Reviewerly/exHarmony huggingface dataset:
Prototype Pengembangan Buku Ajar Berbasis Mobile Learning pada Materi Menerapkan Pengelolaan Informasi Melalui Web Log (Blog) Kelas X OTKP SMK PGRI 13 Surabaya

The learning process requires teaching materials that can support learning in the classroom, be it modules or interactive teaching materials. Interactive teaching materials can use gadgets as a medium to support the learning process. Based on observations at SMK PGRI 13 Surabaya, the teacher only has a book handle and teaches with brwosing. This makes students passive in learning activities and can bring up lessons about the sophistication of various gadgets. Textbook base mobile learning are an effort to optimize the use of student smartphones so students become more independent and do not miss lessons. In this study using the ADDIE development model which has 5 stages, namely analysis; design; development; implementation; evaluation. The purpose of this study was to determine the development, and feasibility textbook base mobile learning. Based on the validation assessment, obtained 89.5% material validation experts which is the average of the number of validation assessments from the Office Administration Education Lecturer, Surabaya State University, and Surabaya PGRI 13 SMK teachers and 91.6% media validation from Curriculum and Education Technology Lecturers, Surabaya State University. This shows the media developed by researchers is categori as very feasible and can use in learning process.
Multi-lingual Author Profiling: Predicting Gender and Age from Tweets!

This article describes how we build a multi-lingual classification system for author profiling. We have used Twitter corpus for English, Dutch, Italian and Spanish languages for building different models incorporating SVM classifier that predicts the gender and age of an author. We evaluated each model using 3-fold cross-validation on the training dataset for each of these languages. The overall maximum average accuracy for gender classification was 81.3% for Spanish while for classification of age we achieved a maximum accuracy score of 70.3% for English using the cross-validation scheme. For other languages, the results were between 64–76%.
Online News Feed Data Mining and Prediction

Data mining and prediction systems have been the center of attraction since information retrieval came into existence. Most IT companies spend a lot of resources on such analysis and systems to improve their performance and generate more revenue depending on the nature of work that they do. Online News Feed Prediction System aims to provide an analysis and comparison of various prediction techniques by using different methods of implementation. UCI repository contains a collection of databases pertaining to different topics. News popularity in multiple social media is one such dataset containing information about news topics from different sources, sentiment analysis of title and headline, topic that they are related to, publishing date, popularity score in various social media platforms. Python, R and Weka have been used on this data set to implement data preprocessing, visualization and prediction techniques like Random Forest, Decision Tree and SVM. Moreover, there is dataset on the analysis of the score for every twenty minutes for the social media platforms chosen. Analysis on these platforms helps in developing a system to reach a wider audience. News agencies can use this system to increase their profit and visibility. This paper aims to realize the ways to obtain these results.
Research on 3D Modeling Software Maya Digital Media Animation Assisted Brain Surgery Technology

Abstract This article uses the three-dimensional (3D) modeling software Maya and the 3D interactive software Unity3D to develop an interactive teaching system that integrates the display of the human brain, the display of the principle pathology of cerebral thrombosis, and the simulation of stent surgery in the brain. The use of this system can result in unlimited learning by multiple people, avoiding shortcomings such as insufficient resources, and strengthening the characteristics of interaction. For human brain models, functions such as zoom-in, rotation, and observation of a single part can be implemented, and text descriptions are provided. For the demonstration of cerebral thrombosis, animation is provided for simulation. For intra-brain stent surgery, the system can automatically demonstrate the surgical process, and it can be actually operated by the user, thereby enhancing the convenience and fun of student learning. This system uses Unity3D software as the main development tool. It uses the software’s advanced lighting system, particle system, and algorithms such as edge detection and screen post effects to establish the main frame of the system. It utilizes C # language for programming, thus enriching the functions of the system. On testing, the virtual interactive teaching system realized by the above method exhibits high interactivity, fun, and practicability.
Agent prominence in the Polish -no/-to construction

The Polish -no/-to construction is an arb, i.e. a human impersonal with a similar meaning as the impersonal pronouns man in German or on in French or the 3pl impersonal in Russian. The common view that it can be formed from virtually all verbs as long as the referent is human is here contested by an acceptability judgement test. It shows that verbs assigning more agentivity features to the subject are significantly better than verbs assigning fewer agentivity features, and completely un-agentive verbs are just as bad as inanimate referents. This reveals a prominence relation. The details of the findings indicate that the -no/-to construction behaves in a similar but nonetheless different way from other arbs and that the list of agentivity features has to be revised in order to model the prominence relation exactly.
A New Era in Geoscience Data Interpretation - Artificial Intelligence Based Solutions

Summary A typical geoscience project is a sequence of actions for a detailed study of the field through an integral analysis of geological and geophysical information. The aim of this work is to minimize the geological risks of well drilling and increase the reliability of the geological prediction, as well as localize reserves and update their quality. Today, there are a large number of analytical approaches and algorithmic implementations for solving the problems of the above stages. Most of them have limitations due to the inability to fully automate processes when working in the complex geological environment, as well as with poor quality material. The authors of this paper have found a solution of these problems in related areas. At this moment, one of the top areas to gain scientific attention is theory of computer vision. Computer vision technologies are developing more and more actively in various fields and areas. A set of solutions is proposed in the work, based on attracting the capabilities of AI to solve the problems of automating the processes for tracking seismic horizons and faults, correlation of well sections, facies modeling.
A Novel Method of Image Denoising: New Variant of Block Matching and 3D

The demand of accurate and visually fair images is increasing with the passage of time and bang of the number of digital images especially in the domain of medical and healthcare systems. The visual image quality of modern cameras affected due to edges, textures and sharp structures noise. Though research community has introduced several techniques such as BM3D (Block Matching and 3D) for image denoising. However, edges and texture preservation capabilities remain issues due to hard thresholds values and captured image diversity. In order to address these issues, we propose a new variant of BM3D namely BM3DMA (Block Matching and 3D with Mahalanobis and Adaptive filter) which is employed through the use of Mahalanobis distance measure (for diversity coverage) and adaptive filter (for soft thresholds). We used two widely known datasets consist of set of standard and medical images. We observe 5% to 10% enhancement in the performance of BM3DMA as compared to BM3D in terms of improving the PSNR (Peak Signal to Noise Ratio) value. The promising experimental result indicates the effectiveness of BM3DMA in terms preserving the edge and texture image noise.
Research on Privacy Preserving Data Mining Based on Randomized Response

Data mining has played an active role in some deep-level applications, but at the same time, it also brings many problems in information security and privacy protection. The association rule mining algorithm based on randomized response (RR) protects private information to a certain extent. However, because all the disturbed data in the data interference strategy based on randomized response are directly related to the real original data, the effect of privacy protection is not obvious. Aiming at this problem, this paper proposes a more effective privacy protection method in association rule mining. Based on the existing association rule mining model, a suitable perturbation strategy is designed to reduce the interference between the perturbed data and the original data. Relevance, without affecting the accuracy of mining, further enhance the degree of privacy protection of the mechanism.
Bessel Smoothing and Multi-Distribution Property Estimation

We consider a basic problem in statistical learning: estimating properties of multiple discrete distributions. Denoting by $\Delta_k$ the standard simplex over $[k]:=\{0,1,\ldots, k\}$, a property of $d$ distributions is a mapping from $\Delta_k^d$ to $\mathbb R$. These properties include well-known distribution characteristics such as Shannon entropy and support size ($d=1$), and many important divergence measures between distributions ($d=2$). The primary problem being considered is to learn the property value of an $\emph{unknown}$ $d$-tuple of distributions from its sample. The study of such problems dates back to the works of Efron and Thisted (1976b); Thisted and Efron (1987); Good (1953b); Carlton (1969), and has been pushed forward steadily during the past decades. Surprisingly, before our work, the general landscape of this fundamental learning problem was insufficiently understood, and nearly all the existing results are for the special case $d\le 2$. \n\nOur first main result provides a near-linear-time computable algorithm that, given independent samples from any collection of distributions and for a broad class of multi-distribution properties, learns the property as well as the empirical plug-in estimator that uses samples with logarithmic-factor larger sizes. As a corollary of this, for any $\ve>0$ and fixed $d\in \mathbb Z^+$, a $d$-distribution property over $[k]$ that is Lipschitz and additively separable can be learned to an accuracy of $\ve$ using a sample of size $\mathcal{O}(k/(\ve^3\sqrt{\log k}))$, with high probability. Our second result addresses a closely related problem-- tolerant independence testing: One receives samples from the unknown joint and marginal distributions, and attempts to infer the $\ell_1$ distance between the joint distribution and the product distribution of the marginals. We show that this testing problem also admits a sample complexity sub-linear in the alphabet sizes, demonstrating the broad applicability of our approach.
SISTEM AUGMENTED REALITY (AR) DALAM DAKWAH ISLAM

The Use Of Mobile Technology In The Industrial 4.0 Era Is A Must. The Number Of Smartphone Users Continues To Increase Over Time. Contiinum Reality Technology Which Consists Of Both Virtual Reality (VR) And Augmented Reality (AR) Becomes A Portable Technology That Is Easy And Affordable Being Used With A Smartphone. This Study Has An Objective To Determine The Potential Uses Of Augmented Reality (AR) Technology In Modern Islamic Da'wa. This Study Are Counted As A Literature Study Using The Traditional Review Method.AR Applications Have Been Used In Many Fields, Such As Medical, Military, Manufacturing, Visualization, Entertainment And Games, Robotics, Education, Marketing, Navigation, Tourism, Geospatial, Urban Planning And Civil Sciences. Da’wa Requires Attention In Both Truth And Good Ability Of The Preachers. It’s Based On Legal Aspects And The Procedures (Fiqhud-Dakwah). There Was Limited Amount Of Research And AR Products Used In Da'wa. The AR’s Technology That Can Be Applied In Islamic Da'wa Are 1) Playing Cards For Da’wa, 2) Islamic Story Books, 3) Da’wa Brochures / Leaflets / Poster, 4) Islamic History Museum.
CRYPTCLOUD, SECURE AND EXPRESSIVE DATA ACCESS CONTROL FOR CLOUD STORAGE

The development of cloud computing and also its devotion to conserving financings are makingit crucial for businesses to assess whether cloud computing makes good feeling for each one of them. Tomanage using the smoke around cloud computing, these affiliations to start with the requirement to need toidentify the rudiments involving modern-day innovation. In this particular paper, our team has attended to theproblem of a credential leak in CP-ABE based cloud storage space system by designing a responsibleauthorization and also revocable CryptCloud which assists white-box traceability as well as bookkeeping(pertained to as CryptCloud+).
A Reparameterization-Invariant Flatness Measure for Deep Neural Networks

The performance of deep neural networks is often attributed to their automated, task-related feature construction. It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples. Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error. For several flatness measures, this correlation has been empirically validated. However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization due to a lack of invariance with respect to reparameterizations. We propose a natural modification of existing flatness measures that results in invariance to reparameterization.
A novel data deduplication scheme for encrypted cloud databases

As the demand for data sharing and complex access-control policies continues to grow, traditional encryption mechanisms, which are generally established using a Public Key Infrastructure, face the problem of massive processing overheads and huge network bandwidth consumption. Attribute-Based Encryption (ABE) schemes have been proposed as a potential means of addressing these issues and have attracted great attention in recent years. Most previous studies on ABE focus on issues such as the revocation mechanism, multi-authority, the access structure design, and traceability. However, very few studies consider the storage efficiency problem and the present study proposes a novel data deduplication scheme based on ciphertext-policy ABE with convergent encryption and block-level data. The scheme can be deployed in third-party semi-trusted environments, and not only provides flexible, fine-grained access control over encrypted data, but also allows for the in-line elimination of redundancies in order to save cloud storage space. The experimental results show that the proposed scheme has an acceptable computational overhead and provides a promising solution for real-world data cloud storage and access scenarios.
Texas A&amp;M Hackin’ Aggies’ Security Verification Strategies for the 2019 Hack@DAC Competition

This article details how well functional verification technique can catch security bugs in a resource-constrained setting.
More Personalized Learning by Reducing Latency in Grading

In this poster, I present initial results on boosting personalized learning with the help of real-time automated grading. The automated system assisted students to debug their code more efficiently and to learn more at their own pace. The instructor complemented the automated assessments with detailed code reviews based on each student's progress and needs. Given the positive feedback from students, I believe that educators can create new classroom environments filled with more personalized learning by reducing latency in grading.
Students' Perception Through Learning Deductive and Inductive in Basic Grammar

The inductive-deductive learning model is a learning model begins by giving examples with the aim that students can identify, differentiate, then interpret, generalize and finally draw conclusions. The objective of this study is to know the students perception to the application of Deductive and Inductive learning in Basic Grammar. The method that writer use is a qualitative research. Subject of the research are students at second semester of English Department. The result of the study showed there are 4% (2 students) answer strongly disagree, 12% (4 students) disagree, 21% (7 students) agree, 29% (10 students) agree and like this statement, andn 34% (11 students) strongly agree using deductive and inductive learning.n
A NEW METHOD OF FAST REGISTRATION OF UNMANNED AERIAL VEHICLE REMOTE SENSING IMAGES BASED-ON AN IMPROVED SURF ALGORITHM

Abstract. Remote sensing system fitted on UAV (Unmanned Aerial Vehicle) can obtain clear images and high-resolution aerial photographs. It has advantages of flexibility, convenience and ability to work full-time. However, there are some problems of UAV image such as small coverage area, large number, irregular overlap, etc. How to obtain a large regional map quickly becomes a major obstacle to UAV remote sensing application. In this paper, a new method of fast registration of UAV remote sensing images was proposed to meet the needs of practical application. This paper used Progressive Sample Consensus (PROSAC) algorithm to improve the matching accuracy by removed a large number of mismatching point pairs of remote sensing image registration based-on SURF (Speed Up Robust Feature) algorithm, and GPU (Graphic Processing Unit) was also used to accelerate the speed of improved SURF algorithm. Finally, geometric verification was used to achieve mosaic accuracy in survey area. The number of feature points obtained by using improved SURF based-on PROSAC algorithm was only 9.5% than that of SURF algorithm. Moreover, the accuracy rate of improved method was about 99.7%, while the accuracy rate of improved SURF algorithm was increased by 8% than SURF algorithm. Moreover, the improved running time of SURFGPU algorithm for UAV remote sensing image registration was a speed of around 16 times than SURF algorithm, and the image matching time had reached millisecond level. Thus, improved SURF algorithm had better matching accuracy and executing speed to meet the requirements of real-time and robustness in UAV remote sensing image registration.
Image Target Recognition via Mixed Feature-Based Joint Sparse Representation

An image target recognition approach based on mixed features and adaptive weighted joint sparse representation is proposed in this paper. This method is robust to the illumination variation, deformation, and rotation of the target image. It is a data-lightweight classification framework, which can recognize targets well with few training samples. First, Gabor wavelet transform and convolutional neural network (CNN) are used to extract the Gabor wavelet features and deep features of training samples and test samples, respectively. Then, the contribution weights of the Gabor wavelet feature vector and the deep feature vector are calculated. After adaptive weighted reconstruction, we can form the mixed features and obtain the training sample feature set and test sample feature set. Aiming at the high-dimensional problem of mixed features, we use principal component analysis (PCA) to reduce the dimensions. Lastly, the public features and private features of images are extracted from the training sample feature set so as to construct the joint feature dictionary. Based on joint feature dictionary, the sparse representation based classifier (SRC) is used to recognize the targets. The experiments on different datasets show that this approach is superior to some other advanced methods.
Comparison of Key Points Clouds of Images Using Intuitionistic Fuzzy Sets

The work is devoted to solving the problem of tracking objects with modern robotic systems. An algorithm for tracking objects based on the representation of images in the form of clouds of singular points using intuitionistic fuzzy sets is considered. The developed method takes into account the geometric structure of the tracked object. The truth of the found comparisons of the singular points of the images is evaluated by categories such as “not quite accurate”, “almost exactly”, “absolutely accurate”. The results of the experiments, as well as estimates of the stability and speed of the algorithm, demonstrate the high quality of the task of tracking objects in a video sequence.
TraCard: A Tool for Smart City Resource Management Based on Novel Framework Algorithm

Background: Fuel is the most important source of energy essential for humanity. It plays a vital role in the economy of a country. Dependency on non-renewable resources like petroleum brings about manipulation of the oil market and an increase in the price of these resources. Excessive use of non-renewable resources leads to global warming. These resources exist in a finite quantity in nature, which makes renewable resources appealing. Objective: Petroleum being a non-renewable energy mode requires monitoring and dynamic pricing based upon the consumption to reduce excessive usage. This dynamic price control on the basis of consumption developed upon the rules of Novel algorithm for resource allocation in the system will orient the end user to move towards developing alternate resources for power generation in the field of renewable energy like wind, hydro, solar, etc. This orientation is one of the key steps for developing a smart city with efficient resource allocation as the key factor of development. Methods: In order to manage resources efficiently, the following processes are incorporated. ➢ Provide End-user a unique ID card (Named as Tra-Card). ➢ Develop data of usage (Fuel, Health Resources, and Supply Chain Management) for initial days. ➢ Automated Management of Resources using Novel Framework Algorithm with fuzzy based stable marriage algorithm). ➢ Develop Customer Management Policy as per the criterion set by governments. ➢ Generate Data and rules for use at individual points of work. Result: The process will help the government make efficient policies and produce real time deliverables over complete control and handling of fuel management; the most important of human life need. This will also allow government’s to provide a solution for end to end charging by making fuel an item of luxury and taxing the people who use it as a luxury product. Discussion: Fuel being primary requisite for human survival needs strict monitoring for irregular usage and laundering. As, the fuel has become an item of luxury for few and item for survival of few “it must be taxed as per the usage” of end-user. The Data is experimental and developed using a multiplication factor α which is multiplied to the base fuel price once it crosses a certain range of Kilometer travelled by the user with his car. Conclusion: The model proposed in the paper captures the necessity of development of an efficient method that considers the finiteness of fossil fuels by monitoring the distribution of fuel and its consumption. The purpose of this project is to save energy with aim of AI Engine managed logistics and goal of creating Energy-Efficient survival of the human species.