Reviewerly/exHarmony huggingface dataset:
Fast multilayer color QR code decoder algorithm utilizing fuzzy technique

Color QR code is an active research topic. Most of the recent research focus on the decoding success rate and ignore the decoding speed. In this paper, we propose a fast multilayer color QR code decoder algorithm to decode an extended color QR code. The extended color QR code utilize color reference for the color recognition. The algorithm starts with the detection of the color QR code. This is followed by the calculation of the model size. Then, color reference selection from the extended QR code. Next, build a dynamic fuzzy membership, from the color reference set of the extended color QR code and fast color enhancement using the center color pixel for each model. After that, optioning monochrome color QR code by applying color de-multiplexing for the enhanced color QR code. We measure the color recovery speed and compared it with an existing work. The experiment shows using the proposed algorithm we got a decoding speed of 200% faster than the existing work.
Quantum communication complexity of distribution testing

The classical communication complexity of testing closeness of discrete distributions has recently been studied by Andoni, Malkin and Nosatzki (ICALP'19). In this problem, two players each receive $t$ samples from one distribution over $[n]$, and the goal is to decide whether their two distributions are equal, or are $\epsilon$-far apart in the $l_1$-distance. In the present paper we show that the quantum communication complexity of this problem is $\tilde{O}(n/(t\epsilon^2))$ qubits when the distributions have low $l_2$-norm, which gives a quadratic improvement over the classical communication complexity obtained by Andoni, Malkin and Nosatzki. We also obtain a matching lower bound by using the pattern matrix method. Let us stress that the samples received by each of the parties are classical, and it is only communication between them that is quantum. Our results thus give one setting where quantum protocols overcome classical protocols for a testing problem with purely classical samples.
Frequent Itemset based Model for Video Quality Assessment

Frequent pattern mining is traditional method for knowledge discovery. Machine learning and knowledge discovery are inspiring many engineering solutions. Emerging technologies in multimedia have prompted a demand for quality analysis and quantification of quality evaluation algorithms. Even though many video quality metrics are proposed to automatically evaluate the quality of video, individual quality metrics fail to comprehensively quantify the quality of video. Hence advanced objective video quality analysis aims to predict quality of video based on many individual quality metrics with the opinion score provided by the observer. Machine learning is helping the field of video quality assessment with its new dimensions. In this paper, association rule-based feature selection using apriori method for video quality assessment is implemented. Evaluation of the efficacy of the prediction models is done using accuracy and found that Naive Base classifier to predict video quality excellent results than with SVM(Support Vector Machine) and KNN(K Nearest Neighbourhood).
METODE CARD SORT DALAM MENINGKATKAN HASIL BELAJAR SISWA PADA MATA PELAJARAN TEMATIK DI SDN 7 RAJA TAHUN AJARAN 2019/2020

This research was conducted to see the process of student participation in the teaching and learning process, especially thematic learning. The purpose of writing this research is to find out the application of the Card Sort Method in class III Thematic lessons at SDN 7 Raja. The method used in this research is Classroom Action Research (CAR). The research subjects were third grade students for the 2019/2020 academic year, totaling 34 students. The instruments used in this research are observation sheets, interview sheets and documentation. Based on the results of tests carried out in cycle I to cycle II, there was an increase in student scores on thematic subjects.
Determinants of Positive Word of Mouth for a Contemporary Art Exhibition on Web-based Virtual Reality

We aims to examine the determinants of visitors’ positive word-of-mouth (WOM) in a web-based virtual reality contemporary art exhibition. We also examines the effects of 3 realms of experience (entertainment, esthetic, escapist) on emotional arousal, how this emotional arousal affects memory and positive word-of-mouth, how memory affects positive word-of-mouth and how age moderate all of the paths. We examined a total of 297 visitors and was conducted through an online survey focusing on Chinese users of ages 20-49. The analysis results showed that entertainment and esthetic have an effect on emotional arousal, but escapist did not. The results also showed emotional arousal has an effect on memory and positive WOM. Memory did not affect positive word-of-mouth. Finally, age has a moderate effect on all the paths, except for the path form escape towards emotional arousal and memory to positive word-of-mouth. The theoretical implications of this study are meaningful exhibition research. While, it also will be helpful to segment the web-based virtual reality art exhibition visitors by dividing into 3 groups (20s, 30s, 40s) and to provide marketing and operation strategies.
Variable selection for Gaussian process regression through a sparse projection

This article presents a new variable selection approach integrated with Gaussian process regression. We consider a sparse projection of input variables and a general stationary covariance model tha...
Digital Forensics Investigation for Attacks on Artificial Intelligence

Digital forensics is a branch of science that mainly focus on the recovery and investigation in digital devices. The term digital forensics were mostly used for computer forensics. [Artificial Intelligence abbreviated AI is family of computational models which deploy black box techniques amongst the family of Neural Networks for classification, prediction and optimization tasks for wide variety of applications such as Computer Vision, Medical Imaging, Natural Language Processing, Autonomous Vehicles, Robotics etc., Cyber-Data Science deals with class of problems where the computational models are tampered for intentional mal-effects such as Mal-ware Classifiers, Adversarial Attacks, Back-end attacks. Adversarial attacks (Induced attacks negatively impacting the computational efficiency of AI and hence seeking malicious outputs) limit the applications of artificial intelligence (AI) technologies in key security fields. Therefore, improving the robustness of AI systems against adversarial attacks is an essential process for further advancement of AI for secured systems and hence warrants applications of Digital Forensics towards Artificial Intelligence attacks and is quite a challenging task. Therefore, it is important that new research approaches are needed to be adopted to deal with these security threats. This research is aimed at investigating the Artificial Intelligence (AI) attacks that are “malicious by design”. It also deals with conceptualization of the problem and strategies for attacks on Artificial Intelligence (AI) using Digital Forensic tools. A specific class of problems in Adversarial attacks are tampering of Images for computational processing in applications of Digital Photography, Computer Vision, Pattern Recognition (Facial Mapping algorithms). State-of-the-art developments in forensics such as 1. Application of end-to-end Neural Network Training pipeline for image rendering and provenance analysis [4], 2. Deep-fake image analysis using frequency methods, wavelet analysis & tools like – Amped Authenticate [1-3], 3. Capsule networks for detecting forged images 4. Information transformation for Feature extraction via Image Forensic tools such as EXIF-SC, SpliceRadar, Noiseprint 5. Application of generative adversarial Networks (GAN) based models as anti-Image Forensics [6], will be studied in great detail and a new research approach will be designed incorporating these advancements for utility of Digital Forensics.  Below fig.1. represents graphical abstract for DFI on AI Attacks.
Penerapan Metode Tilawati Dengan Strategi Mnemonic Dalam Meningkatkan Baca Tulis Al-Qur’an Melalui Media Whatsapp

This article was written to describe the application of the tilawati method and Mnemonic strategies in learning to read and write the Qur'an with the WhatsApp application media in the learning process of reading and writing the Qur'an at Madrasah Diniyah Nurus Salam, where with these methods and strategies students can more easily understand read and write the Qur'an, and can find out the fluency of reading and writing the Qur'an of students. By utilizing the latest technology. This research is a descriptive qualitative research with observation data collection techniques . The subjects in this study were teachers and students of grade 1 Madrasah diniyah Nurus Salam, totaling 22 students, precisely in the village of Sindetlami, Gundal hamlet. The author describes how these methods and strategies work in learning to read and write the Qur'an with WhatsApp media so that they can help educators in teaching their students even though in different distances and in different ways. And the research results obtained include 1. The level of student understanding is easier by applying the Tilawati and Mnemonic methods by using the WhatApp feature, 2. The level of fluency is quite good with the use of the WhatApp feature, 3. Utilization of technology media in distance learning.
Infant Mortality Case: An Application of Negative Binomial Regression in order to Overcome Overdispersion in Poisson Regression

Infant mortality is an indicator to determine the degree of public health. Infant mortality is death that occurs in the period from birth to before the age of one. The high rate of infant mortality indicates that the quality of public health services is not optimal. The number of infant deaths is an example of count data that follows a Poisson distribution, so it can be analyzed using Poisson Regression. The assumption that must be met when using this method is the equidispersion or variance of the response variable is equal to mean. However, this condition rarely occurs because usually the counted data has a greater variance than the mean or it is called overdispersion. One way to solve this problem is to use the Negative Binomial Regression method. The data used in this study is the case of infant mortality in the city of Padang. First, we model the data using Poisson Regression, then we check the assumption, if there is overdispersion, we handle it by modeling the data with Negative Binomial Regression. The results showed that the equidispersion assumption could not be met so that the data was modeled with Negative Binomial Regression.
Learning To Track With Object Permanence

Tracking by detection, the dominant approach for online multi-object tracking, alternates between localization and re-identification steps. As a result, it strongly depends on the quality of instantaneous observations, often failing when objects are not fully visible. In contrast, tracking in humans is underlined by the notion of object permanence: once an object is recognized, we are aware of its physical existence and can approximately localize it even under full occlusions. In this work, we introduce an end-to-end trainable approach for joint object detection and tracking that is capable of such reasoning. We build on top of the recent CenterTrack architecture, which takes pairs of frames as input, and extend it to videos of arbitrary length. To this end, we augment the model with a spatio-temporal, recurrent memory module, allowing it to reason about object locations and identities in the current frame using all the previous history. It is, however, not obvious how to train such an approach. We study this question on a new, large-scale, synthetic dataset for multi-object tracking, which provides ground truth annotations for invisible objects, and propose several approaches for supervising tracking behind occlusions. Our model, trained jointly on synthetic and real data, outperforms the state of the art on KITTI, and MOT17 datasets thanks to its robustness to occlusions.
An Introduction to Computing (For All BS Degree Program students & Beginners in All Fields)

A computer is a very complex machine comprised of numerous parts, cables, and devices that all need to properly work together in order for the computer to operate correctly. Similarly, computer networks have had a profound effect on the way we communicate with each other. And in this introductory book, we have considered database fundamental definitions which are mandatory for all. It will motivate all the learners for knowing about how to develop and maintain databases that meet user requirements.Windows Operating System, Microsoft Word, Excel, and PowerPoint are brilliant tools with many applications that are helpful to students and teachers in education. In this introductory Book, different screenshots are taken and presented for learners to enhance their practical skill, so that they can use each and every feature efficiently and effectively. The lessons were designed to help you understand and use Microsoft Word, Excel and PowerPoint to the fullest potential of the program.
The Object and its Significance in Crimes of Looting Other’s Property by Using Computer Tools

It should be noted that some types of plundering can damage not only property, but also other social relations protected by criminal law, that is, such crimes affect several social relations at once. In criminal law, crimes of this type are called crimes of a complex nature if the damage is caused to more than one object as a result of the commission of a socially dangerous act.The crime of looting other people's property by means of computers is also a crime of a complex nature, ie not only property relations, but also social relations in the field of information technology, and in some cases social relations in the field of public administration. will also be damaged.As a rule, in complex crimes, the object is qualified depending on the main direct object, if the objects affected are two or more social relations protected by criminal law.In robberies of other people's property using computer tools in most cases, the object of the crime is not in the direct possession of the victim, ie non-cash money is usually kept in a special institution (bank) or device (plastic card) where the money is stored and authorized to carry out certain operations.
HI-CMLM: Improve CMLM with Hybrid Decoder Input

Mask-predict CMLM (Ghazvininejad et al.,2019) has achieved stunning performance among non-autoregressive NMT models, but we find that the mechanism of predicting all of the target words only depending on the hidden state of [MASK] is not effective and efficient in initial iterations of refinement, resulting in ungrammatical repetitions and slow convergence. In this work, we mitigate this problem by combining copied source with embeddings of [MASK] in decoder. Notably. it’s not a straightforward copying that is shown to be useless, but a novel heuristic hybrid strategy — fence-mask. Experimental results show that it gains consistent boosts on both WMT14 En<->De and WMT16 En<->Ro corpus by 0.5 BLEU on average, and 1 BLEU for less-informative short sentences. This reveals that incorporating additional information by proper strategies is beneficial to improve CMLM, particularly translation quality of short texts and speeding up early-stage convergence.
Penggunaan Media Audio Visual Untuk Meningkatkan Prestasi Belajar Akidah Akhlak Siswa: Indonesia

This study aims to improve student achievement through the use of audio-visual media. This research was applied to fifth-grade students of Madrasah Ibtidaiyah Negeri 4 Muaro Jambi in the first semester of the 2019/2020 school year. This research is classroom action research or Classroom Action Research with a cycle design. Data collection techniques using observation, tests, and documentation. Data were analyzed through descriptive qualitative data analysis was carried out on each data collected, both quantitative and qualitative data. The results showed that learning by using audio-visual media can improve students' learning achievement of moral aqidah. Student learning achievement increased from cycle I to cycle II. It can be concluded that the results of student learning activities in cycle I reached 62% and increased in cycle II to 88%. The results of student attitudes in the first cycle reached 72% but increased in the second cycle reaching 85.5%. This is evidenced by the student achievement scores at the end of the first cycle obtained an average of 69,54% with the category close to completion and student achievement scores increased in the second cycle obtained an average of 91.36% with the complete category.
UPAYA MENINGKATKAN KEMAMPUAN MEMBACA ANAK KELOMPOK B MELALUI PERMAINAN KARTU HURUF DI PENDIDIKAN ANAK USIA DINI YABISAH SUBANG JAWA BARAT

The background of this research is that Reading in the learning process plays a very important role. Reading is the main means for a child to hone his curiosity. Children who have good reading skills generally have good abilities in expressing thoughts, feelings and interactive actions with their environment. Therefore, the development of children's reading skills in the learning process must receive serious attention for educators (especially teachers and parents or families). The development of children's reading ability can be observed through the ability to tell stories, converse, read poetry, sing and so on, all of which can be obtained from various sources either through reading material, told by others or hear mass media broadcasts either by radio or television. Efforts to develop children's language skills in Group B Early childhood education programs Yabisah Academic Year 2017/2018. Research conducted by researchers in writing this essay is expected to provide many benefits, both for the writer, for Subang Yabisah Early childhood education programs West Java, as well as for readers. This study uses Classroom Action Research. The subjects of this study were the 2017/2018 Academic Year Yabisah Early childhood education programs group of 15 children. Classroom Action Research is conducted in two cycles, where each cycle consists of four stages, namely planning, implementation, observation and reflection. Increasing intelligence through playing Efforts to Improve the Reading Ability of the Beginning of Group B Children through the Letter Card Game in the 2017/2018 Academic Year of Yabisah. In this study indicated by the percentage in the first cycle that is 70.66% of children in the good category and because of the improvement of some shortcomings of the first cycle, the cycle II value increased to 82.07%. It can be concluded that the Efforts to Improve the Reading Ability of the Beginning of Group B Children through Letter Card Games can develop children's intelligence in the Yabisah Early childhood education programs 2017/2018 Academic Year. Suggestions from the results of this study are that early childhood educators should apply interesting and enjoyable learning methods to children to improve their early childhood reading skills.
Study of the Electrical Characteristics of a Solar Panel for Multi-Residential Apartments Using a Computerized Measuring Stand ‘Eph 2 Advanced Photovoltaic Trainer’

In this paper, these parameters are evaluated based on the results of a study of the voltage characteristic of a solar panel obtained in automatic mode using a computer and a digital measuring device “EPH 2 Advanced photovoltaic trainer”. In order to determine the prospects of using a solar panel as the basis for creating a charger, you need to know a number of parameters: short-circuit current, no-load voltage, efficiency, etc.
3DIAS: 3D Shape Reconstruction With Implicit Algebraic Surfaces

3D Shape representation has substantial effects on 3D shape reconstruction. Primitive-based representations approximate a 3D shape mainly by a set of simple implicit primitives, but the low geometrical complexity of the primitives limits the shape resolution. Moreover, setting a sufficient number of primitives for an arbitrary shape is challenging. To overcome these issues, we propose a constrained implicit algebraic surface as the primitive with few learnable coefficients and higher geometrical complexities and a deep neural network to produce these primitives. Our experiments demonstrate the superiorities of our method in terms of representation power compared to the state-of-the-art methods in single RGB image 3D shape reconstruction. Furthermore, we show that our method can semantically learn segments of 3D shapes in an unsupervised manner. The code is publicly available from this https URL .
Comparative Analysis of various cryptographic algorithms on the basis of time and block size

Data encryption is the process of protecting information. It protects its availability, privacy and integrity. To write this article we have study about information security using cryptography technique. After the analyzing different techniques of encryption, we are proposing Advance Encryption Standard (AES). The AES has the better security compared others encryption algorithm and prevent data from Spoofing. It is very efficient in both hardware and software.
Functional Safety-based Smart Factory Message Queuing Telemetry Transport Communication System Development

Purpose: Reliability and safety-based message queuing telemetry transport (MQTT) communication applications and artificial intelligence (AI)-smart sensor development are considered in the smart factory to ensure competitiveness by minimizing risks in manufacturing, pre-failure diagnosis, and failure prediction prognostics and health management (PHM) technology.<BR>Methods: The algorithm was implemented to ensure that the MQTT Publisher software technology application conforms to the standard by applying it as a process design method in the smart factory. In the functional safety standard and reliability prediction methods, safety risk data were collected, analyzed, and verified as an evaluation procedure in the smart factory.<BR>Results: The performance of MQTT communication as well as the safety data collection and database (DB) storage functions were confirmed in the smart factory. The contents of the literature search were confirmed in relation to the evaluation of the reliability feature extraction factors and functional safety-related failure prognosis parameters of the AI-smart sensor, center control server, monitoring-related design, and development products to be used as the basis for reliability and safety in smart factories.<BR>Conclusion: The process of sensor input using Arduino, the transmission of measured values, and storage of collected data into a DB using the resistive temperature device was developed as a software algorithm and demonstrated in the smart factory using MQTT communication. According to a literature search related to priority-based MQTT, this partial application can be effectively used in small and medium-sized smart factories that require a real-time priority algorithm. It can be used as a price-competitive communication method.