Reviewerly/exHarmony huggingface dataset:
Comment on “An Efficient ABE Scheme With Verifiable Outsourced Encryption and Decryption”

Recently in IEEE Access (DOI: 10.1109/ACCESS.2018.2890565), Li <i>et al.</i> proposed a secure outsourcing algorithm for modular exponentiation in one single untrusted server model and a new method of generating transformation keys. They claimed that their solution can securely outsource encryption and decryption to untrusted ESP (encryption service providers) and DSP (decryption service providers), leaving only a constant number of simple operations for the DO (data owner) and eligible users to perform locally. In addition, both DO and qualified users can check the correctness of the results returned from ESP and DSP, respectively. Although the authors provide security proofs for their scheme, unfortunately, after carefully observing their scheme, we find that the scheme has security vulnerabilities. These vulnerabilities allow the adversary to generate the sub-key for any attribute and replace ciphertext sub-item, which result in the adversary to be able to break their scheme. In response to this problem, we propose an improved solution and proved its security.
Restoration for blurred noisy images based on guided filtering and inverse filter

The development of complex life leads into a need using images in several fields, because these images degraded during capturing the image from mobiles, cameras and persons who do not have sufficient experience in capturing images. It was important using techniques differently to improve images and human perception as image enhancement and image restoration etc. In this paper, restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed. In the color images denoising process, it was very significant for improving the edge and texture information. Eliminating noise can be enhanced by the image quality. In this article, at first, The color images were taken. Then, random noise and blur were added to the images. Then, the noisy blurred image passed to the guided filtering to get on denoised image. Finally, an inverse filter applied to the blurred image by convolution an image with a mask and getting on the enhanced image. The results of this research illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure. Also, it enhanced the image and retained the edge details in the denoising process. PSNR and SSIM measures were more sensitive to Gaussian noise than blur.
Augmented Reality for Self-Organized Logistics in Distribution

We have developed an augmented-reality (AR) based system which keeps track of events during the parcel handling process for last-mile logistics. The system can retrieve and highlight in AR on user's smartphone, the location of parcels in large piles of parcels. A camera array automatically detects the parcels placed manually on the shelf by an operator. New parcels are scanned, and parcel fingerprints are generated semi-automatically. The system can detect and track the known parcels by fingerprint and can further highlight the location of the parcel using 3D visual clues, directly on the smartphone of the operator.
SOLAR PHOTOVOLTAIC SYSTEM

A solar PV System is a system which converts solar energy into electricity. PV gets its name from the process of converting light (photons) to electricity (voltage), which is called the photovoltaic effect. In solar PV system, solar inverter is used to convert the output from direct to alternating current. Solar PV system is very reliable and clean source of electricity which suit a varied range of applications. As the demand for solar electric systems grows, progressive builders are adding solar Photovoltaic (PV) as an option for their customers.
Home Kredit Integration System and Vehicle Insurance Policy Simas Insurtech at Sinar Mas Insurance

Web service API is meant for each platform to be connected and can be used for personal, group, or company needs. Web service API in the insurance world is used for insured parties to get their policy numbers and payment details for insurance purposes both for self-insurance and the property of the insured. By using a primary data collection method that is directly from the source, by using existing parameters in the procedure that was made previously to be able to get a valid policy number and is used by the insured as a form of cooperation with the Sinar Mas Insurance company. The results of the analysis are the number of policies that have been processed, will be used for the insured to make insurance claims on the insurance. This API has been made which aims to facilitate interaction between application platforms both web-based or mobile in data retrieval without regard to the platform used so that the activity of exchanging data between applications can run properly.
THE DILEMATIC ON-LINE ENGLISH LEARNING DURING THE COVID-19 PANDEMY IN RURAL AREA

Abstract: The government has made a decision of COVID-19 pandemic through the Minister of Education and Culture Circular No. 4 dated 24 March 2010 concerning the Implementation of Education Policy in the Emergency Period of Corona Virus Disease (COVID-19), namely learning from home. The problems are English language skills and students’ motivation. This study aims to describe English language skills and student motivation during the COVID-19 pandemic. The benefit is facilitating lecturers in online learning based on appropriate applications with students are in the rural area. This study uses a qualitative descriptive method by conducting the Monkey Survey application to collect and analyse data, it created and applied on May 29, 2020 and closed on May 31, 2020. After May 31, 2020 respondents could not open and fill out the questionnaire because the closed Application. The sample of this study was 35 students who studied English online by using different applications. The category is the education category and this application was modified on June 2, 2020. The result of his research is that online English Learning research during the COVID-19 pandemic is that the use of the WhatsApp, Facebook, YouTube and Google Classroom applications improve writing, listening and speaking skills and have no effect on reading skill, because reading skills can be improved by using the Schoology application. While the Schoology application cannot be used because of internet network limitations and quotas. Other findings are that online English language learning during the COVID-19 pandemic cannot increase student motivation.
An Efficient Threshold-Fuzzy-Based Algorithm for VM Consolidation in Cloud Datacenter

Cloud computing has grown exponentially in the recent years. Data growth is increasing day by day, which increases the demand for cloud storage, which leads to setting up cloud data centers. But they consume enormous amounts of power, use the resources inefficiently, and also violate service-level agreements. In this paper, an adaptive fuzzy-based VM selection algorithm (AFT_FS) is proposed to address these problems. The proposed algorithm uses four thresholds to detect overloaded host and fuzzy-based approach to select VM for migration. The algorithm is experimentally tested for real-world data, and the performance is compared with existing algorithms for various metrics. The simulation results testify to the proposed AFT_FS method is the utmost energy efficient and minimizes the SLA rate compared to other algorithms.
Predicted microscopic cortical brain images for optimal craniotomy positioning and visualisation

During a craniotomy, the skull is opened to allow surgeons to have access to the brain and perform the procedure. The position and size of this opening are chosen in a way to avoid critical structures, such as vessels, and facilitate the access to tumors. Planning the operation is done based on pre-operative images and does not account for intra-operative surgical events. We present a novel image-guided neurosurgical system to optimize the craniotomy opening. Using physics-based modeling we define a cortical deformation map that estimates the displacement field at candidate craniotomy locations. This deformation map is coupled with an image analogy algorithm that produces realistic synthetic images that can be used to predict both the geometry and the appearance of the brain surface before opening the skull. These images account for cortical vessel deformations that may occur after opening the skull and is rendered in a way that increases the surgeon's understanding and assimilation. Our method was tested retrospectively on patients data showing good results and demonstrating the feasibility of practical use of our system.
DEVELOPMENT OF 3-DIMENSIONAL SNAKE AND LADDER GAME: IMPLEMENTATION WITH SECOND GRADE STUDENTS OF SDN 37 PEKANBARU

This study aims to test the feasibility of a three-dimensional snake and ladder game to reflect the thematic learning material from theme 6 subthemes 2 about taking care of animals around me. This study was conducted in June 2020 in one of second grade parent house of SD N 37 Pekanbaru. This research is motivated by the lack of learning media that can be used to primary school students. The learning outside learning hours is important for students to be able to have leraning activities, working together in groups, respecting friends' opinions and interacting. The 3D snake and ladder game is an interesting game. The development of 3D snake and ladder game is carried out according to the Research and Development (R&amp;D) method according to the procedure of ADDIE development models. The feasibility test of 3D snake and ladder game product had validation rate of 87.5% from the media expert in the "very acceptable" category and 92.5% from the content expert in the "very acceptable" category. Student responses to the snake and ladder game developed as a whole scored 95.97% in the "excellent" category.
A MILP Model for a Byzantine Fault Tolerant Blockchain Consensus

Mixed-integer mathematical programming has been widely used to model and solve challenging optimization problems. One interesting feature of this technique is the ability to prove the optimality of the achieved solution, for many practical scenarios where a linear programming model can be devised. This paper explores its use to model very strong Byzantine adversaries, in the context of distributed consensus systems. In particular, we apply the proposed technique to find challenging adversarial conditions on a state-of-the-art blockchain consensus: the Neo dBFT. Neo Blockchain has been using the dBFT algorithm since its foundation, but, due to the complexity of the algorithm, it is challenging to devise definitive algebraic proofs that guarantee safety/liveness of the system (and adjust for every change proposed by the community). Core developers have to manually devise and explore possible adversarial attacks scenarios as an exhaustive task. The proposed multi-objective model is intended to assist the search of possible faulty scenario, which includes three objective functions that can be combined as a maximization problem for testing one-block finality or a minimization problem for ensuring liveness. Automated graphics help developers to visually observe attack conditions and to quickly find a solution. This paper proposes an exact adversarial model that explores current limits for practical blockchain consensus applications such as dBFT, with ideas that can also be extended to other decentralized ledger technologies.
Framework to prioritize digital transformation initiatives based on the country's development impact

Current public administration trends have raised the importance of putting the public values of the states' mission (social value and well-being) at the centre of the public management. Digital transformation of the states is a public initiative to adapt public processes of institutions to the new digital reality, and in this way, to adapt public processes of institutions to improve the generation of public values. As well as a public value is co-constructed by different institutions, the transformation of an institution may impact (positively or negatively) different public values. Unfortunately, digital transformation has not considered the impact (positive or negative) of the transformation of the process on all public values impacted by transformation, but motivation is put in specific focuses. In this paper, we propose a group decision-making framework to prioritize, as a group, all public values in order to obtain as an outcome, the prioritized list of digital transformation initiatives aligned to the development of the country. We present an illustrative example based on the National Forestry Corporation (CONAF), where we discuss what would be the most important initiatives if our proposal were used.
K-Means to Determine the e-commerce Sales Model in Indonesia

Sales of e-commerce in Indonesia are currently experiencing a pretty good increase by starting to switch sales that are done traditionally or offline to online sales using the use of internet media to facilitate trade transactions between sellers and buyers can be done anywhere and anytime. In this study using the K-means clustering method algorithm. The data source used in this study is based on sales using e-commerce based on provinces in Indonesia in 2018. The criteria used in the calculation are divided into 3 clusters, namely seller, reseller and dropship. Centroid data for cluster 1 = 68.30,33,58,4.95 namely Aceh, Riau, West Kalimantan, East Kalimantan, and Maluku. Centroid data for cluster 2 = 78.58,22,25.4,22 i.e. North Sumatra, West Sumatra, Jambi, Bangka Belitung Islands, D.K.I Jakarta, Central Java, East Java, Banten, Bali, Central Kalimantan, South Kalimantan, South Sulawesi and Papua  and Centroid data for cluster 3 = 86.36,13.34,4.38 namely South Sumatra, Bengkulu, Lampung, Riau Islands, West Java, D.I Jogyakarta, West Nusa Tenggara, East Nusa Tenggara, North Kalimantan, Central Sulawesi, Gorontalo, West Sulawesi, North Maluku and West Papua.
Disruptive Technologies and Impact on Industry- An Exploration

Disruptive technologies changed the way of doing business activities in our day to day life. It increased the business opportunities by using the internet. Today, these technologies have deeply penetrated different industries and their sub-sectors. Disruptive technology is the bunch of technologies which changed the way of life as well as it becomes the important part of all sectors of industry. Due to this lots of small-scale industries also grown up and got some new business opportunities along with new business market. Disruptive technologies increased the domain of customers which is much familiar with technologies and preferring it for other purposes also including business activities. In this paper we are going to discuss about the impact of disruptive technologies in different sectors of industry and how was that industry working or dealing before the innovation of the disruptive technologies.
Moths–Flame Optimization Algorithm

Moth–flame optimization (MFO) algorithm is a simple and easy to implement, nature-inspired, meta-heuristic algorithm. This chapter covers the introduction to moth flame optimization algorithm, steps for generating moths randomly within the neighbourhood or solution space and solved numerical example are provided. The fitness value for each moth is calculated and the best position obtained is tagged by flame. The updating process takes place after which the process is repeated until a point where criteria for termination is attained. The MFO model was implemented in MATLAB, to illustrate the approach, 30 search agents were considered (n = 30) and the maximum number of iterations were set to 100. The best solution obtained by MFO [1.7999 0.19996] and the best optimal value of the objective function found by MFO is 84. MFO algorithm can be used in diverse areas of operation for scheduling, estimation, simulation, and control.
Evaluation of Coreference Resolution Systems Under Adversarial Attacks

A substantial overlap of coreferent mentions in the CoNLL dataset magnifies the recent progress on coreference resolution.This is because the CoNLL benchmark fails to evaluate the ability of coreference resolvers that requires linking novel mentions unseen at train time.In this work, we create a new dataset based on CoNLL, which largely decreases mention overlaps in the entire dataset and exposes the limitations of published resolvers on two aspects-lexical inference ability and understanding of low-level orthographic noise.Our findings show (1) the requirements for embeddings, used in resolvers, and for coreference resolutions are, by design, in conflict and (2) adversarial approaches are sometimes not legitimate to mitigate the obstacles, as they may falsely introduce mention overlaps in adversarial training and test sets, thus inflating the performance.
Effective approach for area-based spaceborne SAR images registration using GLCM textural Features

Image registration is a very important step and an integral part of radargrammetry, interferometry, change detection, image fusion, etc. Because of noises and geometric challenges in synthetic aperture radar (SAR) images, the registration process is more complicated in these images in comparison to optical imagery. Moreover, one of the challenges in SAR image registration is to deal with weak textures. In this study, a multistep method was proposed for SAR image registration. In the proposed method, the use of grey level co-occurrence matrix (GLCM) textural features improved the output of regions with weak textures. The proposed method includes three main steps: first, as a pre-processing step, the speckle noise of SAR images was reduced through the refined Lee filter. Then, for each of the master and slave images, 10 GLCM textural features of original images were generated. Using each of the stereo textural feature images and Lucas-Kanade optical flow algorithms, one can determine the corresponding points. Finally, by considering some constraints, the coordinates of true matches were estimated. The precision of the proposed method was evaluated by the root mean square error (RMSE), Mean absolute error (MAE), and standard deviation (STD) criteria. Furthermore, the random sample consensus (RANSAC) -2D projective transformation method was used for accuracy evaluations. The results showed that the proposed method would generate more corresponding points compared to the two common registration methods, including template matching with normalized cross-correlation (NCC) and the traditional Lucas-Kanade optical flow. The proposed method improved the number of true matches up to 37% and 52% compared to the traditional LK and the template matching method, respectively.
Pix2Pitch: generating music from paintings by using conditionals GANs

Generative adversarial networks (GANs) (Goodfellow et al., 2014) have been extensively used to transform and create images or sounds in their own domains. But transformation between different modalities is a problem that hasn’t been so explored. This work proposes a method to generate sound from image, based on Pix2Pix architecture (Isola et al., 2017), a conditional GAN that was designed for general purpose image-to-image translation. In this work a new implementation that allows creating music from images has been developed. The main goal is to create music that describes specific paintings and to answer the question: How does that image sound?. This is an answer that blind people could find useful in several applications, like in museums. To do so it has been taken into account different thesis that posit that there is an interaction between visual art and music, also several works that study synesthetic experimentations. The process implies: first to label and pair images and sounds from different style and points in time, second extract common features from the data by exploring multiple methods for music feature extraction and third to introduce multimodal layers into the GAN. Finally, a method to create novel pieces of music by using the generated sound features has been implemented. As it will be presented in the state-of-the-art section, some advances in crossmodal generation have been achieved but most of them are focused on creating image from sound or image from text, but only a few explore image-to-sound transformations.
CONTEMPORARY EDUCATIONAL TECHNOLOGY AND TEACHING MEDIA IN HIGHER EDUCATION TEACHING DURING THE COVID-19 PANDEMIC

CONTEMPORARY EDUCATIONAL TECHNOLOGY AND TEACHING MEDIA IN HIGHER EDUCATION TEACHING DURING THE COVID-19 PANDEMIC
Quantifying Voter Biases in Online Platforms: An Instrumental Variable Approach

In content-based online platforms, use of aggregate user feedback (say, the sum of votes) is commonplace as the standard for measuring content quality. Use of vote aggregates, however, is at odds with the existing empirical literature, which suggests that voters are susceptible to different biases-reputation (e.g., of the poster), social influence (e.g., votes thus far), and position (e.g., answer position). Our goal is to quantify, in an observational setting, the degree of these biases in online platforms. Specifically, what are the causal effects of different impression signals-such as the reputation of the contributing user, aggregate vote thus far, and position of content-on a participant's vote on content? We adopt an instrumental variable (IV) framework to answer this question. We identify a set of candidate instruments, carefully analyze their validity, and then use the valid instruments to reveal the effects of the impression signals on votes. Our empirical study using log data from Stack Exchange websites shows that the bias estimates from our IV approach differ from the bias estimates from the ordinary least squares (OLS) method. In particular, OLS underestimates reputation bias (1.6-2.2x for gold badges) and position bias (up to 1.9x for the initial position) and overestimates social influence bias (1.8-2.3x for initial votes). The implications of our work include: redesigning user interface to avoid voter biases; making changes to platforms' policy to mitigate voter biases; detecting other forms of biases in online platforms.