Reviewerly/exHarmony huggingface dataset:
Pixel-Wise Phase Unwrapping with Adaptive Reference Phase Estimation for 3d Shape Measurement

Phase unwrapping plays an important role in phase-based three-dimensional (3D) shape measurement. This paper proposes an effective pixel-wise phase unwrapping method without requiring additional patterns or auxiliary cameras. Firstly, in contrast to the geometric constraint-based (GCB) method which needs calibrate the measurement projection system and create the reference phase at the closest plane within the measurement volume, the proposed method can adaptively and directly estimate the reference phase from the wrapped phase without system calibration and estimation. Secondly, based on the finding that the slope angle of the plane has significant impact on the measurement range, thus phase gradients that highly correlates with the optimal slope angle are involved into reference phase estimation. The wrapped phase can be pixel-by-pixel unwrapped by comparing with the reference phase. Both simulations and experiments have been conducted to demonstrate the efficiency of the proposed phase unwrapping method.
A Dual-Branch Dilated Convolutional Neural Network for Image Denoising

Image denoising is a long-standing low-level computer visual task, which aims to recover high-quality clean images and remove noise from noisy images. Relying on powerful learning and representation capabilities, convolutional neural networks have better performance in image denoising tasks. In this paper, we propose a dual-branch dilated convolutional neural network (DBDNet) for image denoising, which mainly includes three blocks: feature extraction block, feature enhancement block and image reconstruction block. In particular, the feature enhancement block fully excavates the detail information of different levels of images, while setting the dilated rate of the dilated convolution on two parallel branches to expand the receptive field. The proposed DBDNet method is evaluated on four public test datasets, its PSNR and SSIM have achieved excellent results, and the subjective images contain richer texture details.
Data Quality Governance Experience at the MID-NET Project

Recently, with the widespread implementation of “Real-World Data (RWD),” there is increasing interest in using high-quality administrative claims, laboratory tests, electronic health records (EHRs), and information-sharing resources to enhance detection of serious adverse drug reactions (ADRs).
AutoMC: Learning Regular Expressions for Automated Management Change Event Extraction from News Articles

Event extraction is one of the challenges to be tackled in order to extract valuable insight from unstructured text. The process of automatically identifying events in a corpus of text and extracting comprehensive information about them is called event extraction. Although a number of systems have been proposed for event extraction, there is currently no publicly available system specific to management changes. This paper presents a novel event extraction system - AutoMC - that identifies management change events from business news articles. Specifically, we propose a novel regular expressions based management change event extraction algorithm. The effectiveness of AutoMC is validated empirically over generated data sets. The experimental results show that our system performs competitively in comparison to management change event detection studies in the literature. High event detection performance (78.72% accuracy score) is achieved by the proposed event extraction rule learning method.
Improving the Universities and Enterprises’ Integration Effectiveness with the Help of Digital Technologies

To fulfill the requirements for the further digital production functions and coordinate objectively contradictory interests of interacting entities we may impede the fulfilment of these requirements, for example, the lack of the necessary level of competence of employees, uncertainty in the distribution of powers, functions, responsibilities, and interests of personnel. Because of this, in reality, more than a quarter of production resources, including equipment, do not benefit the owner of capital or employees who carry out production and innovation processes during operation. The practical implementation of the described process involves the combination and interaction of the efforts of many participants, such as regional administration, scientific and educational organizations, production and commercial and financial companies, private investors. The integration and coordination of these actors can ultimately bring about positive changes in the regional economy. First of all, such changes can include: promotion of regional products in both interregional and international markets; possibility of economic stimulation of effective interaction of regional scientific and entrepreneurial sphere. Even relatively small enterprises have an advantage over large enterprises in terms of knowledge management and innovation using digital technologies.
A Unified Sense Inventory for Word Sense Disambiguation in Polish

We introduce a comprehensive evaluation benchmark for Polish Word Sense Disambiguation task. The benchmark consists of 7 distinct datasets with sense annotations based on plWordNet–4.2. As far as we know, our work is a first attempt to standardise existing sense annotated data for Polish. We also follow the recent trends of neural WSD solutions and we test transfer learning models, as well as hybrid architectures combining lexico-semantic networks with neural text encoders. Finally, we investigate the impact of bilingual training on WSD performance. The bilingual model obtains new State of the Art performance in Polish WSD task.
Classifying Anomalous Members in a Collection of Multivariate Time Series Data Using Large Deviations Principle: An Application to COVID-19 Data

Anomaly detection for time series data is often aimed at identifying extreme behaviors within an individual time series. However, identifying extreme trends relative to a collection of other time series is of significant interest, like in the fields of public health policy, social justice and pandemic propagation. We propose an algorithm that can scale to large collections of time series data using the concepts from the theory of large deviations. Exploiting the ability of the algorithm to scale to high-dimensional data, we propose an online anomaly detection method to identify anomalies in a collection of multivariate time series. We demonstrate the applicability of the proposed Large Deviations Anomaly Detection (LAD) algorithm in identifying counties in the United States with anomalous trends in terms of COVID-19 related cases and deaths. Several of the identified anomalous counties correlate with counties with documented poor response to the COVID pandemic.
Intelligent System for Acquiring Knowledge by Converting Arabic Speech to Text

The purpose of this study is to acquire the required knowledge resulting from converting Arabic speech into text. This system passes through some stages. The initial stage is “system training.” This stage starts with identifying the lecturer's sound in different frequencies and then saving it upon an audio file and then converting it from analogue waves into digital ones; this is called “the preprocessing path.” The properties of this audio file are then extracted and stored within a template. The second stage is “system testing.” This stage compares the new sound file with the one previously stored on the system directory by comparing certain features. When the sound characteristics are matched using a similarity scale, the selected sound file has been identified. The third stage is printing the words into a text document. The next stage revises the resulting words according to specific rules identified by the proposed system itself. The final stage extracts the knowledge from the revised text. The proposed system is evaluated using Arabic dataset; it contains Arabic speech collected from different person types and ages. The experiment tests the system under three different recording modes, namely normal, quick, and loud. The results showed that the proposed system is effective in acquiring knowledge from converted Arabic speech to text especially with normal recording mode in Arabic speech recognition and classifying the fields.
On the Compatibility Between the Minimalist Foundation and Constructive Set Theory

The Minimalist Foundation $$\mathbf {MF}$$ was ideated by M.E. Maietti and G. Sambin and then completed as a formal system by M.E. Maietti in order to provide a foundation for constructive mathematics compatible with the main classical and intuitionistic, predicative and impredicative, foundational theories. Here we show that $$\mathbf {MF}$$ is in fact compatible with Aczel’s constructive set theory $$\mathbf {CZF}$$ . We prove this by extending the extensional level of $$\mathbf {MF}$$ with rules obtaining a system which turns out to be equivalent to $$\mathbf {CZF}$$ .
Design And Implementation Of 64-Bit Ripple Carry Adder And Ripple Borrow Subtractor Using Reversible Logic Gates

Minimal power design is desirable for a range of applications, including the Internet of Things (IoT), quantum computing, and so on. The reversible logic approach is at the foundation of a new technique for designing minimal power digital logic circuits for quantum computing applications. The reversible logic circuit offers a whole new approach to quantum computer processing. Reversible logic gates-based devices will be in high demand for future computer technologies since they require less power. Reversible logic gates were used to design a ripple carry adder (RCA) and a ripple borrow subtractor (RBS), which were simulated in Verilog 2018.3 and coded in Verilog HDL.
Effective Unsupervised Constrained Text Generation based on Perturbed Masking

Unsupervised constrained text generation aims to generate text under a given set of constraints without any supervised data.Current state-ofthe-art methods stochastically sample edit positions and actions, which may cause unnecessary search steps.In this paper, we propose PMCTG to improve effectiveness by searching for the best edit position and action in each step.Specifically, PMCTG extends perturbed masking technique to effectively search for the most incongruent token to edit.Then it introduces four multi-aspect scoring functions to select edit action to further reduce search difficulty.Since PMCTG does not require supervised data, it could be applied to different generation tasks.We show that under the unsupervised setting, PMCTG achieves new state-of-the-art results in two representative tasks, namely keywordsto-sentence generation and paraphrasing.
Towards a Unified Benchmark for Monocular Radial Distortion Correction and the Importance of Testing on Real-World Data

Radial distortion correction for a single image is often overlooked in computer vision. It is possible to rectify images accurately when the camera and lens are known or physically available to take additional images with a calibration pattern. However, sometimes it is impossible to identify the camera or lens of an image, e.g., crowd-sourced datasets. Nonetheless, it is still important to correct that image for radial distortion in these cases. Especially in the last few years, solving the radial distortion correction problem from a single image with a deep neural network approach increased in popularity. This paper shows that these approaches tend to overfit completely on the synthetic data generation process used to train such networks. Additionally, we investigate which parts of this process are responsible for overfitting. We apply an explainability tool to analyze the trained models’ behavior. Furthermore, we introduce a new dataset based on the popular ImageNet dataset as a new benchmark for comparison. Lastly, we propose an efficient solution to the overfitting problem by feeding edge images to the neural networks instead of the images. Source code, data, and models are publicly available at https://github.com/cvjena/deeprect .
A study of Association of Blood Groups with Anemia in healthy Young Adults

Background: Blood is a specialised connective tissue that gives each individual their identity by determining theirblood group and type. In transfusion medicine, the blood group has clinical significance. Malnutrition is one of theworld’s significant health issues, with anaemia accounting for a significant proportion of the problem, particularly inunderdeveloped nations. Reduced haemoglobin levels have an unfavourable effect on the functioning of other humanbodily systems. The association between blood groups and disorders including stomach cancer and pernicious anaemiahas been established. Previously, several research examining the relationship between anaemia and different bloodtypes yielded mixed results. As a result, the current study was conducted in order to shed light on blood groups andhaemoglobin (Hb) levels.Objectives: To determine if there is a relationship between blood group distribution and the prevalence of anaemiaamong young medical students based on the variations of haemoglobin concentration of individuals. Materials&amp; Methods: First-year MBBS and BDS students enrolled to GMC Jammu in the academic year 2020-21 wereused as subjects, and blood grouping was performed using the conventional antisera slide agglutination method. Withthe use of Sahli’s hemoglobinometer, the participants’ haemoglobin content was calculated. Hb less than 13 g/dl for males and less than 12 g/dl for females was deemed anaemia.
Deduplication: Beginning from Data Backup System

Data backup is an important application to prevent data loss caused by a disaster. But it will generate a lot of redundant data to increase the cost. In this chapter, we will describe the definitions, schemes, and key technologies of data backup, and introduce the data deduplication research originally from the data backup system. Section 1.1 describes the background of backup. Section 1.2 describes the generation of redundant data and the advantages of deduplication. Section 1.3 describes a basic architecture of a deduplication-based backup system.
Research on English Translation Correction Algorithm

To improve the accuracy of the translation software English results, this article, taking the research of translation correction algorithms, this paper discusses mainly the present situation of the English translation software, demonstrated to improve the accuracy of translation in the necessity, after the correction algorithm were analyzed, and a basic algorithm model, finally for validation algorithm model, first for translation in the process of system design, and then on the basis of this system, The advantages of the proposed algorithm can be verified by comparison with the two mainstream translation systems. Through this study, the algorithm system can effectively improve the accuracy of English translation results, and has certain advantages compared with mainstream translation systems.
Conversion of Punjabi Sign Language Using Animation

Nature has given the most precious gift to human being is the ability to listen, speak and talk. But there are some unfortunate ones who are unprivileged from this valuable gift. This creates a gap between the normal and underprivileged humans. Sign Language (SL) is the special language that is used by Deaf and Dumb people for communication. SL’s is a language which contain symbolic encoded message for communication without speech channel. In this research paper, we have focused on a Punjabi Sign Language. To develop this system we worked on daily used words of Punjabi Language. It consists of components such as Adobe Illustrator, Adobe Flash for generating SL of Punjabi Language. In this we used Illustrator for creating graphics of SL and animate them in Flash. We tested this process and results are coming in positive manners.
Recommendations on Streaming Data: E-Tourism Event Stream Processing Recommender System

The Association for Computing Machinery ACM recommendation systems challenge (ACM RecSys) [1] released an e-tourism dataset for the first time in 2019. Challenge shared hotel booking sessions from trivago website asking to rank the hotels list for the users. Better ranking should achieve higher click out rate. In this context, Trivago dataset is very important for e-tourism recommendation systems domain research and industry as well. In this paper, description for dataset characteristics and proposal for a session-based recommender system in addition to a comparison of several baseline algorithms trained on the data. The developed model is personalized session-based recommender taking into consideration user search preferences. Technically, paper compare between six different models vary from learning to rank, nearest neighbor and popularity approaches and compared results with two benchmark accuracy. Taking into consideration the ability to deploy model into production environments and the accuracy evaluation based on mean reciprocal rate as per challenge guidelines. Our winning experiment is using one learning to rank model achieving 0.64 mean reciprocal rate compared to 37 model achieving 0.68 by ACM challenge winning team [2].
Data Security

In the current world, we have moved toward cloud and cloud-native architectures, both for the platform and the infrastructure. Therefore, data security, as well as continuous application security, will focus on cloud-native security. The security must be built into the assets that you are working to secure. Integrated security also enables your workload to be mobile, from the cloud.
Analysis of obesity rates on calorie consumption of some foods in 40 Asian countries

Differences in culture, habits, and environment in different countries cause calorie intake consumed in other countries based on different types of food. This triggers an increase in obesity and malnutrition rates in different countries. The method used is a multivariate regression analysis with a sample of 40 countries located on the Asian continent and eleven independent variables. Obtained the form of a regression model to determine the level of obesity in a country. With a significance level of 5%, all assumptions of the multivariate regression model are fulfilled. Based on the results above, it can be concluded that the regression model is suitable model. Furthermore, the k-means analysis was carried out with the optimal number of k formed by the silhouette method as 4 clusters. Cluster 1 consists of 15 countries, cluster 2 consists of 10 countries, cluster 3 consists of 11 countries, and cluster 4 consists of 4 countries. It can be concluded that each country is expected to pay attention to calorie intake and the type of food consumed to decrease obesity rates in various countries.
Fact Discovery for Text Data

Fact extraction, which aims to extract (entity, attribute, value)-tuples from massive text corpora, is crucial in text data mining. Recent approaches focus on extracting facts by mining textual patterns with semantic types, where the quality of a pattern is evaluated based on content-based criteria, such as frequency. However, these approaches overlook the dimension of pattern reliability, which reflects how likely the extracted facts are correct. As a result, a pattern of good content quality (e.g., high frequency) may still extract incorrect facts. In this chapter, we consider both pattern reliability and fact trustworthiness in addressing the pattern-based fact extraction problem [1]. We give a motive example and the problem definition in Sects. 5.1 and 5.2, respectively. We detail the CNN-LSTM model design and present the experimental results in Sect. 5.3. Next, we conclude in Sect. 5.4.
Metric Learning and Adaptive Boundary for Out-of-Domain Detection

Conversational agents are usually designed for closed-world environments. Unfortunately, users can behave unexpectedly. Based on the open-world environment, we often encounter the situation that the training and test data are sampled from different distributions. Then, data from different distributions are called out-of-domain (OOD). A robust conversational agent needs to react to these OOD utterances adequately. Thus, the importance of robust OOD detection is emphasized. Unfortunately, collecting OOD data is a challenging task. We have designed an OOD detection algorithm independent of OOD data that outperforms a wide range of current state-of-the-art algorithms on publicly available datasets. Our algorithm is based on a simple but efficient approach of combining metric learning with adaptive decision boundary. Furthermore, compared to other algorithms, we have found that our proposed algorithm has significantly improved OOD performance in a scenario with a lower number of classes while preserving the accuracy for in-domain (IND) classes.
Evaluation of interface interaction efficiency of industrial design education based on virtual reality

Virtual technology is more and more widely used in various fields, such as medical treatment, military, entertainment, education and so on. However, Virtual Reality (VR) has not been well applied in education, especially for industrial design, which requires multidimensional perception system and advanced interaction technology to achieve more interactive efficiency and learning effect. The purpose of this paper is to study the effect of VR on the efficiency of classroom interaction in industrial design and to predict its application prospects. By collecting and quantifying the user's attitude and feelings through the Likert scale and semantic differential method to design questionnaire, the paper analyzes the classroom learning effect of applying VR. Then uses Kano model to predict the necessity of applying VR to promote the interaction efficiency of educational interface based on several learning characteristics of industrial design classroom. Data analysis based on 132 survey volumes shows that users are more expected to integrate VR in the classroom, especially the understanding and memoryability of the classroom interface. However, Kano model shows that application of VR is still not necessary. Therefore, it is necessary to adapt to the new visual interaction mode according to the changes in learning styles, which will bring convenience to design education.
Geochemical Characteristics of Dolomite: A Case Study from Lower Jurassic Formations, Imbricated Zone, Iraqi Kurdistan Region

The major, trace, rare earth elements, and stable isotopes (carbon and oxygen) compositions were analyzed to study the geochemical characteristics and origin of the Lower Jurassic dolomites from Sarki and Sehkaniyan formations at Imbricated Zone, Northern Iraq. The narrow range of CaO and MgO contents, their positive correlation, and high Mg/Ca values suggest the sedimentary origin of early contemporaneous dolomitization. The Sarki and Sehkaniyan dolomites are characterized by low Fe, Mn, Sr, and relatively moderate Na contents, low ΣREE contents, low formation temperatures, no LREE enrichment, and relatively negative δ 18 O values reject the effect of hydrothermal fluids in the dolomitization. The PAAS normalized REE patterns are characterized by depletion of LREE relative to HREE, distinct negative Ce, and positive Eu anomalies for most dolomites. The δ 13 C values of dolomites are consistent with those of the coeval seawater, while most of the δ 18 O values of dolomites are more negative than those of the coeval seawater of Lower Jurassic. The salinity factor indicates that the studied dolomites were formed under a low to moderate saline environment, and oxygen isotope thermometry reflects the relatively low temperature.