Reviewerly/exHarmony huggingface dataset:
Gender and Information Needs and Seeking Behaviour of Cataloguers in University Libraries in Edo and Delta States

This study investigated Gender and Information Seeking Behaviour of Cataloguers in University Libraries in Edo and Delta States, Nigeria. The study employed a descriptive survey method and questionnaire was the instrument used for data collection. The population of this study consisted of fifty-two (52) cataloguers in all the university libraries in Edo and Delta States. The population of this study is relatively small and as such the entire population was used as the representative sample using purposeful sampling techniques. The researchers visited the sample institutions to administer the questionnaire. It was revealed from the study that male cataloguers often use list of subject headings compared to their female counterparts. Both sexes very often and often use classification schemes respectively in the process of performing their duties. Findings also show that there are more males cataloguers compared to females. Males and females cataloguers very often and often use Anglo-American Cataloguing Rule 2nd edition. The finding indicates that there is more female’s percentage in the use of Anglo-American Cataloguing Rule 2nd edition (AACR11) in the process of performing their task. The study also reveals that authority file is the least tool used by both sexes of cataloguers in the process of performing their duties. From the study, it is obvious that a majority of both male and female cataloguers consult the university’s main library when they are seeking for information. The study recommended that universities’ and libraries’ management should enact policies mandating cataloguers to be attending seminars, conferences and workshops because these are avenue where ideas can be shared on the advancement of their professional duties such as cataloguing and classification.
Uyghur–Kazakh–Kirghiz Text Keyword Extraction Based on Morpheme Segmentation

In this study, based on a morpheme segmentation framework, we researched a text keyword extraction method for Uyghur, Kazakh and Kirghiz languages, which have similar grammatical and lexical structures. In these languages, affixes and a stem are joined together to form a word. A stem is a word particle with a notional meaning, while the affixes perform grammatical functions. Because of these derivative properties, the vocabularies used for these languages are huge. Therefore, pre-processing is a necessary step in NLP tasks for Uyghur, Kazakh and Kirghiz. Morpheme segmentation enabled us to remove the suffixes as the auxiliary unit while retaining the meaningful stem and it reduced the dimension of the feature space present in the keyword extraction task for Uyghur, Kazakh and Kirghiz texts. We transformed the morpheme segmentation task into the problem of labeling the morpheme sequences, and we used the Bi-LSTM network to bidirectionally obtain the position feature information of character sequences. We applied CRF to effectively learn the information of the preceding and following label sequences to build a highly accurate Bi-LSTM_CRF morpheme segmentation model, and we prepared morpheme-based experimental text sets by using this model. Subsequently, we used the stem vectors’ similarity to modify the TextRank algorithm, subsequent to the training of the stem embedding vector using the Doc2vec algorithm, and then we performed a text keyword extraction experiment. In this experiment, the highest F1 scores of 43.8%, 44% and 43.9% were obtained for three datasets. The experimental results show that the morpheme-based approach provides much better results than the word-based approach, which shows the stem vector similarity weighting is an efficient method for the text keyword extraction task, thus proving the efficiency of morpheme sequence for morphologically derivative languages.
Using CNN with Multi-Level Information Fusion for Image Denoising

Deep convolutional neural networks (CNN) with hierarchical architectures have obtained good results for image denoising. However, in some cases where the noise level is unknown and the image background is complex, it is challenging to obtain robust information through CNN. In this paper, we present a multi-level information fusion CNN (MLIFCNN) in image denoising containing a fine information extraction block (FIEB), a multi-level information interaction block (MIIB), a coarse information refinement block (CIRB), and a reconstruction block (RB). In order to adapt to more complex image backgrounds, FIEB uses parallel group convolution to extract wide-channel information. To enhance the robustness of the obtained information, a MIIB uses residual operations to act in two sub-networks for implementing the interaction of wide and deep information to adapt to the distribution of different noise levels. To enhance the stability of the training denoiser, CIRB stacks common and group convolutions to refine the obtained information. Finally, RB uses a residual operation to act in a single convolution in order to obtain the resultant clean image. Experimental results show that our method is better than many other excellent methods, both in terms of quantitative and qualitative aspects.
3D sampling moiré measurement for shape and deformation based on the binocular vision

The sampling moiré method enables digitization and automation of moiré generation and phase analysis by recording single specimen grating image processed by sampling and interpolation approach. With its high efficiency and robustness, it has been extensively utilized for real-time deformation field measurements in multi-scale structures. However, current methods focus primarily on 2D deformation, and the measurement technology of 3D deformation field is still in shortage. This paper introduces a universal 3D sampling moiré method based on binocular vision principles for surface topography and 3D displacement component measurements. The method simplifies two-dimensional phase matching to one-dimensional matching using epipolar rectification, significantly enhancing stereo matching speed. And a fixed-point iterative method addresses large deformations caused by non-uniform grating distribution in 3D measurements. Simulations and experiments are carried out to demonstrate the feasibility and precision of the proposed method. When being compared with the traditional 3D-DIC methods, the 3D sampling moiré method offers comparable accuracy, faster computation, and improved noise resistance. The obtained results verify that the 3D sampling moiré has a good potential for real-time 3D deformation measurement in further application.
A Novel High Computing Power Efficient VLSI Architectures of Three Operand Binary Adders

Directly or indirectly adders are the basic elements in almost all digital circuits, three operand adders are the basic building blocks in LCG (Linear congruential generator) based pseudo-random bit generators. Elementary adders are fast, area and power efficient for small bit sizes. Carry save adder computes the addition in O(n) time complexity, due to its ripple carry stage. Parallel prefix adders such as Han-Carlson compute the addition in O(log(n)) time complexity but at the cost of additional circuitry. Hence new high-speed power-efficient adder architecture is proposed which uses four stages to compute the addition, which consumes less power, and the adder delay decreases to O(n/2). Even though it is not much faster than the High-speed Area efficient VLSI architecture of three operand adders (HSAT3), it computes the addition by utilizing less power. The proposed architecture is implemented using Verilog HDL in Xilinx 14.7 design environment and it is evident that this adder architecture is 2 times faster than the carry save adder and 1, 1.5, 1.75 times faster than the hybrid adder structure for 32, 64, 128 bits respectively. Also, power utilization is 1.95 times lesser than HSAT3, 1.94 times lesser than the Han-Carlson adder, and achieves the lowest PDP than the existing three operand techniques.
Towards the Management of the Location and Use of Medical Equipment with Reduced User Interaction in Health Centers 4.0

Quickly locating equipment in health centers, such as hospitals, may be vital to saving a patient. This research intersects human-computer interaction, software engineering, health centers 4.0, and the Internet of Things to address this problem. We propose to support the management of the location and use of medical equipment within health centers by utilizing context-aware interactive software systems while contributing to reducing the explicit interactions between users and software systems. In this work in progress, we have defined a preliminary version of a conceptual model for characterizing the problem of locating medical equipment (objects) and observing their usage states in health centers by relating users, locations, technologies, and objects to be located while representing the objects’ traits and the Environment’s rules. The first model instance (proof of concept) illustrates the proposed approach. Furthermore, to verify the feasibility of the proposed conceptual model, it will be used to conceive context-aware IoT software systems in hospitals in two countries.
Bootstrapped Evaluation with OctoDollop: A Mobile Application for Evaluating Mobile GUI Aesthetics in Context

There are currently many tools to evaluate the aesthetic quality of a graphical user interface, either by calculating metrics or by performing a machine learning analysis. Both methods typically work as follows: one takes a screenshot and then goes to a separate website or software to evaluate it, which induces a certain break between the context of use in which the screenshot was taken and the context of evaluation. Furthermore, metrics-based methods are often agnostic of the context of use while machine learning methods require a large size dataset to take the context into account. To facilitate the evaluation of a mobile graphical user interface being used in its real context of use, we demonstrate OctoDollop, a mobile application that instantly and seamlessly evaluates a graphical user interface without leaving its context of use and that is based on a limited number of samples by relying on an average bootstrapping. OctoDollop automatically segments the interface into regions, identifies its elements, and computes the overall aesthetic score towards harmony or contrast.
Similarity evaluation of graphic design based on deep visual saliency features

Abstract The creativity of an excellent design work generally comes from the inspiration and innovation of its main visual features. The similarity among primary visual elements stands as a paramount indicator when it comes to identifying plagiarism in design concepts. This factor carries immense importance, especially in safeguarding cultural heritage and upholding copyright protection. This paper aims to develop an efficient similarity evaluation scheme for graphic design. A novel deep visual saliency feature extraction generative adversarial network is proposed to deal with the problem of lack of training examples. It consists of two networks: One predicts a visual saliency feature map from an input image and the other takes the output of the first to distinguish whether a visual saliency feature map is a predicted one or ground truth. Unlike traditional saliency generative adversarial networks, a residual refinement module is connected after the encoding and decoding network. Design importance maps generated by professional designers are used to guide the network training. A saliency-based segmentation method is developed to locate the optimal layout regions and notice insignificant regions. Priorities are then assigned to different visual elements. Experimental results show the proposed model obtains state-of-the-art performance among various similarity measurement methods.
Causal mechanisms of common barriers to national adaptation policy processes and practical solutions in South Korea and the UK

Non-technical Summary As adaptation deficits become increasingly evident and widespread, barriers to adaptation draw more attention as a key reason. However, the current understanding of the barriers is limited, making it challenging to provide practical solutions for real-world adaptation policy processes. This study aims to identify the origins, influences, and relationships of common barriers to national adaptation policy processes, and to analyse their causal mechanisms. The findings present a barrier map that illustrates potential causal mechanisms of common barriers to national adaptation policy processes and, based on it, suggest a systematic approach for practical solutions. Technical Summary Despite progress in national adaptation policies in the last two decades, the adaptation deficit is getting wider and barriers to adaptation are regarded as a key reason for it. However, our understanding of barriers to adaptation does not help improve real adaptation processes. Based on South Korean and UK cases, this study identified 17 common barriers to national adaptation policy processes and placed them in four categories. It also identified the barriers' origins and influences, drew a common barrier map underlying national adaptation policy processes and identified causal mechanisms of the common barriers, which were limitedly addressed in the earlier literature. The results highlight that understanding the causal mechanisms of barriers to national adaptation policy processes is important to devise practical solutions to overcome barriers and improve the effectiveness of real adaptation processes. The findings also offer a practical understanding of common barriers to national adaptation policy, which can help adaptation policy stakeholders and practitioners to diagnose policy problems, analyse what barriers and origins are related to the problems, decide what should be addressed first to solve the problems, and ultimately make efforts to reduce the current adaptation deficit. Social Media Summary New study identifies causal mechanisms of 17 common barriers to national adaptation policy processes &amp; suggests a systematic approach to overcome the barriers.
A note on complex conference matrices

Let Fq be the Galois field of order q=pm, p a prime number and m a positive integer. We prove in this article that for any nontrivial multiplicative character ϰ of Fq⁎ and for any b∈Fq⁎ we have ∑a∈Fq⁎ϰ(a)ϰ(a+b)‾=−1. Whenever q is odd and ϰ is the Legendre symbol this formula reduces to the well-known Jacobsthal's formula. A complex conference matrix is a square matrix of order n with zero diagonal and unimodular complex numbers elsewhere such that C⁎C=(n−1)I. Paley used finite fields with odd orders q=pm, p prime and the real Legendre symbol to construct real symmetric conference matrices of orders q+1 whenever q≡1(mod4) and real skew-symmetric conference matrices of orders q+1 whenever q≡−1(mod4). In this article we extend Paley construction to the complex setting. We extend Jacobsthal's formula to all other nontrivial characters to produce a complex symmetric conference matrix of order q+1 whenever q≥4 is any prime power as well as a complex skew-symmetric conference matrix of order q+1 whenever q is any odd prime power. These matrices were constructed very recently in connection with harmonic Grassmannian codes, by use of finite fields and the character table of their additive characters. We propose here a new proof of their construction by use of the above generalized formula similarly as was done by Paley in the real case. We also classify, up to equivalence, the complex conference matrices constructed with some nontrivial characters. In particular, we prove that the complex conference matrix constructed with any nontrivial multiplicative character ϰ and that one constructed with ϰpk for any integer k=1,...m−1 are permutation equivalent. Moreover, we determine the spectrum of any complex conference matrix obtained from this construction.
Speaker diarization with variants of self-attention and joint speaker embedding extractor

The speaker diarization task pertains to the automated differentiation of speakers within an audio recording, while lacking any prior information regarding the speakers. The introduction of the self-attention mechanism in End-to-End Neural Speaker Diarization (EEND) has elegantly resolved the issue of overlapping speakers. The Transformer model equipped with self-attention mechanism has shown great potential in collecting global information, yielding remarkable outcomes in various tasks. However, the individual speaker characteristics are predominantly reflected in the contextual information, which conventional self-attention would not adequately address. In this study, we propose a hierarchical encoders model to augment the encoders’ acquisition of speaker information in two distinct ways: (1) Constraining the perceptual field of the self-attentive mechanism with left-right windows or Gaussian weights to highlight contextual information; (2) Utilizing a pre-trained time-delay neural network based speaker embedding extractor to alleviate the shortcomings of speaker feature extraction ability. We evaluate the proposed methods on a simulated dataset of two speakers and a real conversation dataset. The model with the most favorable outcomes among the proposed enhancements achieves a diarization error rate of 7.74% on the simulated dataset and 21.92% on MagicData-RAMC after adaptation. These results compellingly demonstrate the efficacy of the proposed methods.
Computer-aided diagnosis using white shark optimizer with attention-based deep learning for breast cancer classification

Breast cancer (BC) is categorized as the most widespread cancer among women throughout the world. The earlier analysis of BC assists to increase the survival rate of the disease. BC diagnosis on histopathology images (HIS) is a tedious process that includes recognizing cancerous regions within the microscopic image of breast tissue. There are various methods to discovering BC on HSI, namely deep learning (DL) based methods, classical image processing techniques, and machine learning (ML) based methods. The major problems in BC diagnosis on HSI are the larger size of images and the high degree of variability in the appearance of tumorous regions. With this motivation, this study develops a computer-aided diagnosis using a white shark optimizer with attention-based deep learning for the breast cancer classification (WSO-ABDLBCC) model. The presented WSO-ABDLBCC technique performs accurate classification the breast cancer using DL techniques. In the WSO-ABDLBCC technique, the Guided filtering (GF) based noise removal is applied to improve the image quality. Next, the Faster SqueezeNet model with WSO-based hyperparameter tuning performs the feature vector generation process. Finally, the classification of histopathological images takes place using attention-based bidirectional long short-term memory (ABiLSTM). A detailed experimental validation of the WSO-ABDLBCC occurs utilizing the benchmark Breakhis database. The proposed model achieved an accuracy of 95.2%. The experimental outcomes portrayed that the WSO-ABDLBCC technique accomplishes improved performance compared to other existing models.
A nonlinear model predictive control based control method to quadrotor landing on moving platform

Abstract To address the problems that the UAV (Unmanned Aerial Vehicle) is vulnerable to distance limitation and environmental interference when tracking and landing on a moving platform autonomously, the accuracy of position estimation relying only on visual odometry in the point‐featureless environment is insufficient, and the traditional linear path planning solvers and controllers cannot meet the fast and safe requirements under the non‐linear strong coupling characteristics of the cooperative landing system, an nonlinear model predictive control (NMPC)‐based multi‐sensor fusion method for autonomous landing of UAVs on motion platforms is proposed. The UAV combines the position information obtained by the RTK‐GPS and the image information obtained by the camera and uses the special identification codes placed in the landing area of the UAV to carry out cooperative planning and navigation while using UKF (Unscented Kalman Filter) to estimate the position of the moving platform and using the interference‐resistant NMPC algorithm to optimise the UAV tracking trajectory based on the precise positioning of the two platforms to achieve the autonomous landing control of the UAV. The simulation and practical experimental results show the feasibility and effectiveness of the proposed algorithm and the autonomous landing control method and provide an effective solution for the autonomous landing of quadrotors on arbitrarily moving platforms.
MODELLING OF DIFFERENT MOTHER WAVELET TRANSFORMS WITH ARTIFICIAL NEURAL NETWORKS FOR ESTIMATION OF SOLAR RADIATION

IIn recent years, the interest in renewable energy sources has increased due to environmental damage and, the increasing costs of fossil fuel resources, whose current reserves have decreased. Solar energy, an environmentally friendly, clean and sustainable energy source, is one of the most important renewable energy sources. The amount of electrical energy produced from solar energy largely depends on the intensity of solar radiation. For this reason, it is essential to know and accurately predict the characteristics of the solar radiation intensity of the relevant region for the healthy sustainability of the existing solar energy systems and the systems planned to be installed. For this purpose, a two-stage forecasting model was developed using the hourly solar radiation intensity of 2014 in a region in Turkey. In the first stage of the study, the second month of each season was selected to investigate the seasonal effects of the region and large, medium, and small-scale events in the study area were examined using discrete wavelet transform. The performances of different mother wavelets in the Artificial Neural Network model with Wavelet Transform (W-ANN) are compared in the second stage. July, the most successful estimation result in seasonal solar radiation intensity was obtained. The most successful RMSE values for January, April, July and October were 65,9471W/m^2, 74,3183 W/m^2, 54,3868 W/m^2, 78,4085 W/m^2 respectively, the coiflet mother wavelet measured it.
Robust semi-supervised classification for imbalanced and incomplete data

Self-training semi-supervised classification has grown in popularity as a research topic. However, when faced with several challenges including outliers, imbalanced class, and incomplete data in reality, the traditional self-training semi-supervised methods might adversely damage the classification accuracy. In this research, we develop a two-step robust semi-supervised self-training classification algorithm that works with imbalanced and incomplete data. The proposed method varies from traditional self-training semi-supervised methods in three major ways: (1) The method in this paper does not necessitate the balance and complete assumption in traditional semi-supervised self-training methods, since it can complete and rebalance the dataset simultaneously. (2) This method is compatible with many classifiers, so it can handle multi-classification and non-linear classification cases. (3) The classifier in this paper is resistant to outliers during semi-supervised classification. Furthermore, several numerical simulations were performed in this research to illustrate the quality of our method to synthesized data, as well as multiple experiments to demonstrate our method superior classification performance on various real datasets.