marginal interaction model, have been widely used to improve accuracy of subgroup parameter estimates by leveraging the relationship between treatment and covariate, there is still a possibility that it can lead to excessively conservative or anti-conservative results. Conceivably, this can be due to the use of the normal distribution as a default prior, which forces outlying subjects to have their means over-shrunk towards the population mean, and the data from such subjects may be excessively influential in estimation of both the overall mean response and the mean response for each subgroup, or a model mis-specification. To address this issue, we investigate the use of nonparametric Bayes, particularly Dirichlet process priors, to create semi-parametric models. These models represent uncertainty in the prior distribution for the overall response while accommodating heterogeneity among individual subgroups. They also account for the effect and variation due to the unaccounted terms. As a result, the models do not force estimates to excessively shrink but still retain the attractiveness of improved precision given by the narrower credible intervals. This is illustrated in extensive simulations investigating bias, mean squared error, coverage probability and credible interval widths. We applied the method on a simulated data based closely on the results of a cystic fibrosis Phase 2 trial.
Exploration Conscious Reinforcement Learning Revisited

The Exploration-Exploitation tradeoff arises in Reinforcement Learning when one cannot tell if a policy is optimal. Then, there is a constant need to explore new actions instead of exploiting past experience. In practice, it is common to resolve the tradeoff by using a fixed exploration mechanism, such as $\epsilon$-greedy exploration or by adding Gaussian noise, while still trying to learn an optimal policy. In this work, we take a different approach and study exploration-conscious criteria, that result in optimal policies with respect to the exploration mechanism. Solving these criteria, as we establish, amounts to solving a surrogate Markov Decision Process. We continue and analyze properties of exploration-conscious optimal policies and characterize two general approaches to solve such criteria. Building on the approaches, we apply simple changes in existing tabular and deep Reinforcement Learning algorithms and empirically demonstrate superior performance relatively to their non-exploration-conscious counterparts, both for discrete and continuous action spaces.
Abstract: Double Your Views: Exploiting Symmetry in Transmission Imaging

For a plane symmetric object we can find two views - mirrored at the plane of symmetry - that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we can automatically have a second, virtual image of that object if the 3D location of the symmetry plane is known. In this work, we show for the first time that the above concept naturally extends to transmission imaging and present an algorithm to estimate the 3D symmetry plane from a set of projection domain images based on Grangeat’s theorem.
A Proposed Framework Against Code Injection Vulnerabilities in Online Applications

Security vulnerabilities are frequently detected and exploited in modern web applications. Intruders obtain unrestricted access to the information stored at the back-end database server of a web application by exploiting security vulnerabilities. Code injection attacks top the list due to lack of effective strategies for detecting and blocking injection attacks. The proposed Token based Detection and Neural Network based Reconstruction (TbD-NNbR) framework is a unique approach to detect and block code injections with negligible processing overheads. This framework makes use of an efficient token mapping and validation technique to match the statically generated legal query tokens against the parsed dynamic query tokens at run time. The proposed approach also has the provision to reconstruct queries from authenticated users. The prototype implementation of TbD-NNbR shows that it does not demand any source code modifications and incurs only a negligible computational overhead without any incidents of false positives or false negatives.
A detailed survey on blockchain integration for connected cars

This paper provides a complete understanding of connected cars and Blockchain which are trending technologies here the discussion among the shared information among vehicles, V2V, V2X, Path planning and integration of Blockchain. Applications in which how the Blockchain is used for the connected car applications and how those operations can be performed with safety and security policy.
Edge-preserving filter with adaptive <i>L</i><sub>0</sub> gradient optimization

Wireless multimedia sensor networks have recently emerged as one of the most important technologies to actively perceive physical world and empower a wide spectrum of potential applications in various areas. Due to the advantages of rapid deployment, flexible networking, and multimedia information perceiving, wireless multimedia sensor networks are suitable for transmitting mass multimedia data such as audio, video, and images. Two-dimensional images are among the nuclear ways to convey certain information, and there exists a large number of image data to be processed and transmitted; however, the complexity of environment and the instability of sensing component both can give rise to the insignificant information of the resulted images. Hence, image processing attracts a lot of research concerns in last several decades. Our concern in this article is filtering technology on image signal. Filtering is shown to be a key technique to ensure the validity and reliability of the wireless multimedia sensor networks images, which aims to preserve salient edges and remove low-amplitude structures. The well-known L0 gradient minimization employs L0 norm as gradient sparsity prior, and it is capable of preserving sharp edges. Similar to the total variation model, L0 gradient minimization may easily suffer from the staircase effect and even lose part of the structure. Therefore, in this article, we propose an edge-preserving filter with adaptive L0 gradient optimization. Different from original L0 gradient minimization, we introduce an adaptive L0 regularization. The newly proposed adaptive function is feature-driven and makes the utmost of the image gradient, enabling the filter to remove low-amplitude structures and preserve key edges. Furthermore, the proposed filter can effectively avoid staircase effect and is robust to noise. We develop an efficient optimization algorithm to solve the proposed model based on alternating minimization. Through extensive experiments, our method shows many attractive properties like preserving meaningful edges, avoiding staircase effect, robustness to noise, and so on. Applications including noise reduction, clip-art compression artifact removal, detail enhancement and edge extraction, image abstraction and pencil sketching, and high dynamic range tone mapping further demonstrate the effectiveness of the proposed method.
Distributed Policy Iteration for Scalable Approximation of Cooperative Multi-Agent Policies

We propose Strong Emergent Policy (STEP) approximation, a scalable approach to learn strong decentralized policies for cooperative MAS with a distributed variant of policy iteration. For that, we use function approximation to learn from action recommendations of a decentralized multi-agent planning algorithm. STEP combines decentralized multi-agent planning with centralized learning, only requiring a generative model for distributed black box optimization. We experimentally evaluate STEP in two challenging and stochastic domains with large state and joint action spaces and show that STEP is able to learn stronger policies than standard multi-agent reinforcement learning algorithms, when combining multi-agent open-loop planning with centralized function approximation. The learned policies can be reintegrated into the multi-agent planning process to further improve performance.
The promotion role of mobile online education platform in students' self-learning

In order to solve the problem that traditional mobile online education platform can not provide personalised recommendation according to students' own situation and neglect students' learning emotion. A new mobile education platform was designed, and the role of the mobile online education platform in promoting students' self-learning was analysed. The education platform is designed using a C/S structure. It is composed of application layer, service layer and storage layer. The nearest neighbour set is formed by the FRUTAI method, and the user data filling of mobile online education platform is completed. The user's score information is used to get neighbour users and complete personalised recommendation. According to the relationship between expression and emotion in emotional psychology, the recognition module of expression information is processed to identify the expression, and the students' emotional state is obtained. According to the mapping relationship between learning state and two-dimensional emotional space, students' learning state and evaluation results are obtained. This paper analyses the role of mobile online education platform in promoting students' self-learning, and draws a conclusion that the design of mobile online education platform can improve students' interest in learning, increase students' self-learning time and enhance students' self-learning ability.
Filter technique of medical image on multiple morphological gradient (MMG) method

Filter technique is supportive for reducing image noise. This paper presents a study on filtering medical images, i.e., CT-Scan, Chest X-ray and Panoramic X-ray collected from two of the most prominent public hospitals in Padang City, Indonesia. The aim of this study preserved to facilitate in diagnosing objects in x-ray medical images. This study used filter technique, i.e. Blur, Emboss, Gaussian, Laplacian, Roberts, Sharpen, or Sobel techniques as pre-processing step. The filter process performed before edge detection and edge clarification. MMG method used in this study to clarify the edge detection. Thus, this research showed the hesitation decline (confidence increase) of the diagnosis of objects contained in medical images.
Research on OWL Ontology Learning Method Based on Relational Schema

Ontology learning is the key technology to construct knowledge base effectively. Most of data in information systems are organized in relational mode. Therefore, the purpose of this paper is to solve how to effectively learn ontology from relational data mode. It is found that the definition of OWL DL ontology based on description logic is similar to the formal definition of relational data schema. A semi-automatic learning method of OWL ontology based on relational schema is proposed in this paper. The method includes a set of mapping rules by which learning OWL ontology from relational schema. The mapping rule set consists of attribute axiom mapping, class axiom mapping and heuristic mapping. Finally, a prototype tool based on Java platform is used to build a knowledge base of traditional Chinese medicine, and the validity of the method is verified.
ANALISIS UNDANG-UNDANG REPUBLIK INDONESIA NOMOR 20 TAHUN 2003 TENTANG SISTEM PENDIDIKAN NASIONAL TERHADAP AKSES ORANG MISKIN PADA PENDIDIKAN

&lt;p&gt;&lt;em&gt;Government steps in realizing compulsory education is not only to provide and build infrastructure kepelosok education area, but the government is also expected to provide assistance in the form of program costs in order to support compulsory education. Investigators therefore conducted this research with the aim to determine the role of Law Number 20 Year 2003 on National Education System in protecting the rights of poor people to participate in education and to investigate the role of government in providing the poor access to education. The method used in this research is qualitative research and research sites located in Jombang District Education Office, SMA Patriot and SMA PGRI in the district Peterongan. Data collection techniques in this study through observation (participatory observation), interviews (structured interviews), documentation (research results). Results from the study showed that education in Jombang district is not discriminatory, even poor people get education services such as the PIP and BKSM budgeted from APBD and APBN.&lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;&lt;br /&gt;&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;