Reviewerly/exHarmony huggingface dataset:
Cybersecurity Assurance in the Emerging Manufacturing-as-a-Service (MaaS) Paradigm: A Lesson from the Video Streaming Industry

The coronavirus disease 2019 outbreak has incapacitated physical supply chains of various industrial sectors. This has been a call for attention to rethinking innovative methods to improve supply chain resilience. Emerging manufacturing technologies like additive/hybrid manufacturing are enabling small and medium enterprises (SMEs) to transform into cybermanufacturing kiosks that deliver manufacturing-as-a-service by digitizing much of the manufacturing workflow (design, communication, and manufacturing). This transformation is causing a paradigm shift in which major producers and original equipment manufacturers can outsource manufacturing of components to distributed SMEs located near the point of need. The increasing reliance on a digital thread would reduce the burden of resiliency required from the physical supply chains. Although digital threads are reducing the burden on the physical supply chains, they introduce a new class of cyber-resilience requirements, chiefly in ensuring the integrity of the designs and other intellectual properties shared across the digital thread. A novel design-sharing scheme for the digital thread based on video streaming is introduced herein to address the issue of IP theft and tampering. The potential of design-sharing schemes for the improvement of the cyberresilience of digital supply chains is discussed.
An Algorithm for Constructing Equations of Geometry Fractals Based on Theories of R-functions

This article discusses the construction of fractal equations, the classical set of Cantor and consisting of spirals based on theories of R-functions (RFM), using recursion procedures. According to the constructed equations, various prefractals can be generated depending on the number of iterations. All results are shown as a raster chart.
A Convex Optimization Approach to Dynamic Programming in Continuous State and Action Spaces

In this paper, a convex optimization-based method is proposed for numerically solving dynamic programs in continuous state and action spaces. The key idea is to approximate the output of the Bellman operator at a particular state by the optimal value of a convex program. The approximate Bellman operator has a computational advantage because it involves a convex optimization problem in the case of control-affine systems and convex costs. Using this feature, we propose a simple dynamic programming algorithm to evaluate the approximate value function at pre-specified grid points by solving convex optimization problems in each iteration. We show that the proposed method approximates the optimal value function with a uniform convergence property in the case of convex optimal value functions. We also propose an interpolation-free design method for a control policy, of which performance converges uniformly to the optimum as the grid resolution becomes finer. When a nonlinear control-affine system is considered, the convex optimization approach provides an approximate policy with a provable suboptimality bound. For general cases, the proposed convex formulation of dynamic programming operators can be modified as a nonconvex bilevel program, in which the inner problem is a linear program, without losing the uniform convergence properties.
Author Identification via a Distributed Neural-Evolutionary Hybrid (DiNEH)

In this paper, we propose a non-traditional Genetic & Evolutionary Feature Selection (GEFeS) method for Author Identification. This method evolves a distributed feature vector for each author and it's therefore distributed. We refer to this new approach as a distributed neural evolutionary hybrid (DiNEH). We compare the performance of DiNEH with a number of well-known Authorship Attribution Techniques (AATs) from the literature. DiNEH was able to outperform all of the AATs on one dataset and was second on the second dataset by a narrow margin.
Machine learning classifier model for attendance management system

Face recognition technology has made many developments in the world. Attendance using face recognition is a real-time solution which involves handling of a person's attendance system. Face recognition based attendance system will be able to find and recognize human faces fast and precise in images or videos that are being captured through a camera. The traditional method of marking attendance is time-consuming and maintenance is difficult. There were so many methods like eigen faces, fisher faces etc. In this paper, the performance of face detection is implemented by a machine learning cascade classifier technique. It recognizes the faces in the image and compares it with the trained faces in the database. On identification of a registered face on the acquired image collections, the attendance of a person is registered automatically without manual interruption.
A Differentially Private Random Decision Tree Classifier with High Utility

Random decision tree-based classifiers are one of the most efficient approaches in data mining to implement classification prediction. However, the structure of decision trees possibly causes the privacy leakage of data. It is necessary to design novel random decision trees to satisfy some privacy requirement. In this paper, we propose a differentially private random decision tree classifier with high utility. We first construct a private random decision tree classifier satisfying differential privacy, which is a strong privacy metric with rigorously mathematical definition. Then, we analyze the privacy and utility of the basic random decision tree classifier. Next, we propose two improved approaches to reduce the number of the non-leaf and leaf nodes so as to increase the count of class labels in the leaf nodes. Extensive experiments are used to evaluate our proposed algorithm and the results show its high utility.
When Can We Answer Queries Using Result-Bounded Data Interfaces?

We consider answering queries on data available through access methods, that provide lookup access to the tuples matching a given binding. Such interfaces are common on the Web; further, they often have bounds on how many results they can return, e.g., because of pagination or rate limits. We thus study result-bounded methods, which may return only a limited number of tuples. We study how to decide if a query is answerable using result-bounded methods, i.e., how to compute a plan that returns all answers to the query using the methods, assuming that the underlying data satisfies some integrity constraints. We first show how to reduce answerability to a query containment problem with constraints. Second, we show "schema simplification" theorems describing when and how result-bounded services can be used. Finally, we use these theorems to give decidability and complexity results about answerability for common constraint classes.
Generating Knowledge-Based Attentive User Representations for Sparse Interaction Recommendation

Deep neural networks (DNNs) have been widely imported into collaborative-filtering (CF) based recommender systems and yielded remarkable superiority over traditional recommendation models. However, most deep CF-based models perform weakly when observed user-item interactions are sparse since user preferences and item characteristics are inferred mainly based on observed (historical) interactions. To address this problem, we propose a deep knowledge-enhanced recommendation model in this paper. Specifically, to augment user/item representations in the scenario of sparse historical user-item interactions, we first incorporate the knowledge from open knowledge graphs and personal information of users as side information, from which sufficient features of users and items are extracted. Second, to well capture shifted user preferences, we leverage a memory component constituted by recently interacted items rather than all historical ones. Third, attentive user representations are generated by attention mechanism to capture the diversity of user preferences. Furthermore, we build a convolutional neural network to pool the latent features in user representations for better user modeling, which enhances recommendation performance further. Our extensive experiments conducted against two real-world datasets, i.e., Douban movie and NetEase music, demonstrate our model’s remarkable superiority over the state-of-the-art deep recommendation models.
A Promising Nonlinear Dimensionality Reduction Method: Kernel-Based Within Class Collaborative Preserving Discriminant Projection

High-dimensional small sample size problems exist in the real world, which significantly increases the difficulty of data processing. In this paper, we propose a kernel-based within class collaborative preserving discriminant projection method to reduce data dimensionality. In order to deal with nonlinear problems and improve the discrimination of the projection subspace, the proposed method preserves the collaborative reconstruction relationship of the same class samples in the kernel space, and pursuits maximizing the between class scatter. A two-step eigenvalue decomposition method is used to stably obtain the optimal discriminant projection matrix. Moreover, simulation experiments show that, even in low dimensions and small sample size, the proposed method can achieve high recognition accuracy.
Software documentation and augmented reality: love or arranged marriage?

There is a significant rise in the availability, development and size of software projects in the present day. Many open source projects are reused or updated for various purposes that include fixing bugs in existing projects, development and maintenance of project extensions. Developers who interact with the projects might require documentation for better comprehension of the project and to develop extensions. Most of the software projects currently do not have sufficient documentation or it is not updated along with the project. If some projects have reasonably sufficient documentation, it is usually difficult to comprehend it either for maintenance or for reuse purposes. Considering the usefulness of Augmented Reality (AR) towards comprehension, we propose the vision of integrating the domains of augmented reality and software documentation, and specifically, visualization of software documentation using AR. In this paper, we present some of the directions that could be explored towards this vision and also present an example visualization scenario for API documentation using neural system metaphor. We see this paper as a basis for the future research direction of leveraging AR towards making documentation as a primary artifact in the software development process.
Deep Reinforcement Learning For Visual Navigation of Wheeled Mobile Robots

A study is presented on applying deep reinforcement learning (DRL) for visual navigation of wheeled mobile robots (WMR) in dynamic and unknown environments. Two DRL algorithms, namely, value-learning deep Q-network (DQN) and policy gradient based asynchronous advantage actor critic ( A 3C), have been considered. RGB (red, green and blue) and depth images have been used as inputs in implementation of both DRL algorithms to generate control commands for autonomous navigation of WMR in simulation environments. The initial DRL networks were generated and trained progressively in OpenAI Gym Gazebo based simulation environments within robot operating system (ROS) framework for a popular target WMR, Kobuki TurtleBot2. A pre-trained deep neural network ResNet50 was used after further training with regrouped objects commonly found in laboratory setting for target-driven mapless visual navigation of Turlebot2 through DRL. The performance of A 3C with multiple computation threads (4, 6, and 8) was simulated on a desktop. The navigation performance of DQN and A 3C networks, in terms of reward statistics and completion time, was compared in three simulation environments. As expected, A 3C with multiple threads (4, 6, and 8) performed better than DQN and the performance of A 3C improved with number of threads. Details of the methodology, simulation results are presented and recommendations for future work towards real-time implementation through transfer learning of the DRL models are outlined.
Comparative Study of Arabic Text Categorization Using Feature Selection Techniques and Four Classifier Models

Text classification is the process of assigning appropriate categories to free text according to its content. It is one of the important task in Text mining. Numerous studies have been conducted for natural languages processing using Japanese, French, Latin and Turkish documents, but the number of works related to the text written in Arabic language is still limited. In this paper we conduct a comparative study of three methods of feature selection using four well-known classifiers namely: Decision Tree, Naive Bayes, K-Nearest Neighbors and Support Vector Machine. A corpus contained 250 Arabic text belonging into five classes: sport, politics, economics, culture and art, and society. The data set is used to evaluate and compare the effectiveness of the obtained model. The experimental results reveal that using improved Chi-square method as feature selection and Support Vector Machine as classifier outperforms other combinations in terms of precision. This combination significantly improves the performance of Arabic text classification model. The highest value of precision measure for this model is 89.9%.
Neural machine translation and the evolution of the localisation sector

Abstract The localisation sector is highly technologized and evolves rapidly. Though significant consideration has been given to third-level training in localisation for Translation Studies students, the nature of the industry is such that this topic demands regular attention. Our objective was to survey employees and executive managers to understand what impact recent technological developments, including but not limited to neural machine translation (NMT), might have on future skills and training requirements for localisation linguists. Our findings are that linguists in localisation take up a variety of roles, including transcreation, data mining, and project and vendor management. NMT is considered an important advancement, and its introduction has emphasised the need for a critical use of technology, while opening new career pathways, such as data curation and annotation. Domain specialisation is recommended for those who translate, and transferable soft skills are more essential than ever. Increased industry and interdisciplinary collaborations in training are also considered valuable.
MDS and <i>I</i>-Perfect Codes in Pomset Metric

Recently pomset metric was introduced to accommodate Lee metric, thereby generalizing poset metrics for codes over ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> . This work mainly studies results on Maximum distance separable (MDS) pomset codes and perfect codes over ℤ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sub> . If I is an ideal in a poset, the concept of I-perfect codes that was introduced to study MDS poset codes is now extended to the study of MDS pomset codes as well. But, unlike the ideals in posets, here the ideals in pomsets are considered as those with full count and those with partial count. In fact, the I-balls are no more linear when the ideal I is with partial count. This makes the investigation of the relationship of I-perfect codes with MDS pomset codes little different from that with MDS poset codes. We thoroughly study the I-balls where I is with partial count. Singleton type bound is established for codes with pomset metric and the connection of MDS codes with I-perfect codes is investigated upon. Then, we prove the duality theorem for codes with chain pomset. Finally, we determine the weight distribution of MDS pomset codes when the pomset is a chain.
WITHDRAWN: Financial auditing big data platform based on FPGA and convolutional neural network

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Deep Feature Compatibility for Generated Images Quality Assessment

The image quality assessment (IQA) for generated images main focuses on the quality of perceptual aspects. Existing methods for evaluating generated images consider the overall image distribution characteristics. At present, there is no practical method for a single generated image. To address this issue, this paper proposes a solution base on the deep feature compatibility (DFC), which first collects suitable comparison images by a collection model. Then it provides an individual score by computing the compatibility of target and pictures with good perceptual quality. This method makes up for the deficiency of Inception Score (IS) in a small number of results or/and a single image. The experiment on Caltech UCSD birds 200 (CUB) shows that our method performs well on the assessment mission for generated images. Finally, we analyze the various problems of the representative IQA methods in evaluating.
Syntactic Approach to Extracting Key Elements of Work Modification Cause in Change-Order Documents

Work modification during the construction phase is quite common, and it typically creates a detrimental impact on project performance significantly. A change-order is developed by affected parties followed by legal agreements that amend to the original contract. Information related to the causes of the modification and its effects, in particular, can provide project participants with useful knowledge that may prevent them from encountering similar changes in the next projects. However, the identification and use of the causal information of a modification is challenging because most information in a change-order is available in a text data format, which is typically unstructured. Thus, the unstructured format makes it difficult to process considerable amounts of information systematically. It also requires a substantial amount of manual effort due to different types of written expressions of the same context or meaning, and dependency on specialized and technical knowledge and jargons. This study presents a process model that automatically extracts key elements related to work modification causes in change-orders using the shortest dependency path (SDP) and conditional random field (CRF) model. The research process is comprised of four tasks: a) build a raw data repository from historical change-orders, b) frame semantic-based key elements labeling, c) create SDP between key nodes, and d) apply conditional random field (CRF) model for label classification. Change-orders obtained from real highway projects have been used to evaluate and verify the model. This research contributes to building a methodological and practical foundation for extracting dependency-based information from construction documents. Project participants can use this method to analyze past projects and assess change-order related risks for new projects before construction begins.
Bayesian Estimation of M/Ek/1 Queueing Model using Bivariate Prior

This article describes Bayes estimation of various queue characteristics such as queue parameters λ and μ, and queue performance measures like traffic intensity, expected waiting time in the queue, and expected queue size of M/Ek/1 model using Mckay’s bivariate gamma distribution as prior under squared error loss function as well as entropy loss function. Closed form expressions are obtained for the Bayes estimators of the queue parameters and various queue performance measures using the properties of confluent hyper geometric function and Gauss hyper geometric function. Bootstrap Bayes estimates and credible regions are computed using simulated data for different set of hyper parameter values. Also we apply Markov Chain Monte Carlo method and compute Bayes estimates and credible intervals of various queue characteristics using the same joint prior distribution and compare the values with bootstrap estimates.
A Mathematical Overview Of Machine Vision

The work is intended to introduce Machine Vision and Image Processing from a mathematical standpoint. Machine visions origins lie within image capturing, digital image processing and machine learning, however its now becoming geared into a much more powerful tool. This work will give description to mathematical methods used in the current algorithms in machine vision and image processing techniques such as digital watermarking, image compression, noise reduction, etc. In this research, we will explore how machine vision is dependent on image processing algorithms in order to get important information from an image, as well as describe some of the most reliable image processing methods such as Singular Value Decomposition (SVD). Alongside SVD, mathematical models of neural networks and how they are used in the field of machine learning will be discussed. Furthermore, we will discuss some of the applications and limitations of machine vision.
Implementation Of Stego-Watermarking Technique by Encryption Image Based On Turbo Code For Copyright Application

In this internet era, there is tremendous growth in the amount of digital data that exchange and disseminate across various internet platforms, especially video. Maintaining copyright for this data is one of the essential things that made people worry. So, there is continuously question around the protection techniques of the data. Video steganography is the human visual system cannot recognise one of the methods that used to hide data inside the digital video file in away. Video watermarking is another technique used to insert additional data inside the digital video to protects its intellectual right against unauthorised manipulation. In this paper, it will use the principle of both steganography and watermarking technique to maintain the video copyright by hiding publisher logo image inside the digital video file by embedding the logo pixel data in LSB of the video frames. The most famous known of the video steganography, watermarking techniques which used in the presented-day has become traditional methods to the attackers. Therefore, there is a necessity to a new smart approach to maintaining the copyright of the digital video. In this paper, an optimised technique is proposed by utilising the advantages of Turbo code to encryption the logo image bits and least significant bit (LSB) approach to embedding the encoded logo pixels inside the frames of the cover video. This process made the system have powerful and high security against the different hacker attacks. The system proved a high quality in result Stego-video by relative rate reach to 98% from the quality of the original video and PSNR equal about (57dB) with high robustness against noise like salt and pepper and Gaussian noise.