Reviewerly/exHarmony huggingface dataset:
Using Workspace to implement Digital Twins in the Mixed Reality Lab

The growing need for decision support, situational awareness and predictive analytics systems in real world iterative scenarios has given rise to the research field of "Digital Twins"; the combining of cyberphysical systems, artificial intelligence, computational processing platforms and mixed-reality visualisation.Digital Twins are living, digital replicas of a physical object, process or system and are comprised of three elements; a physical system; a digital model of that system; and the interactions that flow between these two systems.In this paper we describe our approach to the ongoing task of building our Mixed Reality Lab (MRL) using our Scientific Workflow System (SWS) called Workspace and describe its characteristics that make it suitable for use in producing Digital Twins for both research and commercial applications.Digital Twins need to address many of the same issues that SWSs address, for example platform independence, handling large data sets, local and remote processing, algorithm integration, hardware integration, advanced visualisation, reproducibility and scalability.In general, we believe that the elements that make our SWS applicable to the creation of Digital Twins could also apply to other SWSs, subject to their capabilities in areas discussed in this paper.Two prototypical use cases supported in our MRL, Defect Detection in Manufacturing scenarios and Human Movement (a.k.a.Digital Human) are outlined.Although these two use cases work at different scales, we see how both can be modelled in the MRL using the infrastructure constructed on top of Workspace.Capability strengths of our SWS that are of particular importance to our Digital Twin use cases are discussed.These include: The use of both continuous execution (interactive) workflows with rich user interfaces as well as distributed, cross-platform, remote execution processing workflows; Advanced interactive visualisation capabilities ranging from 2D widgets and charts, OpenGL-based 3D rendering through to a real-time Virtual and Augmented Reality pipelines using third party rendering engines; a modular, extensible plugin based architecture that exposes capabilities from many permissive open source libraries combined with in-house developed capabilities; and a streamlined deployment and productisation process to support different distribution or commercialisation needs.The MRL is very much a work in progress, optimisations to the current systems are ongoing and work to add extra facilities and infrastructure to support more scenarios is progressing.
Enhanced Bounding Box Estimation with Distribution Calibration for Visual Tracking

Bounding box estimation by overlap maximization has improved the state of the art of visual tracking significantly, yet the improvement in robustness and accuracy is restricted by the limited reference information, i.e., the initial target. In this paper, we present DCOM, a novel bounding box estimation method for visual tracking, based on distribution calibration and overlap maximization. We assume every dimension in the modulation vector follows a Gaussian distribution, so that the mean and the variance can borrow from those of similar targets in large-scale training datasets. As such, sufficient and reliable reference information can be obtained from the calibrated distribution, leading to a more robust and accurate target estimation. Additionally, an updating strategy for the modulation vector is proposed to adapt the variation of the target object. Our method can be built on top of off-the-shelf networks without finetuning and extra parameters. It yields state-of-the-art performance on three popular benchmarks, including GOT-10k, LaSOT, and NfS while running at around 40 FPS, confirming its effectiveness and efficiency.
Columns Occurrences Graph to Improve Column Prediction in Deep Learning Nlidb

Natural language interfaces to databases (NLIDB) has been a research topic for a decade. Significant data collections are available in the form of databases. To utilize them for research purposes, a system that can translate a natural language query into a structured one can make a huge difference. Efforts toward such systems have been made with pipelining methods for more than a decade. Natural language processing techniques integrated with data science methods are researched as pipelining NLIDB systems. With significant advancements in machine learning and natural language processing, NLIDB with deep learning has emerged as a new research trend in this area. Deep learning has shown potential for rapid growth and improvement in text-to-SQL tasks. In deep learning NLIDB, closing the semantic gap in predicting users’ intended columns has arisen as one of the critical and fundamental problems in this research field. Contributions toward this issue have consisted of preprocessed feature inputs and encoding schema elements afore of and more impactful to the targeted model. Various significant work contributed towards this problem notwithstanding, this has been shown to be one of the critical issues for the task of developing NLIDB. Working towards closing the semantic gap between user intention and predicted columns, we present an approach for deep learning text-to-SQL tasks that includes previous columns’ occurrences scores as an additional input feature. Overall exact match accuracy can also be improved by emphasizing the improvement of columns’ prediction accuracy, which depends significantly on column prediction itself. For this purpose, we extract the query fragments from previous queries’ data and obtain the columns’ occurrences and co-occurrences scores. Column occurrences and co-occurrences scores are processed as input features for the encoder–decoder-based text to the SQL model. These scores contribute, as a factor, the probability of having already used columns and tables together in the query history. We experimented with our approach on the currently popular text-to-SQL dataset Spider. Spider is a complex data set containing multiple databases. This dataset includes query–question pairs along with schema information. We compared our exact match accuracy performance with a base model using their test and training data splits. It outperformed the base model’s accuracy, and accuracy was further boosted in experiments with the pretrained language model BERT.
CTET+: A Beyond-Birthday-Bound Secure Tweakable Enciphering Scheme Using a Single Pseudorandom Permutation

In this work, we propose a construction of 2-round tweakable substitutionpermutation networks using a single secret S-box. This construction is based on non-linear permutation layers using independent round keys, and achieves security beyond the birthday bound in the random permutation model. When instantiated with an n-bit block cipher with ωn-bit keys, the resulting tweakable block cipher, dubbed CTET+, can be viewed as a tweakable enciphering scheme that encrypts ωκ-bit messages for any integer ω ≥ 2 using 5n + κ-bit keys and n-bit tweaks, providing 2n/3-bit security.Compared to the 2-round non-linear SPN analyzed in [CDK+18], we both minimize it by requiring a single permutation, and weaken the requirements on the middle linear layer, allowing better performance. As a result, CTET+ becomes the first tweakable enciphering scheme that provides beyond-birthday-bound security using a single permutation, while its efficiency is still comparable to existing schemes including AES-XTS, EME, XCB and TET. Furthermore, we propose a new tweakable enciphering scheme, dubbed AES6-CTET+, which is an actual instantiation of CTET+ using a reduced round AES block cipher as the underlying secret S-box. Extensivecryptanalysis of this algorithm allows us to claim 127 bits of security.Such tweakable enciphering schemes with huge block sizes become desirable in the context of disk encryption, since processing a whole sector as a single block significantly worsens the granularity for attackers when compared to, for example, AES-XTS, which treats every 16-byte block on the disk independently. Besides, as a huge amount of data is being stored and encrypted at rest under many different keys in clouds, beyond-birthday-bound security will most likely become necessary in the short term.
Determinants of Economic Growth in the European Union Countries

This paper reassesses the long-debated relationship between the financial system development and economic growth. We use not only indicators for financial access, efficiency, stability and depth of the bank-oriented financial sector, but we also consider Eurozone membership, corruption perception and competitiveness of countries to examine the determinants of economic growth. We apply a panel data approach to 27 European countries over the 2004–2017 period. By splitting the time span, we examine whether the effect of financial system development, Eurozone membership, corruption perception and competitiveness on economic growth is affected by the occurrence of financial and debt crises. Our results indicate that loans to private sector do not always support economic growth. Our research also reveals that corruption perception has a negative impact on economic growth, and so does membership in Eurozone during a crisis.
Research on Knowledge Tracing Model Based on “Problem Situation”

The understanding of the problem situation is the key to the representation of algebraic problems. Through the Hidden Markov Model (HMM), the participants’ ability to understand the problem situation is modeled, which is conducive to the individualized counseling of the participants. The traditional knowledge tracking model ignores the consideration of the problem situation, and it is not accurate to predict the mastery of comprehensive knowledge simply through the students' answering situation. The article will propose a knowledge tracking model (PS-HMM) based on the "problem context", through the participants to reconstruct the problem, show the understanding of the problem context, classify the problem in different contexts, and introduce the problem context into the model. Compare the Hidden Markov Model (sq-HMM, dq-HMM) and traditional HMM in two different situations after classification. Experiments show that the PS-HMM model can significantly improve the prediction accuracy.
Prosodic Speech Synthesis of Narratives Depicting Emotional Diversity Using Deep Learning

Shah, Shloka S. Gupta, Rishika A. Jardosh, Parth M. Nimkar, Anant V.Emotions are an essential part of speech or communication, which is why they cannot be neglected. The existing text-to-speech systems are not the most appropriate at conveying the emotions present behind the text. The systems can speak out the text monotonically lacking expressiveness. In this paper, an Expressive Text-to-Speech Synthesis System (ETSSS) is proposed which considers the dominant emotions in the text provided. ETSSS works in two parts: first, it identifies the label behind the text, and second produces expressive speech. In the first part, the input text is given an emotional label. Later, this label is used to generate expressive and prosodic speech. Labeling emotions in ETSSS is carried out using BERT which has an accuracy of 94%, 90%, and 90% for disgust, amused, and anger, respectively. The speech synthesis with the emotion module of ETSSS achieves a good MOS of 3.8 for anger, 3.5 for disgust, and 3.2 for amused.
An improved USM sharpening detection method for underwater images

In this paper, we have come up with a forensics algorithm based on adjacent pixels and multivariate coding. In the proposed method, the adjacent pixels with four directions are computed with center pixel to ferret out the overshoot artifacts caused by weak sharpening. A large number of experimental results of sharpened image detection show that the proposed method has excellent accuracy and adaptability, especially for underwater image sharpening detection.
Influence of Microwave Radiation on Whiskey Distillate Quality Indicators

The paper aims to evaluate the quality indicators of the whiskey distillate during its maturation under the influence of microwave radiation based on the method of multiple regression analysis to determine the optimal technological parameters for its production. In the course of solving the problem of product import substitution, we tested the patented technology for producing strong alcoholic beverages at the stage of their aging, using oak chips with various degrees of heat treatment. One of the promising areas for intensifying the process of whiskey distillate maturation is applying “green technologies”, with microwave heating being one of them. Positive results were obtained for the effect of microwave radiation on the process of target component extraction from oak chips with a water-alcohol solution. The duration of this extraction process was reduced by more than 2 times, which confirmed its intensity in relation to the classical method. The obtained regression models proved adequate, since the mean relative error varied within 10%. The chromatographic analysis of the whiskey produced at the optimal technological parameters showed that all physical and chemical properties and product safety meet the requirements of the applicable regulations and standards.
Training set augmentation in training neural- network language model for ontology population

This paper is a continuation of the research focused on solving the problem of ontology population using training on an automatically generated training set and the subsequent use of a neural-network language model for analyzing texts in order to discover new concepts to add to the ontology. The article is devoted to the text data augmentation - increasing the size of the training set by modification of its samples. Along with this, a solution to the problem of clarifying concepts (i.e. adjusting their boundaries in sentences), which were found during the automatic formation of the training set, is considered. A brief overview of existing approaches to text data augmentation, as well as approaches to extracting so-called nested named entities (nested NER), is presented. A procedure is proposed for clarifying the boundaries of the discovered concepts of the training set and its augmentation for subsequent training a neural-network language model in order to identify new concepts of ontology in the domain texts. The results of the experimental evaluation of the trained model and the main directions of further research are considered.
The Assessment Method of Mobile privacy security Based on AHP and Risk Matrix

research-article The Assessment Method of Mobile privacy security Based on AHP and Risk Matrix Share on Authors: Ming Yang School of Information, Yunnan University of Finance and Economics, China School of Information, Yunnan University of Finance and Economics, ChinaView Profile , Tao Zhang School of Information, Yunnan University of Finance and Economics, China School of Information, Yunnan University of Finance and Economics, ChinaView Profile , Tilei Gao School of Information, Yunnan University of Finance and Economics, China School of Information, Yunnan University of Finance and Economics, ChinaView Profile , Di Zhang School of Information, Yunnan University of Finance and Economics, China School of Information, Yunnan University of Finance and Economics, ChinaView Profile , Di Yang School of Information, Yunnan University of Finance and Economics, China School of Information, Yunnan University of Finance and Economics, ChinaView Profile Authors Info & Claims CSSE 2021: 2021 4th International Conference on Computer Science and Software Engineering (CSSE 2021)October 2021 Pages 257–261https://doi.org/10.1145/3494885.3494931Online:20 December 2021Publication History 0citation22DownloadsMetricsTotal Citations0Total Downloads22Last 12 Months22Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Formalising Geometric Axioms for Minkowski Spacetime and Without-Loss-of-Generality Theorems

This contribution reports on the continued formalisation of an axiomatic system for Minkowski spacetime (as used in the study of Special Relativity) which is closer in spirit to Hilbert's axiomatic approach to Euclidean geometry than to the vector space approach employed by Minkowski.We present a brief overview of the axioms as well as of a formalisation of theorems relating to linear order.Proofs and excerpts of Isabelle/Isar scripts are discussed, with a focus on the use of symmetry and reasoning "without loss of generality".
Stock Index Movement Prediction: A Crow Search-ELM Approach

Predicting the direction of the stock index is a challenging task to tackle because of the inconsistent and noisy stock market data. Extreme learning machine (ELM) being a simpler yet faster machine learning algorithm has the potential to be used for stock market trend analysis due to its better generalization ability. However, the performance of ELM is heavily affected by the input weights allocated to the edges connecting the input layer and hidden layer neurons. On this basis, the crow search (CS) optimization algorithm being a recent and unexplored metaheuristic approach has shown very promising outcomes in various fields of research. This study develops a stock index movement prediction framework combining ELM and CS called CS-ELM. CS-ELM optimizes the weights of ELM for getting an improved accuracy. Furthermore, its performance is compared with default ELM as well as traditional optimization algorithms in particular particle swarm optimization and differential evolution implemented on similarly developed ELM models. The empirical analysis has been carried out utilizing BSE SENSEX and NIFTY 50 benchmark stock index datasets and the outcomes show that CS-ELM achieves better forecasting performance in contrast with other prediction models.
Automated feature extraction in deep learning models: A boon or a bane?

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Machine Learning (ML) models hold a significant place in the ever-emerging field of Artificial Intelligence (AI). There are several ML approaches whose performance varies with respect to the nature of the data and the type of the application. Till the early 2000's, Artificial Neural Networks (ANN) were considered to be the prime ML model which is advantageous in an omnidirectional way. Suddenly, somewhere in the mid 2000's, a new variation called as “Deep Learning” gained significant attention which slowly started side-lining the conventional ANN. It is interesting to note that the entire world started moving towards DL. What is the difference between conventional ANN and DL models? The answer from everyone will be the same: increase in performance measures. But, how and why? You will hardly receive an answer for the second question. This work tries to give an answer for this question. There are two prime differences: (1) Hand-crafted features on which ANN is dependent on is eliminated in DL models and (2) The number of layers in the architecture of DL models is significantly high. Feature extraction is the process which is normally used to extract the significant features of any data. However, when a human user doesn't have an expertise on the data, the hand-crafted features may be irrelevant which leads to the inferior performance of ANN. Hence, blaming the conventional ANN for weak performance may not be completely correct. Instead, what if we give the complete responsibility of feature extraction to the algorithm/model itself? This is the main reason for the increased reason of the DL models. How does the DL models extract features on its own? As a case study, Convolutional Neural Networks (CNN) depend on huge number of convolutional layers for feature extraction. The initial layers extract the low-level features and the higher order layers extract the high-level features. Thus, one must pay the price for the increased performance measure in the form of huge number of layers which leads to computational complexity. This situation leads to a confused mind whether the performance measure is important or the computational complexity. On the other hand, it throws open plenty of opportunities for research in the area of DL to solve the complexity problem without sacrificing the performance measures.
Route Planning Algorithms in Civil Aviation: A Brief Review and Future Prospects

Route planning is one of the core technologies in civil aviation. While various algorithms have appeared in the literature, the overall algorithmic scheme remains unchanged: A route subject to the aircraft nonholonomic constraints such as minimum turning radius and environmental restrictions like atmospheric disturbances, gust and adverse climates must be planned to achieve optimization goal of the airline, for example, minimum travelling time and lowest fuel consumption. This paper briefly reviews 5 different algorithms which have been implemented in addressing the civil aviation route planning problems. We highlight strengths for each algorithm. A summary, as well as prospects for the route planning algorithms development in civil aviation is also presented.
Massive rise in fraud losses

The UK's National Fraud Intelligence Bureau (NFIB) has reported a massive spike in losses attributed to technology-enabled fraud and cybercrime in the first half of 2021.
THE EFFECT OF ENTERPRISE RISK MANAGEMENT, KNOWLEDGE MANAGEMENT, AND ORGANIZATIONAL CULTURE ON ORGANIZATIONAL RESILIENCE

This study aims to identify the influence of Enterprise Risk Management, Knowledge Management, and Organizational Culture on the company's ability to survive in an ever-changing environment. This research is a quantitative study using primary data. The data were obtained through a questionnaire distributed to accountants who held the lowest supervisory positions in private Indonesian companies. With a sample of 103 respondents, the data were processed using linear regression. This study found that in the context of companies in Indonesia, Enterprise Risk Management and Organizational Culture can increase Organizational Resilience. However, this study failed to prove the role of Knowledge Management in Organizational Resilience. This study provides contributions in both managerial and theoretical aspects. In the managerial aspect, this study implies ERM and organization culture are elements that should be implemented so that companies can survive in a volatile environment. From a theoretical point of view, this research has proven that in the long term ERM is able to maintain organizational resilience. Besides, this study also indicates that knowledge management is still not widely applied by companies in Indonesia. For this reason, the attention of management so that knowledge management is applied needs to be improved.