Reviewerly/exHarmony huggingface dataset:
A liver tumor segmentation method based on cascaded fully convolutional neural networks and 3D conditional random fields

Aiming at the following problems encountered by Multiple Supervised Fully Convolutional Networks (MSFCN ) in liver tumor segmentation: rich blood vessels around the liver, complex organs, and inconspicuous boundaries, which have a great impact on accurate tumor segmentation, plus The tumor boundary is not clear and the segmentation is difficult. In this paper, we propose a method to automatically segment liver and tumor in CT abdominal images using Cascaded Fully Convolutional Networks (CFCN) and 3D Conditional Random Fields (CRF). We train and cascade two fully convolutional neural networks for combined segmentation of the liver and its tumors. In the first step, our trained FCN segmented the liver from abdominal CT images as the input of the second FCN, which removed the influence of the environment around the liver. The second FCN segmented the tumor region only based on the liver obtained in the first step. Finally, 3D conditional random field is used to optimize the segmented image obtained by cascaded FCN, and the edge information of liver tumor image is extracted to solve the problem of unclear boundary of liver tumor and complete the segmentation.
FEATURES OF SELF-ATTITUDE OF GIRLS

The article deals with the study in specifics of the girls self-attitude. It gives data on their relationship with significance, realization, conflict of significance and realization of various values, life satisfaction. It is indicated that with increasing age, girls achieve greater inner harmony. An important role in shaping the self-attitude of girls can be played by the value sphere of the individual, in particular, the practical implementation of values. Of particular importance are the values of physical attractiveness and material well-being. An excessive increase in the conflict of significance and the implementation of values can negatively affect the self-attitude of girls. Such areas of life as education and health may be underestimated by young girls, and therefore do not have a significant impact on their self-attitude, which is also characteristic of the creative work value, which girls also do not correlate much with their own personality.
Big Data Analysis Technology for Artificial Intelligence Decision-Making Platform Construction and Application

With the development of the information age, it is the opportunity and challenge for enterprises to apply big data analysis technology to make decisions and better solve the major problems of global and sustainable development. Decision-making is crucial for enterprises, and a correct decision can improve the development potential and competitiveness of enterprises. However, traditional decision models have certain limitations, and it is difficult to handle the massive, polymorphic, and changing decision data. To address these problems, we propose a combination of temporal data anomaly detection and width learning big data analysis technology for building an intelligent decision platform to assist enterprises to better solve major decision problems. First, the goal of time series data anomaly detection is to correctly determine whether the data points in each moment of the time series are abnormal. The variation of time series data is affected by various factors, and the fluctuation of data caused by some nonanomalous factors can increase the difficulty of anomaly detection. To address the above problems, we propose an anomaly detection algorithm for time series data based on time series decomposition method. In this algorithm, the time series are decomposed by STL method and HP filtering method according to whether the time series is periodic or not, and then the components of the time series that are relevant to anomaly detection are retained and anomaly detection is performed on the processed time series using a cyclic model. Then, based on the call text and signaling data in enterprise decision-making, an improved width learning model called coding width learning is proposed. The coding width learning model is used to identify decision problems and make comprehensive decisions to improve the model training time and the accuracy of identification. At the same time, an integrated learning method with parallelized training is proposed for width learning in order to further improve the efficiency of coding width learning and prevent the potential memory explosion problem. Finally, the experimental results show that the proposed anomaly detection method effectively improves the anomaly detection performance of the model, and its performance is better than the existing time series anomaly detection algorithm based on variation self-encoder; combined with the improved width learning model, it can make fast decisions and analysis without using scenarios and targets, and help and guide the work of related personnel.
Measuring Vowel Harmony within Hungarian, the Indus Valley Script Language, Spanish and Turkish Using ERGM

Front-back vowel harmony is an important characteristic of many languages. Testing whether an untranslated script has vowel harmony may aid its decipherment. This paper tests vowel harmony for three different modern languages (Hungarian, Spanish and Turkish) as well as the extinct underlying language of the undeciphered Indus Valley script. We also introduce a novel vowel harmony index based on the Exponential Random Graph Model for graphs. To achieve this, we first select words from each of the modern languages (Hungarian, Turkish, and Spanish) from their Swadesh list. Then we divide each word into syllables, isolating the vowels. We then analyze the three modern languages using Exponential Random Graph Model methods. The results indicate that this procedure and the vowel harmony index are feasible to define the degree of vowel harmony in a language. The procedure is then extended to the undeciphered Indus Valley Script. Our results indicate that the underlying language of the Indus Valley Script also had vowel harmony. We found that on average the odds of the IVS having vowel harmony were 6.61 times higher than would be found in a random graph.
COMPARATIVE ANALYSIS OF ML CLASSIFIERS FOR RECOGNITION OF HANDWRITTEN DEVANAGARI SCRIPT

Devanagari has been widely adopted across India and Nepal to write Sanskrit , Marathi , Hindi , Hindi dialects , Konkani , boro and various Nepalese languages.Millions of people all in India use this script for writing documents in Hindi and Marathi.Many of the Indian mythology is written in this script.Therefore, it has received a tons of popularity over the decades.In this paper, we have taken a dataset of handwritten Devanagari characters which consist of 61200 handwritten photographs of the characters.Along with the dataset we have also proposed the deep learning algorithms for recognition of those characters named as "multi-layer perceptron classifier", "Random Forest classifier" and " K-Nearest Neighbor classifier ".The sole purpose of this paper is to compare all the three classifiers on the basis of their accuracy for recognition of the images and to come up with the best one.
Chinese-Uyghur Bilingual Lexicon Induction Based on Morpheme Sequence and Weak Supervision

The bilingual dictionary is a vital data resource for machine translation and cross-language information retrieval research. Uyghur language has rich derivative forms, in which words are formed by a stem connecting with several suffixes, thus a large number of new words can be generated. This will increase the repetition rate of intentional features in the text and affect the efficiency of bilingual dictionary extraction. Aiming at the poor alignment of Chinese-Uyghur cross-language word embeddings due to significant morphological differences, this paper proposes a multilingual morphological analyzer based on morpheme sequence combined with neural network cross-language word embedding vector mapping and used for Chinese-Uyghur bilingual dictionary extraction task. A robust morpheme segmentation and stemming of bilingual text data are used to obtain excellent and meaningful word semantic features. Using a small number of Chinese-Uyghur parallel seed dictionaries as weakly supervised signals, respectively, map multilingual word or morpheme vectors to a unified vector space. And by associative alignment and locally scaling two bi-lingual retrievals through nearest-neighbor retrieval and cross-domain similarity, bilingual dictionaries are automatically extracted. Experimental results show that the morpheme sequence-based method for the Chinese-Uyghur dictionary induction task has significantly improved the accuracy of dictionary alignment compared to the word-based model. The manner in this paper can efficiently improve the accuracy of bilingual word alignment and is effective for morphologically derivative languages.
Speech Language Identification Using CNN-BiLSTM with Attention Mechanism

Aiming at the problems of low feature extraction ability and low recognition rate in language identification task, this paper proposes CNN-BiLSTM language identification method based on attention mechanism from the two aspects of feature and model. In the feature extraction stage, we compare the recognition effects of different speech features such as MFCC, LPCC and spectrogram, Experiments show that the recognition effect of spectrogram features is better in the actual environment. This method extracts the spectrogram of speech data, uses convolution network to obtain the visual features of spectrogram, and introduces attention mechanism, which can pay attention to the language related information in speech fragments and ignore non language information. This method is tested on two cross-channel data sets (AP19-olr) and (AP20-olr) of the Oriental Language Recognition competition. Compared with the language identification methods such as the baseline system x-vector, the equal error rate (EER%) on the two data sets is reduced by 3.38% and 4.66%, The results clearly show that the CNN-BiLSTM language identification method based on attention mechanism effectively improves the feature extraction ability in language identification task, and improves the performance of language identification in practical application environment.
Aspect Term Extraction Based on BiLSTM-CRF Model

Aspect term extraction is an important subtask in aspect sentiment analysis, and it is a necessary step to complete other subtasks. Existing studies focus on complex and changeable models and only use single dataset for training, which is not conducive to the research and has no good to the application of aspect term extraction task. Therefore, this paper seeks for a simple and effective model to complete the task. We transformed the aspect term extraction task into sequence tagging task, and applied the BiLSTM-CRF model to extract the aspect terms. Experiment results and case studies showed that the F1 score of proposed model on the laptop dataset is 80.13, which is 6.35 higher than the best baseline model. On the restaurant dataset, the F1 score reached 85.2, 1.19 higher than the best baseline model. It proved that the BiLSTM-CRF has better performance than the baseline, and had greater advantages on multiple aspect words recognition. In addition, we applied BiLSTM-CRF model to our practical task, and constructed an aspect-level Yelp dataset in a semi-supervised method. The parameter setting of the method was discussed.
Feasibility of Measuring Shot Group Using LoRa Technology and YOLO V5

Shooting is a common activity all over the world for both military and recreational purposes. Shooting performance can be measured from the size of the shot group (grouping). Shooters have been calculating the size of the group by measuring the distance between bullet impacts using their hands. This paper aims to create a reasonable automated shot grouping size measuring module that is available from several kilometers away. It includes an IoT(Internet of Things) system and a mobile application that users can access. LoRa technology is adopted for covering long distances, and YOLO V5 is implemented to detect bullet impacts. Mathematical methods for calculating accurate distance and engineering techniques to fill the needs are described with experiments on various parameters and conditions. The proposed module showed that indoor tests measured the shot group with a mean accuracy of 91.8%. For future work, outdoor tests, which were affected by environmental control variables, are expected to give better accuracy.
Deep Learning-Based Image Extraction

The development of the web and advancements in computation and multimedia technologies have led to an increase in the variety of photo databases and the collection of hundreds of images that include medical images, e-libraries, and art galleries. The necessary images from that kind of big collection may demand a lengthy period to retrieve using traditional image extraction techniques like Textual Based Images Retrieval. It is essential to develop an effective image extraction procedure that can handle such vast amounts of data at once. The main objective is to develop a trustworthy tool that effectively generates, uses, and responds to data. It employs a strategy for creating an effective picture retrieval application that enables individuals to ask questions about the software and extract it from a huge database.
Three-Dimensional Path Planning for AUVs Based on Standard Particle Swarm Optimization Algorithm

This paper proposes an improved standard particle swarm optimization 2011 for autonomous underwater vehicles (AUVs). A mutation operator with a threshold is introduced to solve the problem of particles falling into the local extreme, and a nonlinear adaptive parameter strategy is introduced to accelerate the convergence speed. The proposed algorithm considers “path length”, “path safety”, “path smoothness” and “physical constraints” synthetically. For the specific navigation environment of AUVs, the path planning simulation is conducted based on MATLAB/Simulink, and the navigation guidance and control closed-loop simulation system is established. Simulation results show the effectiveness of the proposed algorithm.
Model Agnostic Local Explanations of Reject

The application of machine learning based decision making systems in safety critical areas requires reliable high certainty predictions.Reject options are a common way of ensuring a sufficiently high certainty of predictions.While being able to reject uncertain samples is important, it is also of importance to be able to explain why a particular sample was rejected.However, explaining reject options is still an open problem.We propose a model-agnostic method for locally explaining reject options by means of interpretable models and counterfactual explanations.
Analyzing and Visualizing Uncertain Knowledge: The Use of TEI Annotations in the PROVIDEDH Open Science Platform

The underlying uncertainty in digital humanities research data affects decision-making and persists during a project's lifecycle. This uncertainty is inevitable since most empirical claims cannot be assessed against an absolute truth (Drucker 2011; Binder et al. 2014). This situation has been previously recognized together with the need to report the degrees of uncertainty that accompany such claims (Blau 2011). Although TEI makes it possible to annotate text with notions of certainty or precision, examples of actual projects taking advantage of this are scarce. There are many possible explanations for uncertainty's lack of visibility in computationally supported humanities research; among them, the need for tools specifically designed to address the goal of defining and managing uncertainty stands out. Thus, efforts to provide technical support for humanities research should focus on managing and making uncertainty more transparent, rather than removing it. Another challenge is the fact that there is no agreement on a generic taxonomy for the different types of uncertainty that researchers may face. Various researchers across disciplines, working on varying projects and data sets, can use different categories to classify the uncertainties present in a particular case. In this paper, we introduce a collaborative platform for collective annotation of TEI data sets. We briefly present the flexible taxonomy of uncertainty used in the platform and describe two data sets used for its testing. Then we describe use cases of annotations available on the platform, and how they translate into TEI annotations. Creating and interpreting annotations with and without uncertainty should now be easier, especially for researchers who do not know TEI markup.
Deep Convolutional Neural Networks with Sequentially Semiseparable Weight Matrices

Modern Convolutional Neural Networks (CNNs) comprise millions of parameters.Therefore, the use of these networks requires high computing and memory resources.We propose to reduce these resource requirements by using structured matrices.For that, we replace weight matrices of the fully connected classifier part of several pre-trained CNNs by Sequentially Semiseparable (SSS) Matrices.By that, the number of parameters in these layers can be reduced drastically, as well as the number of operations required for evaluating the layer.We show that the combination of approximating the original weight matrices with SSS matrices followed by gradient-descent based training leads to the best prediction results (compared to just approximating or training from scratch).
The “veronica bucket” and the inventive step requirement under the patent law of Ghana

The Veronica bucket, a hand washing mechanism consisting of a bucket with a tap fixed at the bottom, mounted at hand height, with a bowl at the bottom to collect wastewater has played a pivotal role in the fight against the Covid-19 pandemic through the promotion of good hand washing habits. The popularity of the Veronica bucket led its Ghanaian inventor in an interview on a radio station in Accra, Ghana in March 2021 to express her regrets for failing to obtain a patent for her product. Her statement, which dominated the headlines of major social media platforms and generated public discussion demonstrates the misconception about the grant of a patent. This paper adopts the doctrinal legal research methodology in an attempt to analyse the requirements for the grant of a patent under Ghanaian law, particularly the inventive step requirement to ascertain whether the Veronica bucket is indeed patentable under Ghanaian law. The paper argues, after a careful analysis of the patentability requirements under Ghanaian law that, even though the Veronica bucket is industrially applicable, it would have failed to meet the inventive step requirement to warrant the grant of a patent. The paper further argues that even if a search was to be conducted to reveal that the invention underpinning the Veronica bucket was new at the material time, such an invention would instead entitle its inventor to a Utility Model Certificate under Ghanaian law, which only requires the product concerned to be new and industrially applicable.&#x0D;
Noisy Low-Illumination Image Enhancement Based on Parallel Duffing Oscillator and IMOGOA

In complex environment, the captured images face several kinds of problems, including low illumination and intensive noise, which deteriorates image quality and has a great impact on the follow-up work. In this work, inspired by stochastic resonance theory, we design a model that considers the spatial characteristics of image and noise reduction and enhancement are simultaneously realized. The 8-neighborhood pixel extraction method and the Duffing oscillator model are used to parallel process the image, and then the image details are restored by homomorphic filter. In order to optimize the parameters of parallel Duffing oscillator model and homomorphic filter adaptively, multiobjective grasshopper optimization algorithm is introduced into the method. Sobol sequence and differential mutation operators are used to improve the optimization algorithm, and the fitness function is constructed by using peak signal-to-noise ratio and standard deviation. To verify the effectiveness of the proposed method, low-illumination image data with Gaussian noise is used for subjective and objective evaluation. The experimental results show that the proposed algorithm gives prominence to useful information, which has smaller color distortion and better visual quality.
University Archives Autonomous Management Control System under the Internet of Things and Deep Learning Professional Certification

The current work aims to meet the needs of the development of archives work in colleges and universities and the modernization of management to realize the standards and standardization of all aspects of archives business construction in colleges and universities, so as to improve the political and professional quality of archives cadres. First, the radio frequency identification (RFID) technology based on the Internet of things (IoT) digitizes the university archive labels. Meanwhile, the filing cabinet's intelligent security system preserves confidential files. Second, the convolutional neural network (CNN) algorithm under deep learning is introduced and college profile information is identified. Finally, the concept of professional certification is used to clarify the purpose of the university archives automation management system. Different activation functions are used to analyze the recognition accuracy loss and recognition accuracy of university archives. The identification error of You Only Look Once (YOLO) of the ReLU-convolutional neural network (R-CNN) of college archives is analyzed. The results show that the selection of rectified linear units (ReLU) activation function for CNN can effectively reduce the loss of identification accuracy of college archives and can improve the accuracy of identification of college archives. The algorithm based on the ReLU activation function has a smaller recognition error accuracy in college archives than that of the YOLO algorithm. The recognition error of the YOLO algorithm is slightly higher than that of the R-CNN. The font recognition error of archival information based on the R-CNN is relatively large. However, the conclusion is reasonable due to the recognition difficulties of handwritten archival fonts. The file positioning recognition error rate is 19.00%, the file printing font recognition error rate is 4.75%, and the image recognition error rate is 1.90%. These results have a certain reference value for the process of identifying information in the automatic management of university archives by CNN under different activation functions.
Determining digital literacy among teacher from gender perspective through the Rasch model

Advances in technology and the implementation of online learning require all parties in educational institutions, especially teachers, to be able to use technology. Digital literacy skills are needed to support the learning process. The purpose of this study was to measure the digital literacy skills of teachers through the Rasch model from a gender perspective during the coronavirus disease 2019 (COVID-19) pandemic at Madrasah Aliyah Negeri Palembang, Indonesia. A quantitative research method using Rasch analysis was applied. The results showed that the ability of teachers in digital literacy can be categorized as very high (three people), high (28 people), moderate (63 people), and low (two people) and showed that there are no question items that only favor one of the genders. The results showed that the male teachers in this study had a lower level of digital literacy skills than the female teachers.