Reviewerly/exHarmony huggingface dataset:
An Implementation Perspective on Electronic Invoice Presentment and Payments

A company’s goods or services are sold to clients on a daily basis in Business to Business (B2B) situations. Companies like Tata Motors, Amazon to name a few, use COMPANY ABC (CLIENT) to purchase desktop and personal computers (PCs) for use in the workplace. There is a giant corporation, COMPANY ABC (CLIENT), which everyday sell a significant number of its personal computers (PCs) to a variety of other corporations. As a result, these corporations must pay COMPANY ABC (CLIENT) for all of the items they have acquired. As a result, COMPANY ABC has a wide range of clients (CLIENT). Accounting software such as Net Suite or SAP is used by COMPANY ABC (CLIENT) to keep track of all of its clients’ unpaid bills. Using EIPP, companies all around the globe may exchange documents like invoices, purchase orders, and credit notes digitally instead of on paper. Electronic Invoice Presentment and Payment Users can rapidly monitor, query, authorize, manage, and pay for all of their payable and receivable transactions online with the touch of a mouse. With the help of the solution, businesses may replace expensive paper-based operations with more efficient technology, cut operational expenses, and make faster settlements and faster payments.
A Productive On-device Face Authentication Architecture for Embedded Systems

Face authentication is one of the effective cyber security confirmation techniques used nowadays, and it is designed and implemented using embedded systems. Face authentication systems need cloud or server-based platforms to manage the face database and to perform image processing and deep learning. The world is moving towards a situation where data privacy has a severe concern compared to any other era. Sharing private data to remote servers for face authentication is highly sensitive. The existing works focus on machine learning and deep learning aspects rather than the practical or implementation aspects of embedded systems. In this paper, we focus the major benchmark inventions on the embedded implementation perspective. We have practically deployed and validated the existing architecture on various embedded platforms such as smartphones, development kits and tablets etc. The architecture was evaluated on different versions of the Android operating system as well. We have analysed and compared different practical aspects such as model size, latency, power consumption, efficiency, and memory usage, along with the machine learning aspects such as prediction accuracy, precision etc.
Medical Imaging a Transfer Learning Process with Multimodal CNN: Dermis-Disorder

The skin is the largest and fastest progressive part in the body. The skin’s immune cells interact with keratinocytes in a number of ways to trigger a mass of dermatitis. Both living immune cells and skin-penetrating cells can coordinate with keratinocytes to promote pathogenesis of the disease. The activated cells make chemokines that attach to the immune cells in the skin. According to the latest data released by WHO in 2018, the number of deaths due to skin cancer in Bangladesh has reached about 0.04%. The mortality rate is 0.27% per 100,000 populations by age. The author has proposed an exploration using Transfer Learning on six types of skin disease as- Peeling, Acne, Eczema, Heat-rash, Melanoma, and Cold sore. The classification of these skin conditions was done using a Convolutional Neural Network. For the comparison with CNN, four state-of-art Transfer Learning models have been applied such as NASNetLarge, InceptResNetV2, EfficientNetB1 and DenseNet169, in which NASNetLarge (Accuracy 90% & Validation 80%) has given the highest accuracy. And our state of model NASNetLarge can perfectly recognise the disease types than the others. Through images processing, skin experts can initial treatment of skin diseases through observation of images of affected areas. As a result, the type of disease can be ensured and consequently can be ensured to reduce the complexity and disorders of skin diseases.
Bijective Cremona transformations of the plane

We study the birational self-maps of the projective plane over finite fields that induce permutations on the set of rational points. As a main result, we prove that no odd permutation arises over a non-prime finite field of characteristic two, which completes the investigation initiated by Cantat about which permutations can be realized this way. Main ingredients in our proof include the invariance of parity under groupoid conjugations by birational maps, and a list of generators for the group of such maps.
Developing e-learning course for information security specialists

Improving the quality of organizing the educational process and ensuring training of the qualified specialists are the leading tasks of the modern education system in Russia. An important condition for solving these problems is to expand the practice and use of innovative forms of education and the introduction of information pedagogical technologies. EdTech (Education Technology) develops rapidly in Russia: the number of people who completed various online courses has increased almost 3 times (from 14.5 to 41.3%) in 2021, more than 18 million people received online education, among them more than 2.5 million people have mastered different IT professions, and spending on online education over the past 12 months amounted to 226 billion rubles. Informatization of the education system entails a rapid increase in some developed electronic educational resources, e-learning systems and e-learning courses. Analysis of the e-learning effectiveness was carried out, modern Russian e-learning systems and their foreign counterparts were analyzed, a proper e-course was created on the base of the LMS Moodle platform, which is adaptable to the conditions of the educational process in the disciplines of higher educational institutions and schools. The electronic resources have been classified and the most successful combination of them in a training course for the successful solution of modern educational problems has been identified. The importance of using LMS and LCMS e-learning systems is stated, which is the best option for use in higher education institutions thanks to automated learning management, remote access to educational content, scalability and flexibility of the learning management system. The development of an electronic training course contributes to the effective and high-quality training of specialists in the field of information security.
ASOSIASI RULE MINING UNTUK REKOMENDASI PADA TRANSAKSI PEMINJAMAN BUKU MENGGUNAKAN FREQUENT PATTERN

The purpose of this study is to provide recommendations for grouping books at the ITB STIKOM Bali library, Jimbaran Campus. Currently, the placement of books in the ITB STIKOM Bali library, Jimbaran Campus, is still using a manual catalog. To make it easier for staff to manage books in the library, especially in terms of book data that can be borrowed, recommendations for grouping and placing books in the library are made based on the data on books that are most often borrowed together by library visitors obtained from previous borrowing data. This is done by data mining using the frequent pattern method. The use of this frequent pattern method is applied to the borrowing data of the ITB STIKOM Bali library at Jimbaran campus so that information or knowledge is obtained about recommendations for placing books that will be loaned to library members. This data mining processing is done using Weka software. The results of processing from the Association Rule data mining obtained 5 itemset combinations with confidence values ​​of 0.97, 0.96, 0.95. With the results of this data mining, the librarian obtains recommendations for grouping and placing books in the library through knowledge of the types of books that are most often borrowed.
Face Sketch-Photo Synthesis and Recognition

This paper presents a simple Face Sketch-Photo Synthesis and Recognition system. Face Sketch Synthesis provides a way to compare and match the faces present in two different modalities (i.e. face-sketches and face-photos). The aim is to significantly reduce the differences between face-sketches and face-photos and also decrease the texture irregularity between them by converting photos to sketches and vice-versa. This results in effective matching between the two thus simplifying the process of facial recognition. This system is modeled using three major components: (i) For a given input face-photo, obtaining an output face-sketch. It is designed using image processing techniques like 2 scale image decomposition and color dodging. (ii) For a given input face-sketch, obtaining an output face-photo. Convolutional Neural Networks are used to model this component. (iii) For a given query face-sketch or face-photo, recognition of face-photo or face-sketch in the database. It is implemented using Fisherface Linear Discriminant Analysis.
Brain-Inspired Spatiotemporal Feature Extraction Using Convolutional Legendre Memory Unit

Feature extraction techniques in image processing are used to obtain useful numerical features from image data which can be further used in classification or regression tasks. Extracting features from single images only provides spatial information and extending them to sequential images provides both the temporal and spatial features. However, such networks require more time and power to run. SNN are efficient and consume less memory which makes them power efficient. In this paper, we extract spatiotemporal features relevant for SNN that can classify sequential images. We train our network to recognize the images even they are transformed or occluded over time. In this paper, we have proposed a new type of SNN called Convolutional Legendre Memory Unit (ConvLMU) inspired by Convolutional Long Short-Term Memory and LMU. Our model outperforms some of the state-of-the-art spatiotemporal feature extraction models run on a synthetic dataset generated from the benchmark MNIST dataset.
GQNN: Greedy Quanvolutional Neural Network Model

Quantum computation, particularly in the field of machine learning, is a rapidly growing technology. Major advantage of Quantum computing is its speed to perform calculations. This paper proposes a novel model architecture for feature extraction. It extracts the features from a colored spectrogram as an extension to the already existing Quanvolutional Neural Network which works on only grayscale images or 2-dimensional representation of spectrograms. The proposed model architecture works on all the three layers of an image (RGB) and uses random quantum circuits to extract features from them and distribute them into several output images and out of them one is selected which contains the most important and pertinent features from the original image helping the training of the CNN model used ahead. COVID-19 use case is used for performance evaluation. Normally the testing methods used to detect virus are expensive, examples include RT-PCR test, CT scan images. These methods require a medical professional to conduct the test while being in the proximity of the patient. Also, the testing kits once used cannot be used again. One of the most evident changes in a Covid 19 patient is the change in his/her coughing and breathing pattern. This work analyzed the spectrograms of the audio samples of coughing and breathing patterns of Covid 19 patients using the proposed model architecture and provided subsequent results. Finally to generalize our model’s applicability, the model is also run-on Alzheimer disease dataset and corresponding results are provided.
Selfie2Business - An Application to Identify Objects and Recommend Relevant Service Providers

Due to the rise in the number of electronic devices, 2.5 million terabytes of data is generated every day. This ranges from device logs and browsing history, to social media posts in the form of text, images, and videos, websites, and news articles. Images alone contain a wealth of data. This data is usually used by companies to glean valuable insights into market trends, usage patterns, and so on. In this paper, we propose giving users a way to utilize the information in images/selfies to search for products and services. This is achieved by using an object detection neural network to detect objects in an image and suggesting relevant products based on the results.
TapasQA - Question Answering on Statistical Plots Using Google TAPAS

The proposed research aims to build a question answering system for statistical plots that will help analysts question and understand plots on a large scale and automate decision-making capabilities. The built model aids in answering open-ended questions and Yes/No binary questions on images of statistical plots that include a vertical bar, horizontal bar, line, and dot plots. Google TAble PArSing (TAPAS) supported with custom operations is employed, which has proved to be better than other state-of-the-art models.
A Novel Structure Learning Method of Bayesian Networks Based on the N Eighboring Complete Node Ordering Search

Learning the optimal structure of a Bayesian network (BN) from observational data has received considerable research attention. In most structure learning methods based on scoring and searching, the discrete space of BNs is used, and the scale of the network grows exponentially with the number of nodes n , that is, [[EQUATION]] . Any existing scoring function in the BN space has multiple peaks. Thus, when using heuristic algorithms, local optimum problems may occur while searching in this space. As the search process involves only a small part of the space, the final learning result is not always ideal. In this study, the scoring and searching task is implemented in the complete node ordering space, and a novel neighbor operation is proposed for improving learning accuracy. This technique reduces the search space to [[EQUATION]] and facilitates optimized learning of a structure. Furthermore, techniques for efficiency optimization are proposed, which address the problems of high computational complexity and repetitive calculation of family scores.
An Innovative Telemonitoring System for Older Adults Based on Low Power Wide Area Networks

We describe an innovative system to support the care network of older adults that live autonomously at home, as well as monitoring their daily activities in the living environments. The novel system is based on two ICT infrastructures: (a) a new open access regional Low Power Wide Area Network (LPWA) based on LoraWAN® technology, and (b) an open regional big-data platform for sensor networks, called SensorNET. The system has been co-developed by Lepida Scpa (a local public owned tech-company) and AIAS WeCareMore Centre (an association of people with disabilities specialized in assistive technologies). The first prototype of the system is currently being tested involving residents of sheltered apartments (Novi Care Residence) and their caregiver. The ultimate goal for the system is to foster the integration of the local care and social networks with a view to allow older adults to stay longer and live independently and safe in their homes.
Exploring Self-supervised Capsule Networks for Improved Classification with Data Scarcity

While Capsule Networks overcome many shortcomings of Convolutional Neural Networks by taking spatial hierarchies between features into consideration and providing a new approach to routing between layers, they are sensible towards overfitting. Self-supervised training improves the semantic understanding of a network significantly and improves generalization without requiring additional images. Therefore, self-supervised Capsule Networks are a promising approach to improve learning under data scarcity, which makes the combination interesting for a huge variety of applications. Our approach improves test accuracy by up to 11.7% for small data sets and by up to 11.5% for small and imbalanced data sets. Furthermore, we explore the synergies and characteristics of this innovative method combination and give a general overview on the possibilities of self-supervised Capsule Networks.
Combined Vision and Wearable System for Daily Activity Recognition

Social assistive robotics aims at improving the quality of life of elderly people and caregivers. Human Activity Recognition (HAR) is one of the capabilities the assistive robot should be endowed with, to allow aged people to independently live in their homes. This work deals with the problem of performing HAR by employing two wearable inertial sensors and one RGB-D camera, mounted on the social robot Pepper. Specifically, the main purpose is to prove that Pepper robot is able to correctly recognize daily living activities by exploiting the information coming from the RGB-D camera and one inertial sensor placed on the index finger of the subject. Ten users were asked to perform ten activities while wearing an inertial glove, SensHand, and while being recorded by the camera. Two different perspectives of the robot were studied to understand if a good activity recognition could be obtained when the robot is in front of the person and on his side. The results show that almost the same recognition performances are obtained when combining the visual sensor, no matter the chosen perspective, with the inertial sensor only on the index (95%), with respect to the fusion of the same camera with the inertial sensor on the index and on the wrist (96%). This supports the conclusion that elderly people could just wear a small ring on the index finger to allow the robot to recognize their activities, taking advantage from a system which is comfortable and easy-to-wear.
Survey on Various Performance Metrices for Lightweight Encryption Algorithms

Lightweight cryptography is the most emerging area in resource constraint applications. In lightweight cryptography, encryption algorithm must be lightweight w.r.t. to resources and should also offer good security against possible attacks. Cryptography algorithms are categorized depending on mode of their implementation or on the architecture. Most of the lightweight cryptography algorithms falls under block ciphers and follow symmetric architecture and can be either hardware or software implemented. Area and energy are essential factors taken into consideration for hardware implemented designs. Whereas code size and throughput are essential factors for software implemented designs. Block ciphers implemented in software are less expensive and offers high flexibility compared to hardware implementation. Hardware implemented block ciphers are relatively simpler to implement compared to software implementation and they mainly focus on optimization of hardware resources. In this article, we represent various hardware and software metrics available for evaluating the performance of lightweight encryption algorithms.
Comparative Analysis of Feature and Intensity Based Image Registration Algorithms in Variable Agricultural Scenarios

Image registration has widespread application in fields like medical imaging, satellite imagery and agriculture precision as it is essential for feature detection and extraction. The extent of this paper is focussed on analysis of intensity and feature-based registration algorithms over Blue and RedEdge multispectral images of wheat and cauliflower field under different altitudinal conditions i.e., drone imaging at 3 m for cauliflower and handheld imaging at 1 m for wheat crops. The overall comparison among feature and intensity-based algorithms is based on registration quality and time taken for feature matching. Intra-class comparison of feature-based registration is parameterized on type of transformation, number of features being detected, number of features matched, quality and feature matching time. Intra-class comparison of intensity-based registration algorithms is based on type of transformation, nature of alignment, quality and feature matching time. This study has considered SURF, MSER, KAZE, ORB for feature-based registration and Phase Correlation, Monomodal intensity and Multimodal intensity for intensity-based registration. Quantitatively, feature-based techniques were found superior to intensity-based techniques in terms of quality and computational time, where ORB and MSER scored highest. Among intensity-based methods, Monomodal intensity performed best in terms of registration quality. However, Phase Correlation marginally scored less in quality but fared well in terms of computational time.
Adversarial vulnerability bounds for Gaussian process classification

Abstract Protecting ML classifiers from adversarial examples is crucial. We propose that the main threat is an attacker perturbing a confidently classified input to produce a confident misclassification . We consider in this paper the $$L_0$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>L</mml:mi> <mml:mn>0</mml:mn> </mml:msub> </mml:math> attack in which a small number of inputs can be perturbed by the attacker at test-time. To quantify the risk of this form of attack we have devised a formal guarantee in the form of an adversarial bound (AB) for a binary, Gaussian process classifier using the EQ kernel. This bound holds for the entire input domain , bounding the potential of any future adversarial attack to cause a confident misclassification. We explore how to extend to other kernels and investigate how to maximise the bound by altering the classifier (for example by using sparse approximations). We test the bound using a variety of datasets and show that it produces relevant and practical bounds for many of them.
Mixed messages: Mediating native belonging in Asian Russian. Kathryn E.Graber. Ithaca, New York; London: Cornell University Press. 2020. xix + 262 pp. Hardback (9781501750502) 92 USD, Paperback (150175050X) 32.18 USD

Journal of SociolinguisticsEarly View BOOK REVIEW Mixed messages: Mediating native belonging in Asian Russian. Kathryn E. Graber. Ithaca, New York; London: Cornell University Press. 2020. xix + 262 pp. Hardback (9781501750502) 92 USD, Paperback (150175050X) 32.18 USD Vlada Baranova, Corresponding Author Vlada Baranova vlada@eu.spb.ru Department of Languages of Russia, Institute for Linguistic Studies, Russian Academy of Sciences, Saint Petersburg, Russia Correspondence Vlada Baranova, Department of Languages of Russia, Institute for Linguistic Studies, Russian Academy of Sciences, Saint Petersburg, Russia. Email: vlada@eu.spb.ruSearch for more papers by this author Vlada Baranova, Corresponding Author Vlada Baranova vlada@eu.spb.ru Department of Languages of Russia, Institute for Linguistic Studies, Russian Academy of Sciences, Saint Petersburg, Russia Correspondence Vlada Baranova, Department of Languages of Russia, Institute for Linguistic Studies, Russian Academy of Sciences, Saint Petersburg, Russia. Email: vlada@eu.spb.ruSearch for more papers by this author First published: 08 June 2022 https://doi.org/10.1111/josl.12581Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation