Reviewerly/exHarmony huggingface dataset:
Use of white light profiles for the contouring of objects

We propose the obtaining of the 3D shapes by scanning with a light line of generated digitally Gaussian, triangular or sinusoidal profile. Algorithms based on the Bezier curves, linear regression, and nonlinear regression methods were implemented, which allowed to obtain the skeleton of each line of light captured with sub-pixel accuracy. The height measurements obtained with each projected profile were compared with a coordinate measuring machine (CMM). Finally, reconstruction efficiency was determined for each projected light profile in terms of accuracy and processing time.
Conceiving of Resilient Railway Systems

The railway systems are based on large, complex and highly developed cyber physical systems (CPS) that require powerful modeling capabilities. The current approach of railway resilience is based on newly introduced Object Enhanced Real-Time Petri Net (OER- TPN) models that include tokens with temporal information. Classes of high impact disturbances including changes of infrastructure and variations of train temporal parameters determine significant modifications of railway traffic behaviors. OER-TPN models can be used to formally describe, conceive and measure the traffic resilience when the classes of disturbances occur. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Giving Guidance to Graphs: Evaluating Annotations of Data Visualizations for the News

This study quantifies the effectiveness of information recall with direct and indirect labeling of the annotation layer in a news data visualization. Variations of three data visualizations from The New York Times, The Washington Post, and The Wall Street Journal were presented to participants in a crowdsourced experiment to measure their story comprehension. Our results demonstrate that direct labeling offers no advantage over indirect labeling. More significantly, annotations on visualizations do no better to enhance comprehension than visualizations without them, contradicting data visualization orthodoxy.
Framework Based on Gestalt Principles to Design Mobile Interfaces for a Better User Experience

This paper presents the results of the user experience test comparing a real functional application and a high fidelity prototype that used a Framework to design graphic user interfaces on mobile devices. This Framework links Nielsen’s heuristics with the principles of perception of Gestalt, offering to developers and usability experts, references to generate and evaluate mockups and prototypes. The constructive and evaluative model of the Framework allows to recognize usability criteria in visual components of the interfaces, during the initial phases of a project that uses agile software development methodologies, reducing the “trial - error” regressions. The experiment allowed obtaining data about satisfaction measures and specific user attitudes regarding the interfaces developed.
MBSE Product Line Engineering – Variability Overview lost?

The article introduces an approach and framework that combines both variability in product line development and reuse of product line assets in customer projects using proven architecture frameworks with some enhancements in one approach.
Design of Seat Selecting System for Self-study Room in Library Based on Smart Campus

A seat selecting system for self-study room in library is designed to integrate the sub-platform of “Campus Service” in smart application layer of the existed architecture of smart campus. The system is based on Struts–Spring–Hibernate (SSH) architecture, and Android platform is used for the client side. Mode of process driven is used for data exchange of the system to implement the major functions of seat search, seat reservation, my seat view, and seat selecting conveniently. The application of the system achieves good results in author’s experimental university. Finally, further work is expected on development of the system in this paper.
Multiple Kernel Subspace Clustering Based on Consensus Hilbert Space and Second-Order Neighbors

How to deal with data sets in high-dimensional space is the focus of image processing. At present, subspace clustering method is one of the most commonly used methods for processing high-dimensional data sets. Traditional subspace clustering assumes that the data comes from different linear subspaces, and the different subspace regions do not overlap. However, the real data often does not satisfy these two constraints, which affects the effect of subspace clustering. In order to deal with these two problems, we first introduce the consensus Hilbert space to solve the nonlinear problem of subspace data, that is, a method that can learn the best consensus Hilbert space from a given set of kernel space; The second-order neighbors of the subspace coefficient matrix are used to deal with overlapping subspace problems. The second-order neighbors can effectively optimize the sparseness and connectivity of the self-representation. Subsequently, in order to effectively handle the above complex iterative optimization steps, an alternating direction multiplier method was developed. Finally, the designed multiple kernel subspace clustering algorithm is tested on a total of sixteen real data sets of four types, including face, handwritten, UCI and biomedical data sets. It can be seen from the experimental results that our proposed method has improved clustering accuracy, normalized mutual information and adjusted rand index compared to the latest algorithms.
Image-Based Product Recommendations Using Market Basket Analysis

Most online shopping search engines are still largely depending on the knowledge base and use keyword matching as their search strategy to find the most likely product that consumers want to buy. This is inefficient in a way that the description of products can vary a lot from the seller's side to the buyer's side. The proposed interactive product recommendation method considers not only the product history of the customer but also the linkage between the same products bought by other customers to recommend new products. The image was provided as an input to the model, and Convolutional Neural Networks has been used to classify the image, and recommendations are generated using market basket analysis.
Complexity bounds for approximately solving discounted MDPs by value iterations

For an infinite-horizon discounted Markov decision process with a finite number of states and actions, this note provides upper bounds on the number of operations required to compute an approximately optimal policy by value iterations in terms of the discount factor, spread of the reward function, and desired closeness to optimality. One of the provided upper bounds on the number of iterations has the property that it is a non-decreasing function of the value of the discount factor.
GDBApex: A graph‐based system to enable efficient transformation of enterprise infrastructures

Summary Graph‐based database engines have been developed by different researchers and companies. Many optimization methods have been integrated within these engines to enable fast and efficient data processing. However, many small‐ and medium‐size organizations have not changed their database infrastructures and still rely on a relational management modeling approach. This limits their service performance, especially in today's large‐scale data processing requirements. Transformation to the use of graph‐based modeling and design is not a straightforward process. In order to make a successful transformation, correct process semantics as well as the design of vertices, edges, labels, and process relations are required. The goal of this article is to help small‐ and medium‐size organizations make this transformation successful in order to satisfy customers' expectations and meet the requirements of data‐intensive applications. The proposed graph‐based modeling approach uses a graph structure for semantic queries and applies software engineering design principles. Moreover, it provides a case study with many data transactions. The system outperformed relational database management systems by an order of magnitude. Scalability of the system is examined and compared with the regular relational‐based modeling. In addition, a load balancing solution is used to achieve high scalability.
Digitizing the Maintenance Documentation; a System of Systems in Oil and Gas Industry

This paper explores the digital transformation of maintenance documentation for systems of systems in the oil and gas industry. The question is how to create and apply maintenance documentation more effectively to shorten delivery time, create cost-efficient systems and services. Our research is based on semi-structured interviews with stakeholders working with documentation, aftermarket services and engineering of subsea solutions. We transferred the interviews into needs, stakeholder requirements and personas, and draw the picture of “as-is” and “to-be” states. The paper clearly illustrates the current, manual way of producing and utilizing the documentation. A core contribution from this paper is the process of generating preservation, storage and maintenance documentation for complex subsea systems of systems. We propose a model for digital generation of required documentation. A need-based transfer to a digital process has a huge potential for enabling more efficient and effective maintenance and subsequent repair of the subsea systems.
Comparison Of Pso-Based Naive Bayes And Naive Bayes Algorithm In Determining The Feasibility Of Bumdes Credit

Abstract In analyzing a credit, sometimes an analysis performs an inaccurate analysis so that some customers are less able to make credit installment payments, resulting in less smooth or even bad loans. From these problems, researchers conducted a credit analysis using computerized techniques using RapidMiner software in data processing. The right data processing technique to use is classification. One method of data mining classification is the Naive Bayes algorithm. Researchers use weighting by implementing Particle Swarm Optimization (PSO) for attribute selection to improve the accuracy of Naive Bayes. After testing with two models namely Naive Bayes algorithm and Naive Bayes based on PSO, the results obtained are for the Naive Bayes algorithm with an accuracy value of 93.24%, while the Naive Bayes algorithm based on particle swarm optimization models produces a higher accuracy value of 98.16% compared to the Naive Bayes algorithm model. From these results, the difference between the two models is 4.92%. Then for the results of using the ROC curve for both models, for the Naive Bayes algorithm, the AUC value is 0.939 with an Excellent Classification diagnostic level, and for the Naive Bayes algorithm model based on particle swarm optimization, the AUC value is 0.977 with an Excellent Classification diagnostic level. From the evaluation of the ROC curve, it is seen that the Naive Bayes model based on particle swarm optimization is higher when compared to the Naive Bayes algorithm. From the results of the AUC, the difference between the two models is 0.038.
Recommending the Workflow of Vietnamese Sign Language Translation via a Comparison of Several Classification Algorithms

The Vietnamese deaf community communicates via a special language called Vietnamese Sign Language (VSL). Three-dimensional space and hand gesture are primarily used to convey meanings that allow deaf people to communicate among themselves and with non-deaf people around them. It maintains syntax, grammar, and vocabulary which is completely different from regular spoken and/or written Vietnamese. The normal procedure of transformation from spoken and/or written language (SWL) to VSL consists of consecutive steps: (i) Vietnamese word tokenization, (ii) machine translation into written sign language sentences, and (iii) conversion of these written sign language sentences into visual sign gesture. In this procedure, the second step gets the most attention due to the completion of the conveyed message. The basic challenge is that sign language, in general, has limited vocabulary compared to spoken/written language. If the machine translation is poorly performed, the complete message might not be successfully communicated, or in some cases, the conveyed message has a different meaning from the original. Consequently, the high accuracy translation should be maintained in any circumstances. This research is the efforts of evaluating an effective classification algorithm that the authors recommend to be integrated into the workflow of VSL translation. We believe that this is the first showing a quantitative comparison of several classification algorithms used in the workflow. The experimental results show that the translation accuracy rate is 96.7% which strongly support our recommendation.
Dynamic prioritization of surveillance video data in real-time automated detection systems

Automated object detection systems are a key component of modern surveillance applications. These systems rely on computationally expensive computer vision algorithms that perform object detection on visual data recorded by surveillance cameras. Due to the security and safety implications of these systems, this visual data st be processed accurately and in real-time. However, many of the frames that are created by the surveillance cameras may be of low importance, providing little or no useful information to the object detection system. Sub-sampling surveillance data by prioritizing important camera frames can greatly reduce unnecessary computation. Consequently, several works have explored dynamic visual data sub-sampling using various modalities of information (ie. spatial or temporal information) for prioritization. Few works, however, have combined and evaluated different modalities of information together for real-time prioritization of visual surveillance data. This work evaluates several individual and combined prioritization metrics derived from different modalities of information for use with a modern deep learning-based object detection algorithm. Both processing time and object detection rate are measured and used to rank the prioritization metrics. A novel approach that uses the historical detection confidences created by the object detection algorithm was demonstrated to be the best standalone prioritization metric. Additionally, a novel ensemble method that uses a KNN regressor to combine the best of the previously evaluated metrics to create a dynamic prioritization method is presented. This ensemble approach is shown to increase the object detection rate by up to 60% as compared to a static sub-sampling baseline as demonstrated using three publicly available datasets. The increased object detection rate was achieved while meeting the real-time constraints of the automated object detection system.
Synthesis through unification genetic programming

We present a new method, Synthesis through Unification Genetic Programming (STUN GP), which synthesizes provably correct programs using a Divide and Conquer approach. This method first splits the input space by undergoing a discovery phase that uses Counterexample-Driven Genetic Programming (CDGP) to identify a set of programs that are provably correct under unknown unification constraints. The STUN GP method then computes these restraints by synthesizing predicates with CDGP that strictly map inputs to programs where the output will be correct.
A New Bit Plane Specific Longest Repeating Pattern Test for Statistical Analysis of Bit Sequences

Most of the existing randomness tests treat whole bit sequence as a stream of bits in one-dimensional space for analysis. In this paper, a new bit plane specific statistical test for randomness analysis of bit sequences in two-dimensional space is presented. Primary focus of this test is to analyze the length of longest repeating pattern (LRP) in a binary random sequence. A random sequence is converted into matrix of L × L bytes. Then analysis of LRP is done for each row and column of each bit plane of the matrix. Experimentations are carried out on cipher data of four block ciphers, image data, and random alphabetic data of various sizes. It is observed that cipher data exhibits good randomness but image and random alphabetic data exhibits non-randomness in bit planes. The proposed test has the capability to capture the patterns by performing two-dimensional analysis in bit planes which may not be possible to capture by existing NIST statistical tests. It has been shown that sequences accepted as random by NIST suite are rejected by proposed LRP test.
When Do Design Workshops Work (or Not)?

This provocation invites reflection on the use of design workshops in research. We are concerned that often, design workshops don't work; at least, not as their facilitators might have intended, or as effectively as they could do. In this short paper we draw from our own experiences of organizing and attending design workshops, to observe common challenges in using design workshops as a research tool. Though critical, we intend this paper to serve as a point of reflection for running more purposeful workshops, and being able to better articulate the research and design developed through them.
Dynamic Visualization of Uncertainties in Medical Feature of Interest

In a medical context, uncertainty visualization of a Feature of Interest (FOI) is important, which could provide medical experts with informative feedback to make better decisions for diagnosis or preoperative planning. Traditional uncertainty visualization techniques allow the exploration of the uncertainty visualization of the FOI. However, when the intensity of the FOI and other materials is similar or overlapping, and/or the FOI is occluded by other materials, it would be difficult or impossible for them to reveal the FOI and its uncertainties. To address this problem, we propose an uncertainty visualization technique that includes two main components: the Multiattribute-rule-based FOI Segmentation (M-rule Seg. for short) and the FOI Dynamic Uncertainty Visualization. To demonstrate its effectiveness, we performed an experiment comparing it with two traditional uncertainty visualization techniques. The experimental results show that our proposed uncertainty visualization technique allows to better reveal the FOI and all its uncertainties that are difficult or impossible to be visualized by traditional uncertainty visualization techniques. In addition, we performed another experiment to compare our proposed M-rule Seg. technique with three traditional segmentation techniques. The experimental results show that our M-rule Seg. technique can generate more accurate segmentation results at the cost of a longer computing time.
MAIN TRENDS IN INFORMATION SUPPORT OF SCIENTIFIC ORGANIZATIONS OF THE AGRICULTURAL PROFILE OF SIBERIA

The article highlights the main areas of activity of the Siberian Scientific Agricultural Library-Branch of the SPSL SB RAS in the field of information support of agrarian science of Siberia. The results of a scientific study conducted by SibNSKHB in 2017–2019 on the study of the information needs of scientists from agricultural organizations of Siberia in the context of a large-scale reorganization of science are presented.
On the Algebraic Combinatorics of Injections and its Applications to Injection Codes

We consider the algebraic combinatorics of the set of injections from a k-element set to an n-element set. In particular, we give a new combinatorial formula for the spherical functions of the Gelfand pair (S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> × S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> , diag(S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> ) × S <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n-k</sub> ). We use this combinatorial formula to give new Delsarte linear programming bounds on the size of codes over injections.
A robust blind watermarking algorithm for depth-image-based rendering 3D images

The protection of 3D contents from illegal distribution has attracted considerable attention and depth-image-based rendering (DIBR) is proved to be a promising technology for 3D image and video displaying. In this paper, we propose a new digital watermarking scheme for DIBR 3D images based on feature regions and ridgelet transform (RT). In this scheme, the center view and the depth map are made available at the content provider side. After selecting the reference points of the center view, we construct the feature regions for watermark embedding. Considering the sparse image representation and directional sensitivity of the RT, the watermark bits are embedded into the amplitudes of the ridgelet coefficients of the most energetic direction. The virtual left and virtual right views are generated from the watermarked center view and the associated depth map at the content consumer side. The watermarked view has good perceptual quality under both the objective and subjective image quality evaluations. The embedded watermark can be detected blindly with low bit error rate (BER) from the watermarked center view, the synthesized left and right views even when the views are distorted and distributed separately. The experimental results demonstrate that the proposed scheme exhibits good performance in terms of robustness against various image processing attacks. Meanwhile, our method can be robust to common DIBR processing, such as depth image variation, baseline distance adjustment and different rendering conditions. Furthermore, compared with other related and state-of-the-art methods, the proposed algorithm shows higher accuracy in watermark extraction.
Mining High Utility Co-location Patterns Using the Maximum Clique and the Subsume Index

Mining high utility co-location patterns (HUCP) is a promising technique in spatial data mining because it treats different features with different levels of importance. Existing HUCP mining (HUCPM) algorithms are based on row instances and table instances, leading to high computational cost. To overcome this problem, an HUCPM algorithm based on the subsume index (HUCPM-SI) is developed using a typical high utility itemset mining model. Maximal cliques are first discovered, enabling the spatial database to be transformed into a clique transaction database. The subsume index is then used to mine HUCPs. Tests conducted on synthetic and real datasets demonstrate the advantages of the HUCPM-SI algorithm.
Evaluating cross-lingual textual similarity on dictionary alignment problem

Bilingual or even polylingual word embeddings created many possibilities for tasks involving multiple languages. While some tasks like cross-lingual information retrieval aim to satisfy users’ multilingual information needs, some enable transferring valuable information from resource-rich languages to resource-poor ones. In any case, it is important to build and evaluate methods that operate in a cross-lingual setting. In this paper, Wordnet definitions in 7 different languages are used to create a semantic textual similarity testbed to evaluate cross-lingual textual semantic similarity methods. A document alignment task is created to be used between Wordnet glosses of synsets in 7 different languages. Unsupervised textual similarity methods—Wasserstein distance, Sinkhorn distance and cosine similarity—are compared with a supervised Siamese deep learning model. The task is modeled both as a retrieval task and an alignment task to investigate the hubness of the semantic similarity functions. Our findings indicate that considering the problem as a retrieval and alignment problem has a detrimental effect on the results. Furthermore, we show that cross-lingual textual semantic similarity can be used as an automated Wordnet construction method.