Reviewerly/exHarmony huggingface dataset:
AMBLE: A Context-Aware Mobile Learning Framework

INTRODUCTION:Mobile Learning is a new pivotal learning trend nowadays.With the increasing use of sophisticated smartphones equipped with augmented reality supporting tools and sensors, mobile learning platforms are expected to deliver tailor-made and customized learning elements to learners.Context-awareness is regarded as the fundamental approach or workaround to lift this learning style to distribute adaptive and personalized learning elements in mobile devices. OBJECTIVES:The main priority in mobile learning is to make learning elements as flexible as possible using different forms of context data to extend the natural adaptation capabilities in mobile devices in order to engage learners in extremely rich environments.METHODS: In this paper, A context-aware MoBile LEarning framework is proposed, namely the AMBLE framework.It processes contextual data at four distinct levels namely: Sensing Layer, Adaptation Layer, Context Processing Layer, and Application Layer to perform adaptation of learning contents based on the actual environment and conditions of the learner.RESULTS: Partial implementation of the proposed framework has the potential to capture and represent the physical context information that may be used to perform a dynamic adaptation of learning contents and thus significantly improve the mobile learning experiences.Extra work is expected regarding the implementation of the other layers and components of the framework including the user model, context manager, and the adaptation engine. CONCLUSION:The AMBLE framework proposes some relevant content adaptations with some positive results.As future works, new forms of user-context adaptation synthesized with other extracted data sets of contextual information will be used to establish and align relevant dynamic adaptation and personalization of learning contents.
Single Image Rain Removal Based on Deep Learning and Symmetry Transform

Rainy, as an inevitable weather condition, will affect the acquired image. To solve this problem, a single image rain removal algorithm based on deep learning and symmetric transformation is proposed. Because of the important characteristics of wavelet transform, such as symmetry, orthogonality, flexibility and limited support, wavelet transform is used to remove rain from a single image. The image is denoised by using wavelet decomposition, threshold value and wavelet reconstruction in wavelet transform, and the rain drop image is transformed from RGB space to YUV (luma chroma) space by using deep learning to obtain the brightness component and color component of the image. the brightness component and residual component of the raindrop source image and the ideal recovered image without raindrop are extracted. The residual image and brightness component are overlapped again, the reconstructed image is restored to RGB space by YUV inverse transformation, and the final color raindrop free image is obtained. After training the network, the optimal parameters of the network are obtained, and finally the convolution neural network which can effectively remove the rain line is obtained. Experimental results show that compared with other algorithms, the proposed algorithm achieves the highest value in both peak signal-to-noise ratio (PSNR) and structural similarity, which shows that the image effect of the algorithm is better after rain removal.
IOT based Smart and Secure Health Care System Analysis &amp; Data Comparison

Advances in ultimate technologies should be begun to the growth of the Internet of Things (IOT). During the new health care situation, the usage of IOT technologies makes the assistance of doctors and patients since they are utilized in various medical sections (such as real-time monitoring, patient data administration, and healthcare supervision). The body sensor network (BSN) technology is one the best technologies of IOT developments in the healthcare system, where patient body parameters can be observed using a group of small powered and lightweight wireless sensor nodes. In this Security or pricy of patient report is very essential aspect.This paper highlights some essential protection provisions in BSN based modern healthcare system. .
Enhancing IPsec Performance in Mobile IPv6 Using Elliptic Curve Cryptography

Internet has become indispensable to the modern society nowadays. Due to the dynamic nature of human activities, the evolving mobile technology has played a significant role and it is reflected in the exponential growth of the number of mobile users globally. However, the characteristic of the Internet as an open network made it vulnerable to various malicious activities. To secure communication at network layer, IETF recommended IPsec as a security feature. Mobile IPv6 as the successor of the current mobile technology, Mobile IPv4, also mandated the use of IPsec. However, since IPsec is a set of security algorithm, it has several well-known weaknesses such as bootstrapping issue when generating a security association as well as complex key exchange mechanism. It is a well-known fact that IPsec has a high overhead especially when implemented on Mobile IPv6 and used on limited energy devices such as mobile devices. This paper aims to enhance the IPsec performance by substituting the existing key exchange algorithm with a lightweight elliptic curve algorithm. The experiments managed to reduce the delay of IPsec in Mobile IPv6 by 67% less than the standard implementation.
A Description of Moral Values in JK Rowling’s Novel Harry Potter and Prisoner of Azkaban and their Application to Indonesian Morality Education

The research aims to find out the moral value in novel and their application to Indonesia morality education. The object of this research is a novel with title Harry Potter and Prisoner of Azkaban by JK Rowling. This research was using qualitative as the methodology and the data was collected by checklist data sheet as an instrument. The checklist consisted of 21 items which represent the moral value base on its types. This research uses Linda theory. The result of data analysis shows that there are 89 items moral value in the novel and all value can be application in Indonesia morality by using 5 value from Character Education in K13. From the results, it is concluded that people in a novel always bring some value which related to education and teacher need to know how to use it.
Strong Bisimulation for Control Operators (Invited Talk).

The purpose of this paper is to identify programs with control operators whose reduction semantics are in exact correspondence. This is achieved by introducing a relation ≃, defined over a revised presentation of Parigot’s λμ-calculus we dub ΛM.Our result builds on two fundamental ingredients: (1) factorization of λμ-reduction into multiplicative and exponential steps by means of explicit term operators of ΛM, and (2) translation of ΛM-terms into Laurent’s polarized proof-nets (PPN) such that cut-elimination in PPN simulates our calculus. Our proposed relation ≃ is shown to characterize structural equivalence in PPN. Most notably, ≃ is shown to be a strong bisimulation with respect to reduction in ΛM, i.e. two ≃-equivalent terms have the exact same reduction semantics, a result which fails for Regnier’s σ-equivalence in λ-calculus as well as for Laurent’s σ-equivalence in λμ.
Joint control of a robotic arm using particle swarm optimization based H2/H∞ robust control on arduino

This paper proposes a small structure of robust controller to control robotic arm’s joints where exist some uncertainties and unmodelled dynamics. Robotic arm is widely used now in the era of Industry 4.0. Nevertheless, the cost for an industry to migrate from a conventional automatic machine to industrial robot still very high. This become a significant challenge to middle or small size industry. Development of a low cost industrial robotic arm can be one of good solutions for them. However, a low-cost manipulator can bring more uncertainties. There might be exist more unmodelled dynamic in a low-cost system. A good controller to overcome such uncertainties and unmodelled dynamics is robust controller. A low-cost robotic arm might use small or medium size embedded controller such as Arduino. Therefore, the control algorithm should be a small order of controller. The synthesized controller was tested using MATLAB and then implemented on the real hardware to control a robotic manipulator. Both the simulation and the experiment showed that the proposed controller performed satisfactory results. It can control the joint position to the desired position even in the presence of uncertainties such as unmodelled dynamics and variation of loads or manipulator poses.
AN APPLICATION OF MACHINE LEARNING TO BAD PAGE PREDICTION IN MULTILEVEL FLASH

Author(s): Sree Prem, Navya | Advisor(s): Siegel, Paul H | Abstract: Flash memory is prone to failures as the number of program-erase cycles increase. These physical failures in the flash result in an increase in the bit error rates. Once the bit error count exceeds a certain threshold the error correction engines are incapable of correcting the error without adversely impacting the system performance, or may even fail entirely. This leads to an interest in learning the behavior of error count increase and page failure in the flash memory and obtaining an ability to make failure predictions. We tackle this problem using a machine learning approach. However, standard machine learning techniques may not work well with the particular data in hand. This is because the error counts are collected from actual flash memory and one can expect to see more pages with a lower error count than pages with a higher error count. This feature of the dataset leads to a formulation of our goal in terms of a classification problem with significant class imbalance in the underlying data. We have investigated various classification methods that address such class imbalance. Among those considered are cost-sensitive boosting techniques, bagging procedures, bagging ensemble support vector machines (SVMs) and cost-sensitive neural networks.
Method for detecting digital image integrity violations due to its block processing

The detection of data integrity violations is one of the main issues for information security professionals. In particular, this problem is relevant for digital images (DI), which this work is dedicated to. Block processing of DI has become typical for modern steganographic algorithms, and it is widely used today. Block processing is any processing that involves saving the resulting image in lossy formats (JPEG, JPEG2000). It is local image processing that occurs during cloning and photo editing. In this regard, the aim of the work is to provide effective detection of the DI integrity violations due to its block processing, regardless of its specific type by developing an appropriate method. The method is based on the results obtained by the author previously through research studies of the formal parameters of DI matrix blocks. The proposed method and its algorithmic implementation (SA) have no analogues, that is confirmed by data obtained from open sources since existing methods for detecting image integrity violations are usually designed for specific perturbations. The paper presents the results of a computational experiment, which has confirmed the high efficiency of the developed algorithm in the conditions of all considered perturbations, including block processing. Comparison of the SA effectiveness with analogues was carried out under conditions of specific perturbations. In all cases considered, the efficiency of the developed algorithm is comparable with the best of modern analogues. When using the SA as a steganalysis algorithm for detecting the LSB-embeddings in containers saved with losses, it remains effective under conditions of low capacity of the hidden communication channel (0,01 bit/pixel), while most of analogues algorithms are inoperative.
An Integrated Methodological Analysis for the Highest Best Use of Big Data-Based Real Estate Development

This study investigates the integration of methods for real estate development planning and feasibility studies in the changing business environments of emerging big-data. It examines high-rise mixed-use development projects for the highest best use by combining fuzzy theory; thus, it identifies a big data-based innovative decision-making method for systemizing the various factors expected to influence real estate development. In this context, the study creates new evaluation fields and factors by integrating both conventional and big-data based high-rise mixed-use projects. The weight of each value was calibrated by relative significance and fuzzy measure using the Analytic Hierarchy Process (AHP) method. A measuring technique that applies analysis methodology to the evaluation areas was developed for more objective and clearer evaluation, and its application in the field was proposed. Evaluators can systematically assess the concerned evaluation areas during development project planning by examining the process. The findings also provided implications for the evaluation system’s operation by reflecting the variability of specific conditions of the varying projects in real estate and urban and land use planning.
Restricted Region Based Iterative Gradient Method for Non-Targeted Attack

Neural networks have been widely applied but they are still vulnerable to adversarial examples. More and more defense models have been proposed and they can resist the attacks to the neural networks. In order to generate adversarial examples with good transferability, we propose the restricted region based iterative gradient method (RRI-GM) for non-targeted attack, which aims at generating adversarial examples to make black-box defense models output wrong decision. We first use object detection algorithm to restrict some key regions in the images, since we regard perturbation in the key region affects more than the whole image. To improve the efficiency of attacks, we use gradient-based attack methods and they show good performance. In addition, the process is iterated for multiple rounds to generate adversarial examples with good transferability. Furthermore, we conduct extensive experiments to validate the effectiveness of the proposed method, and the results show that our method can achieve good attack performance against black-box defense models.
Some properties of the generalized Gaussian ratio and their applications

We are devoted to an integral, asymptotic expansion and Maclaurin series representation for the generalized Gaussian ratio, and find their various related properties such as complete monotonicity and some useful inequalities.As applications, several simple approximations for its inverse function are presented, which may be essential to the estimations for the shape parameter of the generalized Gaussian distribution.
Visualizing Social Documents as Traces of Collaborative Activity in Enterprise Collaboration Platforms

Enterprise collaboration platforms are large-scale information infrastructures that provide a wide range of tools and functionality to support collaborative work in organizations.These collaborative activities leave digital traces in the form of social documents, which can be analyzed to understand how employees work together to coordinate their joint work.In this paper, we present the findings of a research project to visualize the structure of social documents to prepare them for analysis as traces of collaborative activity.Using the representation of social documents defined in the Social Document Ontology (SocDOnt), we draw on concepts from graph theory to develop a method for the graphical visualization of social documents.Applying this method to analyze the social documents in an operational enterprise collaboration platform, we identify and display different types of social documents and define their characteristic structure.Our findings provide the necessary foundation for conducting computational ethnographies of collaborative work.
Application of SSR for Bridge Deflection Measurement by Using of Digital Image Correlation

Being different from the displacement and strain measurements in a laboratory environment, the bridge deflection measurement by using of digital image correlation is very impressionable due to the changes of ambient light. The illumination disturbances can result in an increase of deflection measurement errors or unreliable results. In order to decrease the influence of illumination disturbances on the results, the images were preprocessed by Single Scale Retinex. The experiments showed Single Scale Retinex could effectively decrease the influence of illumination disturbances on bridge deflection measurement. In a laboratory environment, SSR could decrease the systematic errors by more than 50% and the random errors by 30%-60%. In a field environment, the inhibitory effect of SSR on the changes of ambient light was not as obvious as the indoor test. But, the accuracy of bridge deflection measurement in field could be improved by using SSR for preprocessing.
Implementation of K-Means Clustering Algorithm in the Crime Data Set

Data mining is a most promising field to group the relative set of data. The data mining deals with many algorithms to discover interesting and useful pattern or to find new knowledge form the dataset. Clustering is the technique of grouping the dynamic data in to classes. K-.Means Clustering algorithms is one of the efficient clustering algorithm. In Data mining processing work, The first step include to collect raw data and process with key factors and to determine which algorithms is more suitable for the given data set. Criminology is the study of   identifying the crime characteristics. This paper gives an idea about implementation of K-means Algorithms for crime data set. It also takes part of crime analysis the exploration and finding crimes with criminals. The volume and complexity and their relationships of crime database is the challenge attributes. This will paper intended to create effective tool for police forces.
The Design of a Digital Behaviour Change Intervention for Third-Level Student Illicit Substance Use: A Persona Building Approach

Illicit substance use among third-level students is an issue of increasing concern.Digital behavioural change interventions have been developed to target this population, but reports of their effectiveness are mixed.The importance of end-user involvement in digital intervention development has been well established, but it appears that many interventions in this area did not engage end-users during development.This absence may have affected engagement, undermining their potential effectiveness.This paper describes the process and contributions of a persona-building approach in the development of a digital behaviour change intervention tailored to the needs of third-level students.Nine exploratory persona-building workshops were carried out with 31 students, and 7 project team members to develop personas for heavy, occasional and nonsubstance using third-level students.Early analysis has identified five archetypes which will contribute to the design of an acceptable and user-friendly intervention, and to the identification of targeted behavioural change techniques.
Verifiability Metric Notion in e-Voting System

Voting has become an important part of the democratic system. The voters are more and more widespread, the more complex the social life aspect, and the need to manage the voting process more efficiently and more quickly, electronic voting (e-voting) becomes a more promising choice. The voting confidence level highly depends on the ability of the system to protect voters' votes to the end of the process. Parameters in e-voting consist of accuracy, invulnerability, privacy, and verifiability. The verifiability aspect is one of the parameters in e-voting, so verifiability is the most dominant thing in increasing the confidence in polling technology. In this paper we propose a metric to improve verifiability in the e-voting system.
Are you ready to vlog? A newly sophisticated way to improve students’ speaking ability

Students find it hard to speak English despite the fact that they have the basic knowledge of the language due to the lack of motivation and confidence. As the technology advances, teachers must be willing to take advantage of the potential channels provided by it. This study aims to improve students’ motivation and confidence to speak English through the use of Vlog in the YouTube Channel as well as to find out the element that affect the most to the improvement of the motivation and confidence. The data were obtained through classroom observations, questionnaires and interviews. In terms of findings, this study revealed that the use of vlog in the YouTube Channel improved students’ motivation and confidence. The data also suggested that students were most motivated and confident to speak English because they were given enough time to think of what they wanted to talk about and it was done in the place they found familiar. Therefore, teachers are suggested to bring technology into the classrooms more often and adjust it to the situation where students find it convenient to speak up.
AUSPP: A universal short-read pre-processing package

There are many short-read aligners that can map short reads to a reference genome/sequence, and most of them can directly accept a FASTQ file as the input query file. However, the raw data usually need to be pre-processed. Few software programs specialize in pre-processing raw data generated by a variety of next-generation sequencing (NGS) technologies. Here, we present AUSPP, a Perl script-based pipeline for pre-processing and automatic mapping of NGS short reads. This pipeline encompasses quality control, adaptor trimming, collapsing of reads, structural RNA removal, length selection, read mapping, and normalized wiggle file creation. It facilitates the processing from raw data to genome mapping and is therefore a powerful tool for the steps before meta-analysis. Most importantly, since AUSPP has default processing pipeline settings for many types of NGS data, most of the time, users will simply need to provide the raw data and genome. AUSPP is portable and easy to install, and the source codes are freely available at https://github.com/highlei/AUSPP .
Applying Optimal Weight Combination in Hybrid Recommender Systems

We propose a method for learning weighting schemes in weighted hybrid recommender systems (RS) that is based on statistical forecast and portfolio theory.An RS predicts the future preference of a set of items for a user, and recommends the top items.A hybrid RS combines individual RS in making the predictions.To determine the weighting of individual RS, we learn so-called optimal weights from the covariance matrix of available error data of individual RS that minimize the error of a combined RS.We test the method on the well-known MovieLens 1M dataset, and, contrary to the "forecast combination puzzle", stating that a simple average (SA) weighting typically outperforms learned weights, the out-of-sample results show that the learned weights consistently outperform the individually best RS as well as an SA combination.