Reviewerly/exHarmony huggingface dataset:
Corpulyzer: A Novel Framework for Building Low Resource Language Corpora

The rapid proliferation of artificial intelligence has led to the development of sophisticated cutting-edge systems in natural language processing and computational linguistics domains. These systems heavily rely on high-quality dataset/corpora for the training of deep-learning algorithms to develop precise models. The preparation of a high-quality gold standard corpus for natural language processing on a large scale is a challenging task due to the need of huge computational resources, accurate language identification models, and precise content parsing tools. This task is further exacerbated in case of regional languages due to the scarcity of web content. In this article, we propose a generic framework of Corpus Analyzer - Corpulyzer - a novel framework for building low resource language corpora. Our framework consists of corpus generation and corpus analyzer module. We demonstrate the efficacy of our framework by creating a high-quality large scale corpus for the Urdu language as a case study. Leveraging dataset from Common Crawl Corpus (CCC), first, we prepare a list of seed URLs by filtering the Urdu language webpages. Next, we use Corpulyzer to crawl the World-Wide-Web (WWW) over a period of four years (2016-2020). We build Urdu web corpus “UrduWeb20” that consists of 8.0 million Urdu webpages crawled from 6,590 websites. In addition, we propose Low-Resource Language (LRL) website scoring algorithm and content-size filter for language-focused crawling to achieve optimal use of computational resources. Moreover, we analyze UrduWeb20 using variety of traditional metrics such as web-traffic-rank, URL depth, duplicate documents, and vocabulary distribution along with our newly defined content-richness metrics. Furthermore, we compare different characteristics of our corpus with three datasets of CCC. In general, we observe that contrary to CCC that focuses on crawling the limited number of webpages from highly ranked Urdu websites, Corpulyzer performs an in-depth crawling of Urdu content-rich websites. Finally, we made available Corpulyzer framework for the research community for corpus building.
IST-Unbabel Participation in the WMT20 Quality Estimation Shared Task.

We present the joint contribution of IST and Unbabel to the WMT 2020 Shared Task on Quality Estimation. Our team participated on all tracks (Direct Assessment, Post-Editing Effort, Document-Level), encompassing a total of 14 submissions. Our submitted systems were developed by extending the OpenKiwi framework to a transformer-based predictor-estimator architecture, and to cope with glass-box, uncertainty-based features coming from neural machine translation systems.
Arabic handwriting recognition: Between handcrafted methods and deep learning techniques

Recently, the area of pattern recognition has attracted the attention of many researchers in various domain and applications such as biometric, classification problems, and object recognition in general. In the last two decades, handwriting recognition is considered as one of the most active topics in this research area. The researchers focus their efforts in order to recognize many language handwriting. Among the language that still a challenged task for researcher is the recognition of Arabic handwriting because of several inherent characteristics of Arabic script including cursiveness and the existence of dots and diacritics. etc. Since deep learning algorithms have become the main core of most proposed solutions, the main aim of this paper is to evaluate the performance of some handcrafted feature extraction methods against CNNs based extraction features on well representing Arabic handwriting. The experimental results have been done on the public AHDB benchmark database.
Gender Coreference and Bias Evaluation at WMT 2020

Gender bias in machine translation can manifest when choosing gender inflections based on spurious gender correlations. For example, always translating doctors as men and nurses as women. This can be particularly harmful as models become more popular and deployed within commercial systems. Our work presents the largest evidence for the phenomenon in more than 19 systems submitted to the WMT over four diverse target languages: Czech, German, Polish, and Russian. To achieve this, we use WinoMT, a recent automatic test suite which examines gender coreference and bias when translating from English to languages with grammatical gender. We extend WinoMT to handle two new languages tested in WMT: Polish and Czech. We find that all systems consistently use spurious correlations in the data rather than meaningful contextual information.
Measuring Software Maintainability with Naïve Bayes Classifier

Software products in the market are changing due to changes in business processes, technology, or new requirements from the customers. Maintainability of legacy systems has always been an inspiring task for the software companies. In order to determine whether the software requires maintainability by reverse engineering or by forward engineering approach, a system assessment was done from diverse perspectives: quality, business value, type of errors, etc. In this research, the changes required in the existing software components of the legacy system were identified using a supervised learning approach. New interfaces for the software components were redesigned according to the new requirements and/or type of errors. Software maintainability was measured by applying a machine learning technique, i.e., Naïve Bayes classifier. The dataset was designed based on the observations such as component state, successful or error type in the component, line of code of error that exists in the component, component business value, and changes required for the component or not. The results generated by the Waikato Environment for Knowledge Analysis (WEKA) software confirm the effectiveness of the introduced methodology with an accuracy of 97.18%.
Automated Indicator Mineral Analysis of Fine-Grained Till Associated with the Sisson W-Mo Deposit, New Brunswick, Canada

Exploration under thick glacial sediment cover is an important facet of modern mineral exploration in Canada and northern Europe. Till heavy mineral concentrate (HMC) indicator mineral methods are well established in exploration for diamonds, gold, and base metals in glaciated terrain. Traditional methods rely on visual examination of &gt;250 µm HMC material. This study applies mineral liberation analysis (MLA) to investigate the finer (&lt;250 µm) fraction of till HMC. Automated mineralogy (e.g., MLA) of finer material allows for the rapid collection of precise compositional and morphological data from a large number (10,000–100,000) of heavy mineral grains in a single sample. The Sisson W-Mo deposit has a previously documented dispersal train containing the ore minerals scheelite, wolframite, and molybdenite, along with sulfide and other accessory minerals, and was used as a test site for this study. Wolframite is identified in till samples up to 10 km down ice, whereas in previous work on the coarse fraction of till it was only identified directly overlying mineralization. Chalcopyrite and pyrite are found up to 10 km down ice, an increase over 2.5 and 5 km, respectively, achieved in previous work on the coarse fraction of the same HMC. Galena, sphalerite, arsenopyrite, and pyrrhotite are also found up to 10 km down ice after only being identified immediately overlying mineralization using the &gt;250 µm fraction of HMC. Many of these sulfide grains are present only as inclusions in more chemically and robust minerals and would not be identified using optical methods. The extension of the wolframite dispersal train highlights the ability of MLA to identify minerals that lack distinguishing physical characteristics to aid visual identification.
COVID-19 and the Digital Ecosystem: Using a Mobile App to Connect a Rural Community

The outbreak of the novel coronavirus disease in 2019 disrupted every aspect of human life, threatening the world's public health system. Most countries accelerated their transition to digital technologies that support healthcare, social activities, learning, and shopping to minimise these disruptions. As COVID-19 propels nations towards the digital economy, epitomising the data age, the digital divide will be more pronounced even within the same country. The transition to the digital space has exposed Africa's infrastructural deficiencies. Zimbabwe's rural communities have limited access to information related to COVID-19 due to the unavailability of radio and television reception. In light of limited access to radio, television, and newspaper by rural communities, the study sought to develop a mobile app that can enhance access to information related to COVID-19. The Information System Development Methodology and agile software design principles guided the development of the app. The evaluation results show positive feedback regarding the usefulness and utility of the app. The findings indicate that the participants found the app very easy to remember, easy to learn, and perceived that it satisfied their information access needs. The results can be useful in improving the deployment of digital health solutions targeting rural and remote communities.
A Unified Approach to Local Quantum Uncertainty and Interferometric Power by Metric Adjusted Skew Information

Local quantum uncertainty and interferometric power have been introduced by Girolami et al. in [1,2] as geometric quantifiers of quantum correlations. The aim of the present paper is to discuss their properties in a unified manner by means of the the metric adjusted skew information defined by Hansen in [3].
Multi-hop Attention Graph Neural Network

Self-attention mechanism in graph neural networks (GNNs) led to state-of-the-art performance on many graph representation learning tasks. Currently, at every layer, attention is computed between connected pairs of nodes and depends solely on the representation of the two nodes. However, such attention mechanism does not account for nodes that are not directly connected but provide important network context. Here we propose Multi-hop Attention Graph Neural Network (MAGNA), a principled way to incorporate multi-hop context information into every layer of attention computation. MAGNA diffuses the attention scores across the network, which increases the receptive field for every layer of the GNN. Unlike previous approaches, MAGNA uses a diffusion prior on attention values, to efficiently account for all paths between the pair of disconnected nodes. We demonstrate in theory and experiments that MAGNA captures large-scale structural information in every layer, and has a low-pass effect that eliminates noisy high-frequency information from graph data. Experimental results on node classification as well as the knowledge graph completion benchmarks show that MAGNA achieves state-of-the-art results: MAGNA achieves up to 5.7 percent relative error reduction over the previous state-of-the-art on Cora, Citeseer, and Pubmed. MAGNA also obtains the best performance on a large-scale Open Graph Benchmark dataset. On knowledge graph completion MAGNA advances state-of-the-art on WN18RR and FB15k-237 across four different performance metrics.
Implementation of Intrusion Detection System in the Internet of Things: A Survey

The Internet of Things (IoT) is a precipitously evolving technology. In which interconnected computing devices and sensors share data over the network to decipher different problems and deliver new services. Medical treatment, control devices remotely, and machine to machine interaction, etc., are services for users without human collaboration. Despite of having many advantages it also has some disadvantages, Security is one of them. There are many techniques used to preclude IoT from several attacks Intrusion Detection System (IDS) is one of them. IDS is one of the most original and well-organized methods that can protect IoT devices from invaders and detect their attack with high accuracy. This study discussed and briefly explained numerous kinds of attacks like DDoS/DoS, hello flood and Sybil attack, etc., and also anticipated different kinds of IDS approaches like Machine Learning, SDN, and Automata-based IDS that can be beneficial for the prevention and detection of IoT devices from attacks.
A Novel Perspective to Threat Modelling using Design Thinking and Agile Principles

Security for every organization in the digital space is of primary focus and to better highlight and define the strategies to keep the systems safe and secure is of prime importance. While unauthorized access and unethical actions by intruders remain a cause of concern, ensuring the right measures through proper Threat Modelling techniques is necessary to create a barrier against them. The intention of getting access to a system or website or server can be born out of multiple threat groups and can be classified into common security threats. This paper looks at a persona-based approach to identify user groups that can be a threat to a system and how we can use the concepts of Design Thinking to model the system and protect it from possible security breaches. Considering the agile methodologies of software development, the paper talks about focusing on a perspective that discusses a design methodology by keeping the individuals and interactions for working models at the top of threat modelling measures.
Learning to Incorporate Structure Knowledge for Image Inpainting

This paper develops a multi-task learning framework that attempts to incorporate the image structure knowledge to assist image inpainting, which is not well explored in previous works. The primary idea is to train a shared generator to simultaneously complete the corrupted image and corresponding structures --- edge and gradient, thus implicitly encouraging the generator to exploit relevant structure knowledge while inpainting. In the meantime, we also introduce a structure embedding scheme to explicitly embed the learned structure features into the inpainting process, thus to provide possible preconditions for image completion. Specifically, a novel pyramid structure loss is proposed to supervise structure learning and embedding. Moreover, an attention mechanism is developed to further exploit the recurrent structures and patterns in the image to refine the generated structures and contents. Through multi-task learning, structure embedding besides with attention, our framework takes advantage of the structure knowledge and outperforms several state-of-the-art methods on benchmark datasets quantitatively and qualitatively.
A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

As the upsurge of information and communication technologies has become the foundation of all modern application domains, fueled by the unprecedented amount of data being processed and exchanged, besides security concerns, there are also pressing privacy considerations that come into play. Compounding this issue, there is currently a documented gap between the cybersecurity and privacy risk assessment (RA) avenues, which are treated as distinct management processes and capitalise on rather rigid and make-like approaches. In this paper, we aim to combine the best of both worlds by proposing the APSIA (Automated Privacy and Security Impact Assessment) methodology, which stands for Automated Privacy and Security Impact Assessment. APSIA is powered by the use of interdependency graph models and data processing flows used to create a digital reflection of the cyber-physical environment of an organisation. Along with this model, we present a novel and extensible privacy risk scoring system for quantifying the privacy impact triggered by the identified vulnerabilities of the ICT infrastructure of an organisation. We provide a prototype implementation and demonstrate its applicability and efficacy through a specific case study in the context of a heavily regulated sector (i.e., assistive healthcare domain) where strict security and privacy considerations are not only expected but mandated so as to better showcase the beneficial characteristics of APSIA. Our approach can complement any existing security-based RA tool and provide the means to conduct an enhanced, dynamic and generic assessment as an integral part of an iterative and unified risk assessment process on-the-fly. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that such holistic security and privacy mechanisms can reach their full potential towards solving this conundrum.
Toward Cosmological Simulations of Dark Matter on Quantum Computers

State-of-the-art cosmological simulations on classical computers are limited by time, energy, and memory usage. Quantum computers can perform some calculations exponentially faster than classical computers, using exponentially less energy and memory, and may enable extremely large simulations that accurately capture the whole dynamic range of structure in the Universe within statistically representative cosmic volumes. However, not all computational tasks exhibit a `quantum advantage'. Quantum circuits act linearly on quantum states, so nonlinearities (e.g. self-gravity in cosmological simulations) pose a significant challenge. Here we outline one potential approach to overcome this challenge and solve the (nonlinear) Schrodinger-Poisson equations for the evolution of self-gravitating dark matter, based on a hybrid quantum-classical variational algorithm framework (Lubasch 2020). We demonstrate the method with a proof-of-concept mock quantum simulation, envisioning a future where quantum computers will one day lead simulations of dark matter.
Color Detection of RGB Images Using Python and OpenCv

The main objective of this application is the methodology for identifying the shades of colors with an exact prediction with their names. A study says, a normal human can able to clearly identify nearly 1 million shades of colors. But in the case of human having “enchroma”, could be able to see only 1% (i.e.10,000 colors) from the normal humans. While painting pictures, a painter needs to identify the color patterns exactly or else the reality of image is not clear.
s4rdm3x: A Tool Suite to Explore Code to Architecture Mapping Techniques

Architectural drift and erosion, where the implementation starts to deviate from the intended software architecture or the rules set by it, are common problems in long-lived software systems.This can be avoided by using techniques, such as Reflexion modeling (Murphy et al., 1995), to validate that the implementation conforms to the indented architecture.Unfortunately, such techniques require a mapping from source code modules (e.g., classes) to elements of the architecture, something that is not always available or up to date.This is a known problem when, e.g., companies want to adopt static architecture conformance checking; the effort to manually create or bring this mapping up to date is just too time-consuming and error-prone (Ali et al.,
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention for automating the design of DNNs to be deployed into more resource-constrained daily life devices. Despite their promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to construct the hardware cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected cost look-up tables or device-specific hardware cost models. The former can be time-consuming due to the needed learning about the device's compilation method and how to set up the measurement pipeline, while the latter is often a barrier for non-hardware experts like NAS researchers. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to the required significant computational resources and the differences in their adopted search space, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance (e.g., energy cost and latency) of all the networks in the search space of both NAS-Bench-201 and FBNet, considering six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases when HW-NAS-Bench (1) allows non-hardware experts to perform HW-NAS by simply querying our pre-measured dataset and (2) verify that dedicated device-specific HW-NAS can indeed often provide optimal accuracy-cost trade-offs. The code is available at https://github.com/RICE-EIC/HW-NAS-Bench.
Adversarial Training for Multi-Channel Sign Language Production

Sign Languages are rich multi-channel languages, requiring articulation of both manual (hands) and non-manual (face and body) features in a precise, intricate manner. Sign Language Production (SLP), the automatic translation from spoken to sign languages, must embody this full sign morphology to be truly understandable by the Deaf community. Previous work has mainly focused on manual feature production, with an under-articulated output caused by regression to the mean. In this paper, we propose an Adversarial Multi-Channel approach to SLP. We frame sign production as a minimax game between a transformer-based Generator and a conditional Discriminator. Our adversarial discriminator evaluates the realism of sign production conditioned on the source text, pushing the generator towards a realistic and articulate output. Additionally, we fully encapsulate sign articulators with the inclusion of non-manual features, producing facial features and mouthing patterns. We evaluate on the challenging RWTH-PHOENIX-Weather-2014T (PHOENIX14T) dataset, and report state-of-the art SLP back-translation performance for manual production. We set new benchmarks for the production of multi-channel sign to underpin future research into realistic SLP.
The Effects of Vertical and Horizontal Distance on The Performance of QR Code Detection System

Abstract The development of image processing science is needed to solve problems that are often faced by humans, especially in the field of computer vision. One application of the image processing system is on a package delivery mission during the Covid-19 pandemic. Drones are used to send packages by detecting the presence of Qr Code to determine the point of delivery location. In this study, tests will be carried out on the maximum distance (vertical and horizontal) that can be detected by the Qr Code detection system and the length of time to detect the presence of the Qr Code (time spent). The test shows that the greater the data collection distance (vertical and horizontal), the longer the system detects the presence of the Qr Code. The maximum horizontal distance that the Qr Code can detect is 155 cm, while the vertical distance is 115 cm. The detection distance at vertical is smaller than horizontal because the vertical distance is affected by the field of view (FoV).