Reviewerly/exHarmony huggingface dataset:
A novel enhanced quantum image representation based on bit-planes for log-polar coordinates

Quantum image representation has a significant impact in quantum image processing. In this paper, a bit-plane representation for log-polar quantum images (BRLQI) is proposed, which utilizes $(n+4)$ or $(n+6)$ qubits to store and process a grayscale or RGB color image of $2^n$ pixels. Compared to a quantum log-polar image (QUALPI), the storage capacity of BRLQI improves 16 times. Moreover, several quantum operations based on BRLQI are proposed, including color information complement operation, bit-planes reversing operation, bit-planes translation operation and conditional exchange operations between bit-planes. Combining the above operations, we designed an image scrambling circuit suitable for the BRLQI model. Furthermore, comparison results of the scrambling circuits indicate that those operations based on BRLQI have a lower quantum cost than QUALPI. In addition, simulation experiments illustrate that the proposed scrambling algorithm is effective and efficient.
Visual Expression Analysis from Face Images Using Morphological Processing

Visual Expression Analysis is an active field of computer vision where any given scenario’s emotion is analyzed as anger, disgust, fear, surprise, sadness, contempt, happiness and many more. Human facial expression can be used as an effective tool in this research arena. Detecting emotion requires identifying whether there is a face or not in the image. Most of the systems were prepared with grayscale images, but this manuscript proposes using MTCNN, a face detector that recognizes faces from RGB images. The methodology includes RGB color images of customized dataset of Flickr. The face is considered as the region of interest (ROI) from any given image. ROI is further converted into binary images after evaluating the combinations of morphological operations (erosion, dilation, opening and closing) that selects the best morphological technique i.e. subtracting the eroded images form the gray images for retrieving facial features. After extracting the features, Random Forest, Logistic Regression, SVM, xGBoost, GBM and CNN classifiers have been implemented to get the best classifier. Consequently, based on the performance analysis, CNN is the best model with 99.71% train accuracy and 98.01% test accuracy to classify four facial expressions: ‘anger’, ‘happiness’, ‘sadness’ and ‘surprise’.
Optical Remote Sensing Image Ship Detection Method for Improving EfficientDet

To address the false alarm and missed detection problems caused by large differences in ship types, ships side by side and complex backgrounds in optical remote sensing images, a High-Efficiency Channel Attention Network (HECAN) model is proposed in this paper. We enhance the detection capability of the model for small-scale ships based on the EfficientDet model by using an efficient channel attention optimized feature extraction network to solve the channel information loss problem. Meanwhile, the Soft-NMS is used to improve the non-maximum suppression algorithm of the model to improve the recall rate of side-by-side ships. The results are compared with other object detection models and show that the HECAN model detects better on small-scale and side-by-side ships with an average precision of 92.46% and and a recall rate of 4.75% higher than the original EfficientDet.
ANALYSIS OF RELEVANT CHARACTERISTICS OF INTERJECTION AUDIO SIGNALS ON THE EXAMPLE OF THE RUSSIAN LANGUAGE

This article describes a method for analyzing the relevant characteristics of audio signals of in-terjections on the example of the Russian language. Audio signals interjections WOW! recorded on a dictaphone and pronounced with different intonation. Interjections are recorded as WAV files for the Russian language using the Praat software product.
A ‘seamless’ experience of data sharing through a ‘seamful’ engagement

This paper explores conceptually and empirically the complex and multi-layered networks of interaction where personal data is shared across different platforms.This is explored through the vocabulary and tensions established by 'seamful' and 'seamless' design approaches and their limitations in allowing people to track the flow of personal data across complex assemblages of digital services.These ideas are explored using a participatory method to support the design of Inclusive Digital Services where a prototype of seamless service related to data reuse and sharing is co-designed to address friction, effort, risk and cost (FERC) inherent to third sector services.The conclusion suggests that we need to foster a new attitude towards the development of trust in human-computer interaction.This can be achieved by promoting the turn to privacy by design and addressing the gap between 'seamless' and 'seamful' design through participatory methods.
Involving Users in Infrastructure Development: Methodological Reflections From the Research Pilot Projects Using the CLARIAH Media Suite

In this contribution we introduce a compilation of articles that reflect on the use of the CLARIAH Media Suite and its impact on research methods, as they were conducted during the CLARIAH research pilot projects (2017-18). The pilot projects fit in a co-development approach in which users are involved in infrastructure development from the start. We discuss how feedback of the pilot researchers was incorporated in the iterative development and testing of the Media Suite.
Generating Artificial Weather Data Sequences for Solar Distillation Numerical Simulations

According to the natural geographical distribution, developing countries are concentrated in tropical climates, where radiation is abundant. So the use of solar energy is a sustainable solution for developing countries. However, daily or hourly measured solar irradiance data for designing or running simulations for solar systems in these countries is not always available. Therefore, this chapter presents a model to calculate the daily and hourly radiation data from the monthly average daily radiation. First, the chapter describes the application of Aguiar’s model to the calculation of daily radiation from average daily radiation data. Next, the chapter presents an improved Graham model to generate hourly radiation data series from monthly radiation. The above two models were used to generate daily and hourly radiation data series for Ho Chi Minh City and Da Nang, two cities representing two different tropical climates. The generated data series are tested by comparing the statistical parameters with the measured data series. Statistical comparison results show that the generated data series have acceptable statistical accuracy. After that, the generated radiation data series continue to be used to run the simulation program to calculate the solar water distillation system and compare the simulation results with the radiation data. Measuring radiation. The comparison results once again confirm the accuracy of the solar irradiance data generation model in this study. Especially, the model to generate the sequences of hourly solar radiation values proposed in this study is much simpler in comparison to the original model of Graham. In addition, a model to generate hourly ambient tempearure date from monthly average daily ambient temperature is also presented and tested. Then, both generated hourly solar radiation and ambient temperature sequences are used to run a solar dsitillation simulation program to give the outputs as monthly average daily distillate productivities. Finally, the outputs of the simulation program running with the generated solar radiation and ambient temperature data are compared with those running with measured data. The errors of predicted monthly average daily distillate productivities between measured and generated weather data for all cases are acceptably low. Therefore, it can be concluded that the model to generate artificial weather data sequences in this study can be used to run any solar distillation simulation programs with acceptable accuracy.
New Stereo Vision Algorithm Composition Using Weighted Adaptive Histogram Equalization and Gamma Correction

This work presents the composition of a new algorithm for a stereo vision system to acquire accurate depth measurement from stereo correspondence. Stereo correspondence produced by matching is commonly affected by image noise such as illumination variation, blurry boundaries, and radiometric differences. The proposed algorithm introduces a pre-processing step based on the combination of Contrast Limited Adaptive Histogram Equalization (CLAHE) and Adaptive Gamma Correction Weighted Distribution (AGCWD) with a guided filter (GF). The cost value of the pre-processing step is determined in the matching cost step using the census transform (CT), which is followed by aggregation using the fixed-window and GF technique. A winner-takes-all (WTA) approach is employed to select the minimum disparity map value and final refinement using left-right consistency checking (LR) along with a weighted median filter (WMF) to remove outliers. The algorithm improved the accuracy 31.65% for all pixel errors and 23.35% for pixel errors in nonoccluded regions compared to several established algorithms on a Middlebury dataset.
Modeling Spiky Functional Data With Derivatives of Smooth Functions in Function-on-Function Regression

Smoothness penalty is an efficient regularization and dimension reduction tool for functional regression.However, for spiky functional data observed on a dense grid, the coefficient function in functional regression can be spiky and hence the smoothness regularization is inefficient and leads to over-smoothing.In this paper, we propose a novel approach to fit the functional-on-function regression model by viewing the spiky coefficient functions as the derivatives of smooth auxiliary functions.Compared to smoothness regularization or sparsity regularization which are imposed directly on the spiky coefficient function in existing methods, imposing smoothness regularization on the smooth auxiliary functions can more efficiently reduce the dimension and improve the performance of fitted model.With the estimated smooth auxiliary functions and taking derivatives, we can fit the model and make prediction.Simulation studies and real data applications show that compared to the existing methods, the new method can greatly improve model performance when the coefficient function is spiky, and performs similarly well when the coefficient function is smooth.
ALBERT-Based Self-Ensemble Model With Semisupervised Learning and Data Augmentation for Clinical Semantic Textual Similarity Calculation: Algorithm Validation Study (Preprint)

<sec> <title>BACKGROUND</title> In recent years, with increases in the amount of information available and the importance of information screening, increased attention has been paid to the calculation of textual semantic similarity. In the field of medicine, electronic medical records and medical research documents have become important data resources for clinical research. Medical textual semantic similarity calculation has become an urgent problem to be solved. </sec> <sec> <title>OBJECTIVE</title> This research aims to solve 2 problems—(1) when the size of medical data sets is small, leading to insufficient learning with understanding of the models and (2) when information is lost in the process of long-distance propagation, causing the models to be unable to grasp key information. </sec> <sec> <title>METHODS</title> This paper combines a text data augmentation method and a self-ensemble ALBERT model under semisupervised learning to perform clinical textual semantic similarity calculations. </sec> <sec> <title>RESULTS</title> Compared with the methods in the 2019 National Natural Language Processing Clinical Challenges Open Health Natural Language Processing shared task Track on Clinical Semantic Textual Similarity, our method surpasses the best result by 2 percentage points and achieves a Pearson correlation coefficient of 0.92. </sec> <sec> <title>CONCLUSIONS</title> When the size of medical data set is small, data augmentation can increase the size of the data set and improved semisupervised learning can boost the learning efficiency of the model. Additionally, self-ensemble methods improve the model performance. Our method had excellent performance and has great potential to improve related medical problems. </sec>
Journey of Letters to Vectors Through Neural Networks

Getting an accurate image description has been one of the most discussed topics in the field of Artificial Intelligence. Numerous models/techniques have been developed in the past few years which makes it difficult to trace the exact path of Image Captioning models. This paper attempts its best to give the reader a clear idea of the evolution in the field of image captioning research elaborating on both traditional procedures and the advancements made with the aid of deep learning. This paper is aimed to discuss methods in detail and understand the very essence of depth and logic behind. It also relies upon the ravishing brilliance proceeded by the forthcoming authors. Further it shreds luminance on how the idea can grow in the near and long future.
Concurrent Control Algorithm for Large-scale Remote Multi-user Information Management System

When the current concurrency control algorithm is used to control of the multi-user information management system, the system’s channel transmission capability is low, and the time it takes is long. In this paper, a concurrency control algorithm for large-scale remote multi-user information management system is proposed. According to the average use rate of the large-scale remote multi-user information management system, the concurrency control structure and state of the system are analyzed and judged; Through the analysis of the results, the delay of data link layer in multi-user information management system is carried out modeling; Combined with the queuing delay and accessing delay, the large-scale remote multi-user information management system control can be realized. Experimental results show that the channel utilization rate of the proposed algorithm is over 98.3%, which can transmit large amounts of information in a relatively short time and concurrency control of information management system. Therefore, the proposed algorithm has high channel utilization and efficiency of information transmission
Feasibility of creation and use of specialized search systems for scientific and technical information

The efficiency of using global Internet search systems versus specialized systems, including the State System of Scientific and Technical Information of the Republic of Belarus, for searching the scientific and technical information is compared in this article. Potential advantages and disadvantages of STI search systems are considered. The experience of individual states in creating and maintaining the functioning of state resources of scientific and technical information is analyzed.
High-Confidence Sample Labelling for Unsupervised Person Re-identification

Person re-identification (re-ID) is factually a topic of pedestrian retrieval across camera scenes. However, it is challenging due to those factors such as complex equipment modeling, light change and occlusion. Much of the previous research is based on supervised methods that require labeling large amounts of data, which is expensive and time-consuming. The unsupervised re-ID methods without manual annotation usually need to construct pseudo-labels through clustering. However, the pseudo-labels noise may seriously affect the model’s performance. To deal with this issue, in this paper, we use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to assign pseudo-labels to samples and propose a model with the high-confidence samples’ labels (HCSL), which is a fully unsupervised learning method and does not use any labeled data. The model constructs high-confidence triplets through cyclic consistency and random image transformation, which reduces noise and makes the model finely distinguish the differences between classes. Experimental results show that the performance of our method on both Market-1501 and DukeMTMC-reID performs better than the latest unsupervised re-ID methods and even surpasses some unsupervised domain adaptation methods.
Research on Teaching Reform of C Programming Language Course for Deep Integration of Professional Fields

Advanced Language Program (C language) is a required basic course of computer technology for science and engineering majors at Sichuan University of Science and Engineering.In order to improve students' abilities of computational thinking, mathematical modeling, algorithm design, problem analysis and solution, this paper puts forward a C language curriculum reform oriented to the deep integration of the professional fields of the lecturers in view of the current C language teaching and experiment without considering the professional characteristics of the lecturers.Taking electronic information science as an example, the C language course plan related to the subsequent courses is designed according to the major characteristics of the students in class 2020.Compared with the final scores of 2018 and 2019, it can be found that the program is conducive to improving students' interest in learning, deepening their understanding of the theoretical knowledge of the course, improving their ability of programming practice and deep integration with the professional field.
Methodology for Assessing Competence-Based Educational Programs

A methodology for evaluating educational programs has been developed. The role of the educational program assessment system in the ESM quality strategy and the implementation of the assessment process of competence-based educational programs in this system are described. Key user roles and functions are indicated. The main evaluation criteria are presented.
Recursive adjectival modification in CLLRS

This paper sketches an analysis in Lexical Resource Semantics of adverbial and adjectival modification in nominal projections which is extensible to modification of other syntactic categories. It combines insights into the syntax-semantics interface of recursive modification in HPSG with underspecified semantics and type-logical meaning representations in the tradition of Montague grammar. The analysis is phrased in such a way that it receives a direct implementation in the Constraint Language of Lexical Resource Semantics as part of the TRALE system.
Communicative activities to enhance oral production in the EFL classroom

Introduction. Speaking is considered one of the most difficult skills to be developed in class when teaching a foreign language. Therefore, teachers need to look for alternatives to enhance learners’ oral production. In this regard, Communicative activities (CA) play a fundamental role in language teaching due to the fact they provide them with opportunities to use language as in real situations. Objective. The main goal of this research is to provide a general overview of Communicative activities to enhance oral production in the EFL classroom. Methodology. This article was based on the qualitative approach. A descriptive - documentary research was carried out through an extensive bibliographic literature review about the main principles of the Communicative Approach, communicative activities, types of CA, and speaking. Results. Results from the reviewed research investigations show that they are quite useful for teachers and learners since CA facilitate learners’ oral production employing the interaction among classmates and teachers. Conclusion. Besides, Communicative activities encourage learners to develop their language competencies due to the dynamic in which these activities are carried out in the EFL classroom.
[Main Control Factors of Cadmium Content in Rice in Carbonate Rock Region of Guangxi Based on the DGT Technique].

In order to explore the main controlling factors of Cd enrichment in rice under a geological high background in the Guangxi carbonate rock area, this study was based on rice grain-root soil samples from the carbonate rock areas in the southwest and north of Guangxi. Combined with diffusive gradients in thin films technology (DGT), the relationship between soil pH, organic matter, cation exchange capacity (CEC), DGT-Cd, and ω(rice-Cd)-BCF value in rice grains was analyzed and discussed. The main factors were determined by principal component analysis, and a quantitative model was established. The results showed that the average value of ω(soil-Cd) was 0.975 mg·kg-1, and the over-standard rate was 33.33%; the average value of ω(rice-Cd) was 0.020 mg·kg-1, and the average BCF value was 0.038, and the over-standard rate of Cd content in rice grains was 4.2%. The content of Cd in paddy soil was high, but bioavailability was low in the study area. The BCF value of rice grains in the study area was significantly negatively correlated with soil pH and cation exchange capacity at the level of 0.01, positively correlated with DGT-CD at the level of 0.01, and negatively correlated with organic matter at the level of 0.05. The results of principal component analysis showed that the total amount of Cd in the soil, pH, and DGT-Cd were the main factors affecting the accumulation of Cd in rice in the Guangxi carbonate rock area. Taking the total amount of Cd in the soil, pH, and DGT-Cd as variables, the fitting equation of BCF value of rice grains in the Guangxi carbonate rock region was established, and the determination coefficient of the model was 0.717, which could better predict the content of Cd in rice grains in this region.
The Evolution and Diversification of Blockchain Technology

The term around the blockchain technology is publicly confusing at present.At present, all terms related to blockchain technology make the public confused.However, the blockchain technology is the perfect example of using public shared information to enhance data security.It is essentially distributed database of records or public ledger of all transactions and digital footprints that have been executed and shared among all the parties.Each transaction in the public ledger is verified by a majority of the participants in the system, and once entered, the information can never be easily erased.This decentralized the peer-to-peer management system is being used in both financial and non-financial sector.In this article, the author exams the application difficulties of the rapidly shifting technology, as well as to understand, govern and potentially apply this technology into real world businesses' operation, and offer suggestions such as additional regulation layers added by the government to ensure the integrity, as well as for how to appropriately implement blockchain technology throughout the society.The author draws out the conclusion of combining regulators' administration and the self-adjustment of the business industry to efficiently exploit the edges of blockchain technology.