Reviewerly/exHarmony huggingface dataset:
Multi Layered Approach based on Real Time Data: Hybrid Approach to deal with Credit Card Frauds

Data breach is a common problem in the current age of digitalization and paperless transactions. Banking and payment industries are preferred targets for this type of cyber-crime. Both rule based fraud-monitoring systems and predictive modelling systems have failed to provide a fool-proof solution to the problem owing to their respective limitations in its approach and manner of institutional handling of such cases of frauds. This paper proposes a hybrid approach to solve the problems posed by various models and techniques. A comparative analysis of models proposes a loan-blend method addressing the issues. It proposes a methodology of detecting data crimes in Credit Card industry. It compares and contrasts various machine learning algorithms in terms of their accuracy and effectiveness in combating the same. Real time data-sets are used for modelling and testing different algorithms in it and validating the model using confusion matrix. A functional architecture is undertaken for exploration of its uniqueness, relevance and implications.
The State Space Subdivision Filter for Estimation on SE(2)

The SE(2) domain can be used to describe the position and orientation of objects in planar scenarios and is inherently nonlinear due to the periodicity of the angle. We present a novel filter that involves splitting up the joint density into a (marginalized) density for the periodic part and a conditional density for the linear part. We subdivide the state space along the periodic dimension and describe each part of the state space using the parameters of a Gaussian and a grid value, which is the function value of the marginalized density for the periodic part at the center of the respective area. By using the grid values as weighting factors for the Gaussians along the linear dimensions, we can approximate functions on the SE(2) domain with correlated position and orientation. Based on this representation, we interweave a grid filter with a Kalman filter to obtain a filter that can take different numbers of parameters and is in the same complexity class as a grid filter for circular domains. We thoroughly compared the filters with other state-of-the-art filters in a simulated tracking scenario. With only little run time, our filter outperformed an unscented Kalman filter for manifolds and a progressive filter based on dual quaternions. Our filter also yielded more accurate results than a particle filter using one million particles while being faster by over an order of magnitude.
On the Modulus in Matching Vector Codes

Abstract A $k$-query locally decodable code (LDC) $C$ allows one to encode any $n$-symbol message $x$ as a codeword $C(x)$ of $N$ symbols such that each symbol of $x$ can be recovered by looking at $k$ symbols of $C(x)$, even if a constant fraction of $C(x)$ has been corrupted. Currently, the best known LDCs are matching vector codes (MVCs). A modulus $m=p_1^{\alpha _1}p_2^{\alpha _2}\cdots p_r^{\alpha _r}$ may result in an MVC with $k\leq 2^r$ and $N=\exp (\exp (O((\log n)^{1-1/r} (\log \log n)^{1/r})))$. The $m$ is good if it is possible to have $k&amp;lt;2^r$. The good numbers yield more efficient MVCs. Prior to this work, there are only finitely many good numbers. All of them were obtained via computer search and have the form $m=p_1p_2$. In this paper, we study good numbers of the form $m=p_1^{\alpha _1}p_2^{\alpha _2}$. We show that if $m=p_1^{\alpha _1}p_2^{\alpha _2}$ is good, then any multiple of $m$ of the form $p_1^{\beta _1}p_2^{\beta _2}$ must be good as well. Given a good number $m=p_1^{\alpha _1}p_2^{\alpha _2}$, we show an explicit method of obtaining smaller good numbers that have the same prime divisors. Our approach yields infinitely many new good numbers.
A study on the training mode of foreign language talenets at universities in the new era

Literary works are portrayal and reflection of social life, and literary masterpieces, as precious spiritual wealth, have made outstanding contributions to human civilization. Due to rich cultural background knowledge in literary works, the teaching based on literary works can greatly broaden students
Transfer Learning with Deep Convolutional Neural Networks in Forensic Face Sketch Recognition

Over the last few years, there has been tremendous progress in the field of artificial intelligence, and crime rates have risen at an alarming rate. As a result, the demand for systems that can identify, detect, and recognise suspects from sketches of their faces has grown. Some automatic methods for identifying suspects from sketches have been developed before, but their efficiencies are insufficient for both real-world and software-generated sketches. Face sketch to photo-realistic picture conversion using Deep Convolutional Neural Network model and face identification using pre-trained VGG-Face model by applying transfer learning are shown in this paper. Convolutional neural networks are used because of their vast data processing power and hierarchical feature representations. The algorithm outperformed existing methods in face sketch recognition, recognising the identity of people from sketches with an average accuracy of 0.98. This method could be extremely beneficial in forensic investigation for identifying suspects.
Modeling of Crater Group Representation Based on V-System

The crater group is one of the obviously geological signs on the planetary research and reveals the information of planetary surfaces. The comparison of crater groups can reflect the difference in the surface physical factors, and the modeling of representation characteristics is meaningful for studying crater groups. This article constructs a representation model by crater groups’ contours and geological properties. First, the interested crater group contours are selected from the studying area. Then, the spectrum of crater group contours is obtained by using the V-system of degree 3 that consists of various complete orthogonal functions. Ultimately, the geological size and the number of recorded craters are integrated into the representation model as the key parameters. The experimental results show that the representation characteristic can distinguish different crater groups and give them reasonable characteristic indices. In addition, the proposed model is tested in the representation performance and invariance of graph transform, the differences in the modeling scheme of different geological parameters and basis descriptor functions are discussed, and the application scenes and fields of the representation model are also elaborated.
KnowMAN: Weakly Supervised Multinomial Adversarial Networks

The absence of labeled data for training neural models is often addressed by leveraging knowledge about the specific task, resulting in heuristic but noisy labels.The knowledge is captured in labeling functions, which detect certain regularities or patterns in the training samples and annotate corresponding labels for training.This process of weakly supervised training may result in an over-reliance on the signals captured by the labeling functions and hinder models to exploit other signals or to generalize well.We propose KnowMAN, an adversarial scheme that enables to control influence of signals associated with specific labeling functions.KnowMAN forces the network to learn representations that are invariant to those signals and to pick up other signals that are more generally associated with an output label.KnowMAN strongly improves results compared to direct weakly supervised learning with a pre-trained transformer language model and a feature-based baseline.
ANALISIS BENEISH M-SCORE UNTUK MENDETEKSI FINANCIAL STATEMENT FRAUD PADA PT. GARUDA INDONESIA Tbk PERIODE 2017-2019

This study aims to detect financial statement fraud at PT. Garuda Indonesia in 2017-2019 used the Beneish M Score model and testing whether the Beneish M Score model can detect the occurrence of financial statement fraud at PT. Garuda Indonesia in 2018. The results of this study indicate that PT Garuda Indonesia was classified in the Gray criteria, that can be interpreted as manipulate or not manipule financial statements in 2017 and 2018 and classified in the criteria not manipulate financial statements in 2019. The results of this study also found that the Beneish M Score model could not accurately to detect the manipulation of financial statements at PT. Garuda Indonesia in 2018, because only two ratios are Gross Margin Index (GMI) and Days Sales in Receivable Index (DSRI) which detect manipulation of financial statements, so they are only classified in the gray area criteria
Perancangan dan Pengembangan Vıdeo Company Profıle Vıhara Dharma Mulıa Batam

In this article, we will discuss the design and manufacture of video company profile in the city of Batam called Vihara Dhrama Mulia Batam. The purpose of this society service is to promote the environment and activities of Vihara Dharma Mulia Batam through a platform called Youtube, because there is no promotion of Vihara Dharma Mulia Batam in the form of a company profile video, so it is still not very well known among the wider community. Therefore, the author decided to make video company profile using the Multimedia Development Life Cycle (MDLC) method. Vihara Dharma Mulia Batam company profile video design using Adobe Premiere Pro 2020 and Adobe After Effects software, with a duration of four minutes and forty-two seconds, 1920x1080 resolution using a laptop with Intel® Core™ i7-7700HQ, GTX 950M, and 16GB RAM specifications The result of this scientific project is an increase in popularity and the introduction of Vihara Dharma Mulia Batam
Improvement of Image Compression Performance by Deep Neural Networks

This paper analyses the deep image compression system that contains an encoder, quantizer, entropy model and decoder optimized by a joint rate-distortion framework. The current model implements a channel-level variable quantization network to dynamically allocate and withdraw the bitrates from significant and negligible channels. Its main specific is the usage of the variable quantization controller that consists of such components: channel importance module that dynamically learns the importance of channels during the training, and splitting-merging module, which allocates different bitrates of the channels. Quantizer implements the Gaussian mixture model manner. The paper is a continuation of several similar research done before that first provided an idea and architecture of the model. The main goals of the proposed work are to do a deeper analysis of the system, verify and improve the model effectiveness. The experiments validate that the hyper-parameter tuning approach proposed here successfully improves the rate-distortion performance of image compression in terms of various quality metrics such as PSNR, MS-SSIM and BPP.
Improving Real Age Estimation from Apparent Age Data

Recent advancements in facial analysis have contributed to the proliferation of applications that use technology for automatic face recognition, detection, and the extraction of useful information. In this context, the problem of automatically estimating the chronological (real) age of a person, given a picture of her face, emerges as a current hot topic. Although very popular across a wide range of applications, the problem remains challenging, especially due to the high variability of the aging process in humans. In this work, we introduce novel techniques that leverage apparent age estimation data to improve real age estimation. The estimation of apparent age is a related problem that aims to guess “how old does a person look like” rather than focusing on the real age. Given a dataset of face images, in which images are annotated with both real and apparent ages, we developed ways to combine statistical methods and convolutional neural networks to improve the current state-of-the-art on real age estimation. Our proposed methods are able to achieve an average MAE (mean absolute error) of 4.847, outperforming at least by 8.5% state-of-the-art methods when experimenting with the publicly available APPA-REAL dataset.
Desıgn and Manufacture of Graphıc Desıgn & Motıon Graphıc at Coffee Shop Aftertaste

In this article, we will discuss the design and manufacture of graphic & motion graphic designs, in a medium, small, micro-business (MSME) in the city of Batam called coffee shop aftertaste. The purpose of this scientific project is to promote brand image through digital media because there is not enough promotion in coffee shop Aftertaste digital media so that it is still not well known among the wider community. Therefore, the author decided to make graphic & motion graphic designs in the form of videos, posters, character designs, and also coupon designs using the Multimedia Development Life Cycle (MDLC) method. The result of this scientific project is an increase in popularity and the introduction of branding through Instagram stories
T-ReQs: A Tool for Tracking Similarity in Software ReQuirements

Finding similar requirements from a requirements dataset is an important problem in requirements engineering (RE). It provides requirements engineers an intuition of what could be related or how the requirements search space can be reduced for further RE activities. In this paper, we describe T-ReQs, a requirements similarity tracking tool based on cosine similarity. We discuss the key features and functionalities of the tool and its potential improvements for future work.
Developing a Scale for Subjective Easiness: Using Pullover Exercise as an Example

The purpose of this study is to develop a scale to evaluate subjective easiness in instructing the pullover exercise. We have questionnaires corresponding visual instructions, verbal instructions and assist methods. We include “easiness” subjectively felt by the examinees as the possible answers.
Detector-Free Weakly Supervised Grounding by Separation.

Nowadays, there is an abundance of data involving images and surrounding free-form text weakly corresponding to those images. Weakly Supervised phrase-Grounding (WSG) deals with the task of using this data to learn to localize (or to ground) arbitrary text phrases in images without any additional annotations. However, most recent SotA methods for WSG assume the existence of a pre-trained object detector, relying on it to produce the ROIs for localization. In this work, we focus on the task of Detector-Free WSG (DF-WSG) to solve WSG without relying on a pre-trained detector. We directly learn everything from the images and associated free-form text pairs, thus potentially gaining an advantage on the categories unsupported by the detector. The key idea behind our proposed Grounding by Separation (GbS) method is synthesizing `text to image-regions' associations by random alpha-blending of arbitrary image pairs and using the corresponding texts of the pair as conditions to recover the alpha map from the blended image via a segmentation network. At test time, this allows using the query phrase as a condition for a non-blended query image, thus interpreting the test image as a composition of a region corresponding to the phrase and the complement region. Using this approach we demonstrate a significant accuracy improvement, of up to $8.5\%$ over previous DF-WSG SotA, for a range of benchmarks including Flickr30K, Visual Genome, and ReferIt, as well as a significant complementary improvement (above $7\%$) over the detector-based approaches for WSG.
Visualizing sharia law: an information retrieval approach based on key extraction algorithm

Islamic jurisprudence as it relates to every element of Muslim life that founds in Sharia. Many interpretations exist for Sharia, all based on differing Islamic schools of thought and multiple laws concerning Sharia. In Malaysia, there are 135 sections of Sharia law related to family and marriage in Act 303. The survey conducted proves that Malaysians struggled when looking for sharia law and took a toll of time. Therefore, we propose a web-based Sharia Law Finder (SLF) system that solves the sharia law’s information retrieval issues in Malaysia. The SLF adapted the key extraction (KE) algorithm, which is Term Frequency-Inverse Document Frequency (TF-IDF), and Rapid Automatic Keyword Extraction (RAKE). The testing for implementation of both algorithm effectiveness is using the functional and usability test. The solution enhances using visualization tools which are bubble charts and word cloud. Bubble charts visualize the related Sharia law based on users’ queries and word cloud to visualize keywords used by the users. A total of 30 respondents have tested the functionality and usability of SLF. As a result, the system successfully works as specified functionality, 96.58% for the System Usability Scale, indicating the proposed solution’s acceptance.
Toward Spatially Unbiased Generative Models

Recent image generation models show remarkable generation performance. However, they mirror strong location preference in datasets, which we call spatial bias. Therefore, generators render poor samples at unseen locations and scales. We argue that the generators rely on their implicit positional encoding to render spatial content. From our observations, the generator's implicit positional encoding is translation-variant, making the generator spatially biased. To address this issue, we propose injecting explicit positional encoding at each scale of the generator. By learning the spatially unbiased generator, we facilitate the robust use of generators in multiple tasks, such as GAN inversion, multi-scale generation, generation of arbitrary sizes and aspect ratios. Furthermore, we show that our method can also be applied to denoising diffusion probabilistic models.
Supply chain management and blockchain: bridging the antecedents of the technology with the <i>status quo</i> of use case applications

Blockchain technology has been characterised as a disruptive technology in multiple business contexts, including supply chain and logistics management. However, being at the centre of the academic ...
A Survey on Efficient Realization of Activation Functions of Artificial Neural Network

Now a day's most of the applications utilize services of Artificial Neural Networks (ANN). Due to overwhelming adoption of ANN based technologies in practical applications, a lot of research has been done for implementation of ANNs on Hardware. Implementing an ANN on hardware is considered as a complex and challenging task. One of these challenges is the design of the neuron's activation function module. Several researchers of the domain have proposed various methods for implementation of activation function (AF). Generally, hardware implementation of a nonlinear AF is done by one of the approximation or LUT based methods. The present article briefly discuss both of major approaches and subsequent developments in methods employed for hardware implementation of such AF. Also, a discussion related to the hardware architecture and associated Hardware, time complexities has been done.
3D Printing, the Maker Movement, IP Litigation and Legal Reform

3D printing is a field of technology, which relies upon additive manufacturing (as opposed to traditional subtractive manufacturing). 3D printing has also been associated with the Maker Movement – a social movement focused upon developing and sharing design files. The field of 3D printing is currently undergoing a transitional phase. The consumer 3D printing revolution – which had hoped that there would be a 3D printer every home - has been a disappointment. The pioneering home 3D printing company MakerBot was embroiled in a number of controversies over its changing approach to intellectual property, resulting in disenchantment with the open source maker community, and alienation from its user-base.. Bre Pettis – the former head of MakerBot – reflected in an interview: ‘The open-source community cast us out of heaven.’ In the end, MakerBot was taken over by the leading 3D printing Stratsys, and restructured and repurposed. A number of other key companies became insolvent. TechShop, a chain of membership-based, open-access, do-it-yourself workshop and fabrication studios, went into bankruptcy. Maker Media – which runs Make Magazine and a couple of United States maker festivals – went into administration. Dale Dougherty, founder of Make Magazine has sought to revive the venture with Make Community LLC. Nonetheless, while personal 3D printing has not developed as anticipated, there has been a rise in a number of other forms and modes of 3D printing. Industrial 3D printing – along with robotics and Big Data – has become integrated into advanced manufacturing. Information technology and design companies have sought to improve the applications of 3D printing. Metal 3D printing has attracted significant investment – particularly from transportation companies. There has also been much experimentation with health applications of 3D printing – such as dental 3D printing, medical 3D printing, and bioprinting. As the technology has matured and advanced, there have been a number of early pieces of litigation and some policy developments in respect of 3D printing regulation. Our recent book 3D Printing and Beyond explores some of the key developments in intellectual property (IP) and 3D printing. In particular, it investigates 3D printing issues in the domains of copyright law, designs law, trademark law, patent law, and trade secrets (as well as some larger questions about 3D printing regulation). It also looks at the use of open licensing models in respect of 3D printing.
Warp Consistency for Unsupervised Learning of Dense Correspondences

The key challenge in learning dense correspondences lies in the lack of ground-truth matches for real image pairs. While photometric consistency losses provide unsupervised alternatives, they struggle with large appearance changes, which are ubiquitous in geometric and semantic matching tasks. Moreover, methods relying on synthetic training pairs often suffer from poor generalisation to real data. We propose Warp Consistency, an unsupervised learning objective for dense correspondence regression. Our objective is effective even in settings with large appearance and view-point changes. Given a pair of real images, we first construct an image triplet by applying a randomly sampled warp to one of the original images. We derive and analyze all flow-consistency constraints arising between the triplet. From our observations and empirical results, we design a general unsupervised objective employing two of the derived constraints. We validate our warp consistency loss by training three recent dense correspondence networks for the geometric and semantic matching tasks. Our approach sets a new state-of-the-art on several challenging benchmarks, including MegaDepth, RobotCar and TSS. Code and models are at this http URL.