Reviewerly/exHarmony huggingface dataset:
A Novel 3D Facial Recognition for Digital Payments

In this research paper, we propose a system that envisions to revolutionize the way digital payments work across the globe. With over six-hundred million users transacting regularly in India alone, the landscape of the global market is potentially huge. But when we talk about the security of these, a big question arises as the current methods are either breachable or complex. The majority of them use age authentication mechanisms, or the ones which have advanced to use new technologies are too slow and memory inefficient. All of which is not very convenient, simplified, and reliable for an end-user. Considering all these issues and to effectively solve them, we propose a 3D facial recognition system backed with a series of preprocessing algorithms, a point cloud neural network along with a VoxelNet layer to reconstruct a textured full 3D face model out from a single image. This system will be further implemented on applications like 3D facial payments.
Progressive Context-Aware Graph Feature Learning for Target Re-Identification

This paper aims at robust and discriminative feature learning for target re-identification (Re-ID). In addition to paying attention to the individual appearance information as in most Re-ID methods, we further utilize the abundant contextual information as additional clues to guide the feature learning. Graph as a format of structured data is used to represent the target sample with its context. It describes the first-order appearance information of the samples and the second-order topological relationship information among samples, based on which we compute the feature representation by learning a graph feature embedding. We provide a detailed analysis of graph convolutional network mechanism applied in target Re-ID and propose a novel progressive context-aware graph feature learning method, in which the message passing is dominated by a pre-defined adjacency relationship followed by a learned relationship in a self-adaptive way. The proposed method fully exploits and utilizes contextual information at a low cost for Re-ID. Extensive experiments on five Re-ID benchmarks demonstrate the state-of-the-art performance of the proposed method.
Collaborative Filtering Based-Recommender System Using Ant Colony Optimisation for Path Planning

In recent years, the recommender system has been extraordinarily successful in the Internet industry, and it has benefited many webs application developers. Popular websites such as YouTube, Netflix, and Facebook use the recommender system to enhance the user experience and attract new users. Nevertheless, the demand for pervasive information processing over the Web has paved the way for a context-aware recommender system capable of dealing with a massive amount of data and capable of generating an effective and personalised output. In this paper, a recommender system was designed and developed for the tourism industry in New Zealand. The proposed method comprises collaborative filtering techniques and a machine learning model, specifically the k-nearest neighbours (k-NN) algorithm, as the framework of the similarity calculation to analyse user preference data and predict similar tourist attractions. In addition, the Ant Colony optimisation algorithm (ACO) was utilised to aggregate the shortest path between multiple attraction points. Furthermore, the researchers demonstrate the integration process of the recommender system into the website and travel route module. This research is based on Python and JavaScript and uses Flask, NumPy and Vue.js to build the web-based recommender system.
Chapter 16. Abductive Learning

This chapter will introduce ABL (ABductive Learning), a new paradigm which integrates machine learning and logical reasoning in a balanced loop enabling them to work together in a mutually beneficial way.
UONN: Energy-Efficient Optical Neural Network

This work proposes a novel loop-based optical neural network UONN capable of large-scale vector-matrix multiplications. Results show that UONN can achieve up to 6000 X higher energy efficiency than existing MZI-based neural network.
ARCHIE++ : A Cloud-Enabled Framework for Conducting AR System Testing in the Wild

In this paper, we present ARCHIE++, a testing framework for conducting AR system testing and collecting user feedback in the wild. Our system addresses challenges in AR testing practices by aggregating usability feedback data (collected <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> ) with system performance data from that same time period. These data packets can then be leveraged to identify edge cases encountered by testers during unconstrained usage scenarios. We begin by presenting a set of current trends in performing human testing of AR systems, identified by reviewing a selection of recent work from leading conferences in mixed reality, human factors, and mobile and pervasive systems. From the trends, we identify a set of challenges to be faced when attempting to adopt these practices to testing in the wild. These challenges are used to inform the design of our framework, which provides a cloud-enabled and device-agnostic way for AR systems developers to improve their knowledge of environmental conditions and to support scalability and reproducibility when testing in the wild. We then present a series of case studies demonstrating how ARCHIE++ can be used to support a range of AR testing scenarios, and demonstrate the limited overhead of the framework through a series of evaluations. We close with additional discussion on the design and utility of ARCHIE++ under various edge conditions.
Distractor-Aware Video Object Segmentation

Semi-supervised video object segmentation is a challenging task that aims to segment a target throughout a video sequence given an initial mask at the first frame. Discriminative approaches have demonstrated competitive performance on this task at a sensible complexity. These approaches typically formulate the problem as a one-versus-one classification between the target and the background. However, in reality, a video sequence usually encompasses a target, background, and possibly other distracting objects. Those objects increase the risk of introducing false positives, especially if they share visual similarities with the target. Therefore, it is more effective to separate distractors from the background, and handle them independently.We propose a one-versus-many scheme to address this situation by separating distractors into their own class. This separation allows imposing special attention to challenging regions that are most likely to degrade the performance. We demonstrate the prominence of this formulation by modifying the learning-what-to-learn [] method to be distractor-aware. Our proposed approach sets a new state-of-the-art on the DAVIS 2017 validation dataset, and improves over the baseline on the DAVIS 2017 test-dev benchmark by 4.6% points.
The Impact of Digital Trade on Jordan's Economic Growth

This Study aims to investigate the impact of digital trade on economic growth in Jordan. The Study focused on four important variables (Digital Trade, Real GDP, Per Capita gross domestic Product and trade openness), in The form of annual data, the limits test for co–integration was applied in the study at the long–run equilibrium relationship between the variables. The results confirmed the existence of a relationship between the variables in the long-run, meaning that there is a positive relationship between digital trade and economic growth in Jordan. Accordingly, the study proposes to develop digital trade in an effort to make its role greater in economic growth using Jordan as a sample.
AN EFFICIENT KEYPOINT FEATURE EXTRACTION TECHNIQUES TO DETECT COPY MOVE IMAGE FORGERY

Digital images are highly manipulated without degrading their visual quality and resolution with advanced and easily available software's.Copy Move Forgery (CMF) is a common manipulation procedure in digital images that involves copying the object or segment of an image and partially pasting it on another area of the same image.Many block-based detection methods are present to identify the copy move forgery in images.However, their performance got deteriorated considerably under the shape of the regions that cannot be exactly identified, and shows limited robustness based on performance metrics like precision and recall.A novel and robust algorithm is introduced in this paper to overcome that problem.The use of non-overlapping segmentation compared to the overlapping method used is to reduce the computational complexity compared to the regular blocks and irregular blocks to obtain better performance to find the forged region accurately.Here, input images are segmented using the SLIC algorithm, and to detect forgery, the key point features are extracted from each block using the Scale Invariant Feature Transform (SIFT) algorithm along with Speeded Up Robust Feature (SURF).Block matching and labelled feature point matching is used to detect the forgery from these extracted results.Block matching process is used to identify the distance between regions of the divided images.Copy move forgery region is identified by the similarities between the features of the image.Precision, Recall and F-measure of the input image are used to assess the performance of proposed scheme.It is observed that 98.97% precision is achieved with the SLIC algorithm on the MICC-F220 database.
Teaching Vocabulary by Using Experiential Learning to The Students of MTsN 2 Banda Aceh

The research aimed to find out if the students of MTsN 2 Banda Aceh who were taught vocabulary using experiential learning got a higher score than those who were taught without experiential learning. This research was conducted using pre-experiment with one class; pre-test and post-test design. The population of this research was the eighth-grade students. The total population of this study was 114 students composed of five classes; VIII-1, VIII-2, VIII-3, and VIII-4. Every class consists of about 28 students. The sample of this research was class VIII-1 of the eighth grade because the students’ ability of this class was better than the other classes. The data were taken through the test. Then, the obtained data were analyzed by using the gain score (GS). The result of this research showed that the GS was 0.32. Meaning that there was a difference between the students’ ability in mastering vocabulary before and after teaching using experiential learning and the difference was 0.32 points or 32%. Therefore, the student’s ability in mastering vocabulary increased 32% with experiential learning. Keywords: Teaching, Learning process, Experiential learning, Vocabulary.
A Web Service for Video Smart-Cropping

This paper presents a Web service that supports the automatic transformation of a video's aspect ratio. We employ a modified smart-cropping technique from the literature that aims to minimize the loss of semantically important visual content. We integrate this method in an easy-to-use publicly- accessible Web service where a video can be uploaded and automatically transformed to the desired aspect ratio. We also demonstrate that the algorithmic modifications we introduced in the process of building our Web service offer performance gains, when compared to the original method of the literature.
Photonic photorefractive MAC accelerator for Machine Learning

AI is fueling an explosive growth in compute demand. Analog crossbar arrays enable power efficient synaptic signal processing for AI acceleration. A photonic photorefractive crossbar array for neural network training will be discussed.
A Didactic model for virtual education leading to the development of competences in higher education at universities

The current circunstances that society faces nowadays have caused that institutions digitize their processes, in the educative area, some institutions, specially public ones, have adapted their traditional teaching-learning process to virtualization. This makes necessary to use a model that let to reach the target competences established in the university curriculum. The searching proposes a student centered model and their new digital native characteristics, taking in consideration global components like: university and society which are closely related and also having as the cross component Communication and Information technologies (TIC) that are generating changes in the society. The search was quantitative with a descriptive level in order to get the situational diagnosis, applying a questionnaire to the students registered in the subject : “Programming Techniques” The components of the competence were analized, concluding that in the Knowing to be and Knowing to do there are failures which were considered at designing the model . This considers the student as the main axis, the model has been validated by three experts who have certified that every component responds to the new needs at virtual education, boosting new TIC as means of individual and collective growth and development.
Experimental symmetric private information retrieval with quantum key distribution

We report an experiment of a two-database symmetric private information retrieval protocol which incorporates a measurement-device-independent quantum key dis-tribution system. Based on the experimental system, we demonstrate information retrieval from a fingerprint database with 800 entries.
Explainable Artificial Intelligence: Rules Extraction from Neural Networks

Researchers are currently working on the development of “Explainable Artificial Intelligence” or “Explainable Artificial Intelligence (XAI)”. Such systems are designed to help the user understand the decisions made by the neural network, which will increase confidence in such systems, will allow making more effective decisions based on the results of the system operation. The first results of applying this approach allowed developers and users to study the factors that are used by the neural network to solve a specific problem and what parameters of the neural network need to be changed to improve the accuracy of its work. In addition, studying how neural networks extract, store, and transform knowledge may be useful for the future development of machine learning methods. To overcome this disadvantage of neural networks, it is proposed to consider methods for extracting rules from neural networks, which can become a link between symbolic and connectionistic models of knowledge representation in artificial intelligence. In this paper, we propose a neuro-fuzzy approach to rule extraction using time series forecasting and text recognition as examples.
Investigation of green marketing practices of UAE hypermarkets

The purpose of this investigation is to explain an empirical relationship between green principles and the firm's competitive advantage. The study is intended towards establishing an association among the constructs of green marketing constructs and firm competitive position. Green loyalty has been investigated as a mediating factor among this direct association of green principles. The data has been taken from prominent hypermarkets' customers in Dubai. This empirical research is quantitative and a cross-sectional study. The data has been collected by adopting a convenience sampling method and targeted respondents of the study were 343. The collected data were analysed by using SPSS and the mediating effect of green loyalty also been measured. The findings highlight the mediating stance of green loyalty among professionals that ultimately links towards their competitive advantage. The hypothesis testing shows a moderately significant association among the variables, showing a positively impact on the firm's competitiveness and promotion of green practices.
The Experience of Computer Music Technology Reform in Music Education Mode

The combination of music teaching and computer music technology in Colleges and universities to assist the practical application and operation reform of music theory is a qualitative change improvement to the shortcomings of traditional music teaching mode. At the same time, it can effectively stimulate college students’ interest in learning music and improve the teaching quality. This paper explores the application of multimedia computer music teaching system and harmony teaching course in Colleges and universities, and proves the superiority of computer music technology in the reform of college music teaching mode.
Cyberspace, threat agents and power

If cybersecurity is a transnational issue per se, the United Arab Emirates (UAE) is one of the main battlegrounds in the world. Indeed, both the public and the private sectors are under constant threat. In 2017 there were more than three attacks daily on Emirati interests. The UAE is prone to attacks for multiple reasons. Cyberattackers exploit the small loopholes in the federation’s massive defence system; indeed, the country has become more involved in transnational networks (finance, tourism, etc.). Because of its drive to become a smart international hub, cyberattacks multiply proportionally with the acceleration of cyber transactions. In the meantime, the necessary security infrastructures are working to keep up with demand. This challenge is coupled with the fact that the country’s two central city-states are the loci of international movement and attention. Owing to the variety of threats, assessing the risks and finding comprehensive solutions remains difficult.
How Useful is Meta-Recommendation? An Empirical Investigation

Despite the proliferation of recommendation algorithms, the question of which recommender works best for which user-item instance remains widely open. In this paper, we develop a meta-learning approach that chooses among several recommendation algorithms, which one is best suited for predicting the preference of a user for an item. We propose an empirical investigation of the meta-learner when applied to implicit and explicit datasets. The meta-learner is trained using four classifiers/regressors: logistic regression, decision trees, stochastic gradient descent, and gradient boosting. We run extensive experiments on four real datasets: RETAIL, a proprietary implicit dataset provided by our industrial partner, TAFENG, a publicly available grocery shopping dataset and two publicly available AMAZON datasets with explicit preferences. Results show that using a meta-learner yields higher accuracy than single recommendation algorithms for explicit datasets when compared to state-of-the-art ensemble-learned models and factorization machines. This work is an ongoing collaboration with the marketing department of a major industrial partner to test promotional offers for different customer segments.
Genesis of digital technologies in teaching students a foreign language in a blended learning environment

Digital technologies quickly and organically entered the life of a modern person. Moreover, the informatization of society has influenced the digitalization of education. Over the past 10–15 years, a solid body of scientific methodic works has appeared on the use of modern digital technologies in the development of foreign language speech skills and the development of students’ language skills, the development of their socio-cultural, intercultural, pragmatic and other types of competencies. Despite the linguodidactic potential and innovativeness of new methods of language teaching based on digital technologies, until 2019 the volume of use of information and communication technologies in the educational process was small. The 2019 coronavirus pandemic forced scientists and practicing teachers in extreme conditions to realistically assess the richness of the choice of digital technologies and rethink the need for each specific technology to solve specific educational problems. This is how the process of “withering away” of some technologies or “absorption” of some technologies by others began. A review of methodic works on the development of methods of foreign language teaching based on e-mail, web forum, blog technology, social service “Twitter”, wiki technology, podcasts and electronic text corpus is carried out. As a result, a conclusion is made about the relevance of four digital technologies that are integrated into modern university educational platforms. These include: blog technology, wiki technology, podcasts, and electronic corpus.
Zero-Shot Remote Sensing Image Super-Resolution Based on Image Continuity and Self Tessellations

The goal of zero-shot image super-resolution (SR) is to generate high-resolution (HR) images from never-before-seen image distributions. This is challenging, especially, because it is difficult to model the statistics of an image that the network has never seen before. Despite deep convolutional neural networks (CNN) being superior to traditional super-resolution (SR) methods, little attention has been given to generating remote sensing scene-based HR images which do not have any prior ground truths available for training. In this paper, we propose a framework that harnesses the inherent tessellated nature of remotely images using continuity to generate HR images that tackle atmospheric and radiometric condition variations. Our proposed solution utilizes self tessellations to fully harness the image heuristics to generate an SR image from a low resolution (LR) input. The salience of our approach lies in a two-fold data generation in a self-preservation case and a cascaded attention sharing mechanism on the latent space for content preservation while generating SR images. By learning a mapping from LR space to SR space while keeping the content statistics preserved helps in better quality image generation. The attention sharing between content and tessellations aids in learning the overall big picture for super-resolution without losing an eye on the main image to be super-resolved. We showcase our results with the generated images given the low resolution (LR) input images in zero-shot cases comparable to state-of-the-art results on EuroSAT and PatternNet datasets with metrics of SSIM and PSNR. We further show how this architecture can be leveraged for non-remote sensing (RS) applications.
Indistinguishable sub-nanosecond pulse generator

The indistinguishable laser pulse is predominant in the realization of qubits in decoy state-based quantum cryptography. Such cryptography in an automotive framework needs to be compact and inexpensive. However, their implementation is cumbrous and uneconomical. Significant effort has been put into reducing the form factor of quantum cryptography implementation. We report a compact, low-cost, indistinguishable, sub-nanosecond pulse generator with adjusted delay and amplitude using a Fabry–Perot laser diode. The approach was derived based on algebraic topology formulation of the electrical network, and the implementation involves time-dependent perturbation of a constant current node to generate tunable, sub-nanosecond excitation with a constant pre-bias. The simulation model of the laser diode accounted for effects of spontaneous emission and relaxation oscillation. The shortest excitation pulse thus generated was measured to have FWHM of 496 ps. Further, the indistinguishability of two laser pulses is statistically evaluated.
How Does AI Make Libraries Smart?

In the past two decades, the rapid development of information technology has been widely used in individuals' daily lives, leading to a sea change. As a city at the forefront of artificial intelligence (AI) technology in China, Hangzhou first applied the technology to public library services to relieve the shortage of reference and circulation service resources caused by a large number of regular patrons. This chapter first introduced the application model of AI in various libraries worldwide, then focused on its application in a public library in China, a developing country. This chapter may shed light on the application of AI in other libraries in developing countries.