Reviewerly/exHarmony huggingface dataset:
Post Implementation Evaluation of Coverage in Software Testing Using Monitoring Tools

Software deployed into production faces a host of issues post deployment. These come due to various reasons ranging from inadequate testing to fitting into a dynamic environment that is in constant flux due to myriad changes that keep happening every day. This paper discusses why it is important to compare production and QA environments for mismatches and identify gaps in coverage. This discovery also forces options to ensure QA tests the application to almost production like load and inter-dependencies, if not the same exact set up and configuration. An important outcome of testing in similar environmental dynamics as production is the evolving of a Release Test Environment, that is separated and isolated from the regular QA testing environment. Production issues point to a same error message for multiple root causes, and unless the root causes are identified, one cannot develop a code that throws the right error messages. This technique of identifying root causes to a specific error message to be embedded in the application code needs to be developed as well and is based on data analytics. This paper is one of a multi-part series and discusses the first of the five states of data analytics which is descriptive. Some monitoring and automation tools will be discussed in this paper to show how those tools can work in sync to help in identification of missed test coverage, and how the coverage can be expanded using automated functional and performance load tests. The end goal is to arrive at a cognitive state where the system is trained to prevent defects from occurring, and the paper discusses the next research topics in that direction to go from descriptive and diagnostic to predictive, prescriptive, and cognitive states. The key to arriving at the end goal is to ensure test coverage in the Release test environment meets the production demands, for the Release test environment is where we want to implement the neural network to train and validate the prediction algorithm. Once successful, and we are confident that the prediction system works, it can then be deployed into the production environment to do real-time prediction. When we begin the discussion on diagnostic and predictive systems, development of instrumented tests and creation of an Artificial Neural Network using “bots” will be discussed, but only as an introduction to the next research paper.
312 Statistical graphics and interactive visualization in animal science

Abstract Statistical graphics has advanced significantly in recent years with the development of statistical computing tools that allow us to create reports dynamically, facilitate reproducible research, and explore data interactively. In particular, data visualization is a fundamental aspect of big data analysis in animal science. However, the static nature of standard visualization limits the information that can be displayed and extracted. The objectives of this hands-on workshop are to learn how to utilize interactive visualization and investigate both global and local structures of graphs with useful zooming in and zooming out capabilities. We will use the Shiny R package, which is a web application framework for R. A Shiny application has great potential to deliver interactive data analysis and visualization in a web browser. Yet there is limited application of this type of tool in agricultural science. We will learn the capabilities of R Shiny and its use with example applications in animal science and how to aid scientific discoveries and decision-making processes using interactive data exploration tools. After taking this workshop, the participants will be able to understand the concept of R Shiny and develop a web-based interactive visualization tool. The interactive and integrative data visualization features embedded in Shiny applications offer a new resource for users to readily extract extensive information from complex data.
Towards an interface for translating natural language questions to SQL: a conceptual framework from a systematic review

Querying relational databases using human language is a complex process that must take into consideration the lack of knowledge of SQL by the end users who use the content of these systems. Many studies published in the last decade are based on limited, unbalanced or even small datasets, thing that is by far not realistic for training and evaluating a model. The SQL structure is rich and the scale of complexity is very large. Moreover, the difference between the natural language, which is a language of communication, and the SQL language that contains details of implementation specific to relational databases, adds more complications to the problem. In this paper, we present a full systematic review of the existing approaches in the task of text to SQL and we introduce the details of our suggested architecture as well as our new approach to tackle the problem of nested queries.
LIMSI @ WMT 2020

This paper describes LIMSI’s submissions to the translation shared tasks at WMT’20. This year we have focused our efforts on the biomedical translation task, developing a resource-heavy system for the translation of medical abstracts from English into French, using back-translated texts, terminological resources as well as multiple pre-processing pipelines, including pre-trained representations. Systems were also prepared for the robustness task for translating from English into German; for this large-scale task we developed multi-domain, noise-robust, translation systems aim to handle the two test conditions: zero-shot and few-shot domain adaptation.
A Study on the Impact of Artificial Intelligence on Project Management

Artificial intelligence and machine learning have found a wide range of business applications, but their impact is only just starting to be seen in project management.This study explores how our existing PM profession will change to be more suitable to AI inputs; and how project management will be forced to change because of the advent of AI, along with concrete, succinct and precise recommendations backed by demonstrable reasoning.
Low-level features based 2D face recognition using machine learning

In modern times, when deep learning-based face recognition is highly in demand, this paper presents machine learning techniques using the low-level feature extraction. Deep learning has a drawback of getting things done in a black-box, however extraction of low-level features viz. histogram of oriented gradients (HOG), speeded up robust features (SURF), and local binary patterns (LBPs) with a machine learning-based classification model presents higher simplicity. This paper presents the experimental demonstrations using 22 variations of machine learning models. Two face datasets, namely, Bosphorus and UMBDB are used for evaluating different classification models. Four experimentations are shown in the implementation section to demonstrate the effect of feature extraction, discretisation, feature variation, and noise in the image under probe. The subspace discriminant ensemble model yields the highest efficiency in classifying faces using HOG features. The effect of various noise attacks on the probe image is shown in the last experimentation.
Maximizing Power Generation of a Solar PV System for a Potential Application at Musselwhite Gold Mine Site in Northwestern Ontario, Canada

Increasing global concern for greenhouse gas emission, air pollution, fossil fuel prices, and electricity demand has generated a significant increase in research of promising renewable energy technologies for green electrical power generation. Solar photovoltaic (PV) systems are known for their capabilities to directly convert renewable solar energy into electrical energy for locations with abundant solar energy where there is a desperate need and demand for electrical power. This includes mining industries in remote locations. Solar PV systems generate more power if they are installed and operated efficiently. The inclination angle at which a PV panel is tilted from the horizontal plane is one of the most influential system parameters that affect the amount of electrical power output from the PV system and the system’s overall efficiency. Typically, the variation in the PV tilt angle determines the amount of incident solar radiation received on a PV panel to be utilized by a connected electrical load for use at a given site. In this paper, a mathematical model with numerical simulations are used to determine the total solar radiation incident on a tilted PV surface and to predict the optimum tilt angle for maximizing power generation from a solar PV system for potential application in Musselwhite Mine located in the remote northwestern region of Ontario, Canada. The total solar energy received on the optimally tilted PV surface is computed for all months in a year at the Musselwhite mine site. The results show that the monthly average optimum tilt angle of the PV system varied from a minimum value of 4o in the month of June to a maximum value of 74o in the months of January and December. It was also found that the highest maximum incident radiation of approximately 22.89 MJ/m2 for the whole year occurred in the month of April, whereas approximately 43.9% of this value (the lowest in the year) occurred in the month of December. The numerical simulation results suggest that PV systems are best be installed directly facing south at a fixed optimum tilt angle of 43o throughout the year at the Musselwhitemine site. This will improve the overall efficiency and save operating cost of the proposed PV system which in turn improves the commercial feasibility for the mining industry as an example of application.
Exchange of Preparatory Information for Secure and Usable Cryptocurrency Transactions

In 2008, the cryptocurrency Bitcoin, which is equivalent to the original idea of a blockchain, emerged as a new currency and revolutionized the digital exchange of value. Despite the great user and research interest, the customary practice is still complex. Poor usability and negative user experience lead to several security threats. This work examines the mitigation of third party attack vectors and non-malicious human failure types identified in the personal payment process. Current approaches are analyzed and a new payment protocol is specified to foster the exchange of preparatory payment information. The qualitative evaluation reveals a significant improvement over the current best-practice exchange procedure. The system demonstrates a balance between security and usability and provides a method for more user-friendly blockchain transactions.
Blockchain and Citizenship: Uneasy Bedfellows

Distributed Ledger Technology can be an effective tool for resource distribution. As individuals and organisations explore innovations which allow to redefine the rules of access, possession and sharing these developments also become important for the future of self-determination. Demonstrated through credit scoring and ‘social credit systems’, the identity of an individual is intertwined with resource access, possession and transferability. A key pre-requisite for participation is formal legal status, which translates to citizenship. However, many proponents of Distributed Ledger Technology focus predominantly on technological features and capabilities, which might enable the implementation of concepts such as decentralised governance, ‘self-sovereign identity’ management, and trust-less transactions based on ‘zero-knowledge proof’. Nevertheless, such narrow consideration overlooks existing legal and political realities. Considering the lessons learned from citizenship, it becomes questionable whether Blockchain as player in the area of identity management will ultimately increase human dignity, or further manifest traditional patterns of discrimination and inequality.
SYMMETRIC KEY GENERATION ALGORITHM USING IMAGE BASED CHAOS LOGISTIC MAPS

One of the latest hotspot in the current security trends is crypto image security i.e., security with images. In this paper, an algorithm for random bit sequence generator, based on images and two chaos logistic maps, is proposed. A symmetric key of length as per user's choice can be generated by choosing a number of points on a selected image. The chosen point pixel values are applied onto the chaos logistic map to obtain corresponding symmetric key. The selected pixel coordinates are transmitted to the receiver in a secure manner to generate the same key at the receiver. Our proposed methodology concentrates on symmetric key generation at the sender and the receiver rather than transmitting the key itself. The strength of the generated key is tested using NIST test suite and found that time required per bit is less and randomness is more when compared with Li et al. (2011), Zhao et al. (2009) and Hu et al. (2009).
LiDAR-Based Touchscreen Wall

We present the application of LiDAR (Light Detection And Ranging) technology to create a touchscreen Wall. A normal wall is converted to a touchscreen wall without the need for any additional equipment other than LiDAR and a projector. The touchscreen wall with LiDAR has delay of 0.57 seconds and minimum LiDAR scanning rate should be 120 revolutions per minute. It can be used for interactive teaching, presentation, and advertisement purposes in classrooms, airports, malls, etc. It is an alternative to the expensive touchscreens available in the market. This touchscreen wall is a simple to use, space, and cost-efficient device.
Formalisation and classification of grammar and template-mediated techniques to model and ontology verbalisation

Computational tools that translate modelling languages to a restricted natural language can improve end-user involvement in modelling. Templates are a popular approach for such a translation and are often paired with computational grammar rules to support grammatical complexity to obtain better quality sentences. There is no explicit specification of the relations used for the pairing of templates with grammar rules, so it is challenging to compare the latter templates' suitability for less-resourced languages, where grammar reuse is vital in reducing development effort. In order to enable such comparisons, we devise a model of pairing templates and rules, and assess its applicability by considering 54 existing systems for classification, and 16 of them in detail. Our classification shows that most grammar-infused template systems support detachable grammar rules and half of them introduce syntax trees for multilingualism or error checking. Furthermore, out of the 16 considered grammar-infused template systems, most do not currently support any of form of aggregation (63%) or the embedding of verb conjugation rules (81%); hence, if such features would be required, then they would need to be implemented from the ground up.
DEVELOP TO TEACHING APPROACH 3D PRIMITIVES WITH VIRTUAL REALITY

Since the last few decades, virtual reality (VR) and augmented reality (AR) interfaces have shown the potential to enhance teaching and learning, by combining physical and virtual worlds and leveraging the advantages of both. Conservative techniques of content presentation (fixed video, audio, scripts) lack personalization and interaction.
Comment on “A Lightweight Auditing Service for Shared Data With Secure User Revocation in Cloud Storage”

Recently, Rabaninejad <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (2019) proposed an excellent auditing protocol for shared data (CoRPA, for short) [ <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IEEE Trans. Ser. Comp., DOI 10.1109/TSC.2019.2919627</i> ], which has many better properties, like the identity-privacy, collusion resistant, efficient user revocation and supporting dynamic update etc. In addition, they also presented the detailed security analysis for CoRPA and described the reduction from the soundness of CoRPA to discrete logarithm assumption. However, in this article, we analyze their original security reduction (to discrete logarithm) and find out that it is incorrect and misleading. That is, the soundness of CoRPA cannot be obtained from the discrete logarithm assumption. Now, we give a new proof for their CoRPA based on the square-CDH assumption, which is also used by them to prove the security of homomorphic proxy re-signature scheme. We also hope the new security proof will provide theoretical guarantee when using CoRPA in practical scenes.
A deeper Newton descent direction with generalised Hessian matrix for SVMs: an application to face detection

By formulating the generalised Newton descent direction according to the parameter resulting from the calculation of the subgradient of the max function, a new version of NSVM (Fung and Mangasarian, 2004) is presented in this paper. This descent direction ensures even more the precision of the solution in a fast time. Associated with a good features extraction technique like Gabor's wavelets, the application of the proposed method in the context of facial detection shows that either the direction is calculated as an optimal solution of a one-dimensional problem or by a heuristic approach, manages to detect faces not detected by advanced methods.
Enhancing user and transaction privacy in bitcoin with unlinkable coin mixing scheme

The concept of coin mixing is significant in blockchain and achieves anonymity and has merited application in bitcoin. Albeit, several coin mixing schemes have been proposed, we point out that they either hoard input transactions and address mapping or not fully satisfy all requirements of practical anonymity. This paper proposes a coin mixing scheme (mixing countersignature scheme, ring signature, and coin mixing approach) that allows users to transact business untraceably and unlinkably without having to trust a third party to ensure coins are safe. Our proposed novel countersignature scheme simulation results prove the countersignature scheme's correctness with an average running time of 4 μs using PBC type A.80. The scheme's security and privacy are met with standard ring signature, ECDSA unforgeability and our countersignature. We demonstrated the efficiency of the mixing scheme using bitcoin's core regtest mode to set up a private bitcoin network. The mix takes 80, 160, 320, 640, 800 secs to service 500, 1,000, 2,000, 4,000, 5,000 users respectively. It was observed that the number of users scales linearly with average running time.
Automatically Predicting Judgement Dimensions of Human Behaviour

This paper describes our submission to the ALTA-2020 shared task on assessing behaviour from short text, We evaluate the effectiveness of traditional machine learning and recent transformers pre-trained models. Our submission with the Roberta-large model and prediction threshold achieved first place on the private leaderboard.
Multi-Encryptions System Based on Autoencoder Deep Learning Network

Encryption is a very important stage in data security. It allows only authorized people to see private data after decryption. This paper proposes Multi-Encryptions System (MES) for encrypting and decrypting images based on the Autoencoder Deep Learning (ADL) network. Four layers of ADL was used to encrypt spcific images multiple times and reconstruct the entered data again. These are the input layer, encryption layer 1, encryption layer 2 and decryption layer. The proposed MES provides high security as it has the ability to generate and utilize multiple encrypted information from original data. To measurement the reliability of this work different statistical calculations are applied. The results are promissing where significant encryption-decryption performances can be obtained.
SparsePoint: Fully End-to-End Sparse 3D Object Detector.

Object detectors based on sparse object proposals have recently been proven to be successful in the 2D domain, which makes it possible to establish a fully end-to-end detector without time-consuming post-processing. This development is also attractive for 3D object detectors. However, considering the remarkably larger search space in the 3D domain, whether it is feasible to adopt the sparse method in the 3D object detection setting is still an open question. In this paper, we propose SparsePoint, the first sparse method for 3D object detection. Our SparsePoint adopts a number of learnable proposals to encode most likely potential positions of 3D objects and a foreground embedding to encode shared semantic features of all objects. Besides, with the attention module to provide object-level interaction for redundant proposal removal and Hungarian algorithm to supply one-one label assignment, our method can produce sparse and accurate predictions. SparsePoint sets a new state-of-the-art on four public datasets, including ScanNetV2, SUN RGB-D, S3DIS, and Matterport3D. Our code will be publicly available soon.
RePOSE: Real-Time Iterative Rendering and Refinement for 6D Object Pose Estimation.

The use of iterative pose refinement is a critical processing step for 6D object pose estimation, and its performance depends greatly on one's choice of image representation. Image representations learned via deep convolutional neural networks (CNN) are currently the method of choice as they are able to robustly encode object keypoint locations. However, CNN-based image representations are computational expensive to use for iterative pose refinement, as they require that image features are extracted using a deep network, once for the input image and multiple times for rendered images during the refinement process. Instead of using a CNN to extract image features from a rendered RGB image, we propose to directly render a deep feature image. We call this deep texture rendering, where a shallow multi-layer perceptron is used to directly regress a view invariant image representation of an object. Using an estimate of the pose and deep texture rendering, our system can render an image representation in under 1ms. This image representation is optimized such that it makes it easier to perform nonlinear 6D pose estimation by adding a differentiable Levenberg-Marquardt optimization network and back-propagating the 6D pose alignment error. We call our method, RePOSE, a Real-time Iterative Rendering and Refinement algorithm for 6D POSE estimation. RePOSE runs at 71 FPS and achieves state-of-the-art accuracy of 51.6% on the Occlusion LineMOD dataset - a 4.1% absolute improvement over the prior art, and comparable performance on the YCB-Video dataset with a much faster runtime than the other pose refinement methods.
Blockchain technology: Integration in supply chain processes

Purpose: Supply chain networks face an increasing demand to integrate globally distributedcustomers and suppliers. As supply chain processes are deemed to lack sufficienttransparency and security, blockchain solutions are piloted to offer an IT infrastructurecovering these needs. This paper aims to bring current projects one stepfurther and evolves a model for integrating blockchain solutions into supply chainprocesses. Methodology: In order to get an overview of existing models for technology integration,an exploratory research study is conducted. In addition, requirements for thespecific integration of blockchain solutions are gathered and categorized in a systematiccontent analysis. Based on these requirements, the models are evaluated,compared and utilized for the development of a new model. Findings: Since none of the presented models fully meet the specific blockchainbasedrequirements, the existing models must be further developed. Specifically, increasesin the number of supply chain partners and external stakeholders involvedin blockchain-based systems are not supported by current models, and need to beintegrated systematically. Originality: In this paper, an integration model is developed that is particularly suitablefor blockchain integration into supply chain processes. In order to give startingpoints for a validation of the model, a case study is conducted in the field of blockchain-based payment gateway solutions.