Reviewerly/exHarmony huggingface dataset:
Discrete Dynamics of Dynamic Neural Fields

Large and small cortexes of the brain are known to contain vast amounts of neurons that interact with one another. They thus form a continuum of active neural networks whose dynamics are yet to be fully understood. One way to model these activities is to use dynamic neural fields which are mathematical models that approximately describe the behavior of these congregations of neurons. These models have been used in neuroinformatics, neuroscience, robotics, and network analysis to understand not only brain functions or brain diseases, but also learning and brain plasticity. In their theoretical forms, they are given as ordinary or partial differential equations with or without diffusion. Many of their mathematical properties are still under-studied. In this paper, we propose to analyze discrete versions dynamic neural fields based on nearly exact discretization schemes techniques. In particular, we will discuss conditions for the stability of nontrivial solutions of these models, based on various types of kernels and corresponding parameters. Monte Carlo simulations are given for illustration.
Using Apriori Algorithm Technique to Analyze Crime Patterns for Kenyan National Crime Data: A County Perspective

The aim of this research is to identify approaches for using Association Rule Mining algorithms to discover rules from the Kenyan crime datasets at a county level. WEKA is the main tool that was used in this research. WEKA is a tool that provides a wide variety of machine learning algorithms that can be used in data mining. In this case the Apriori algorithm was applied on the dataset to discover frequent attribute sets and associations between them. The original Crime dataset was preprocessed to comprise data for 47 counties each having 7 attributes of interest. This was run through WEKA to discover the best Association Rules. These discovered rules could in turn be used to determine the Crime risk factor of a particular county.
The Combination of Computer Multimedia Technology and College English Teaching

Abstract College English is very important for college students, it is an compulsory course for them. If teachers are still based on the traditional teaching methods, and do not effectively apply new technology to promote the innovation and reform of college English teaching, it will lead to low efficiency of college English teaching. What is worse, it will affect the quality of English teaching. In this case, college English teachers need to master the main application characteristics of multimedia technology and the effective method to combine college English and computer multimedia technology. Our teachers should try our best to give full play to the advantages of computer multimedia technology during college English teaching. In this process, it is necessary to study and analyze the existing problems and deficiencies of college English teaching, and understand the specific development of computer multimedia technology. Only in this way can we take effective measures to promote the combination of computer multimedia technology and college English teaching, and improve the level of college English teaching.
Frequency Disentanglement Distillation Image Deblurring Network

Due to the blur information and content information entanglement in the blind deblurring task, it is very challenging to directly recover the sharp latent image from the blurred image. Considering that in the high-dimensional feature map, blur information mainly exists in the low-frequency region, and content information exists in the high-frequency region. In this paper, we propose a encoder–decoder model to realize disentanglement from the perspective of frequency, and we named it as frequency disentanglement distillation image deblurring network (FDDN). First, we modified the traditional distillation block by embedding the frequency split block (FSB) in the distillation block to separate the low-frequency and high-frequency region. Second, the modified distillation block, we named frequency distillation block (FDB), can recursively distill the low-frequency feature to disentangle the blurry information from the content information, so as to improve the restored image quality. Furthermore, to reduce the complexity of the network and ensure the high-dimension of the feature map, the frequency distillation block (FDB) is placed on the end of encoder to edit the feature map on the latent space. Quantitative and qualitative experimental evaluations indicate that the FDDN can remove the blur effect and improve the image quality of actual and simulated images.
Diagnostic Data Integration Using Deep Neural Networks for Real-Time Plasma Analysis

Recent advances in acquisition equipment is providing experiments with growing amounts of precise yet affordable sensors. At the same time an improved computational power, coming from new hardware resources (GPU, FPGA, ACAP), has been made available at relatively low costs. This led us to explore the possibility of completely renewing the chain of acquisition for a fusion experiment, where many high-rate sources of data, coming from different diagnostics, can be combined in a wide framework of algorithms. If on one hand adding new data sources with different diagnostics enriches our knowledge about physical aspects, on the other hand the dimensions of the overall model grow, making relations among variables more and more opaque. A new approach for the integration of such heterogeneous diagnostics, based on composition of deep variational autoencoders, could ease this problem, acting as a structural sparse regularizer. This has been applied to RFX-mod experiment data, integrating the soft X-ray linear images of plasma temperature with the magnetic state. However to ensure a real-time signal analysis, those algorithmic techniques must be adapted to run in well suited hardware. In particular it is shown that, attempting a quantization of neurons transfer functions, such models can be modified to create an embedded firmware. This firmware, approximating the deep inference model to a set of simple operations, fits well with the simple logic units that are largely abundant in FPGAs. This is the key factor that permits the use of affordable hardware with complex deep neural topology and operates them in real-time.
Application of Big Data Technology in the Cultivation Mode of Compound Talents in Civil Engineering

Today is the era of big data. While subverting people’s activities, big data technology also promotes the transformation of thinking mode, carrier form, and practice mode in the field of civil engineering. It is an innovative creation of the training model for the application of compound talents in civil engineering. The talent training model is a work that all fields attach great importance to. Only by cultivating professional talents in the field can the development of the field be promoted and the involution of the field can be reduced. It is only in different countries and historical periods that the goal of talent training is, the levels and specifications are different. Big data technology has strong data perception and processing capabilities, which can contribute to the network education work of colleges and universities. But at the same time, the emergence of new things will inevitably bring new thinking. Therefore, it is of great significance to study the application of big data technology to the training mode of compound talents in civil engineering. To this end, this article puts the field of civil engineering under the background of big data, and explores innovative talent training models in the field of civil engineering in the era of big data through research and analysis of its basic connotations and changes in the background of big data. Based on the analysis of the research data, it is concluded that big data technology can bring innovation and transformation to the talent training model of civil engineering, and it can also cultivate high-quality talents in the field of civil engineering.
Three-Mode Fuzzy Co-Clustering Based on Probabilistic Concept and Comparison with FCM-Type Algorithms

Three-mode fuzzy co-clustering is a promising technique for analyzing relational co-occurrence information among three mode elements. The conventional FCM-type algorithms achieved simultaneous fuzzy partition of three mode elements based on the fuzzy c -means (FCM) concept, and then, they often suffer from careful tuning of three independent fuzzification parameters. In this paper, a novel three-mode fuzzy co-clustering algorithm is proposed by modifying the conventional aggregation criterion of three elements based on a probabilistic concept. The fuzziness degree of three-mode partition can be easily tuned only with a single parameter under the guideline of the probabilistic standard. The characteristic features of the proposed method are compared with the conventional algorithms through numerical experiments using an artificial dataset and are demonstrated in application to a real world dataset of MovieLens movie evaluation data.
Crowd Counting with Distribution Matching and Dilated Networks

Crowd counting is an important task due to its various application areas. In this study, we used deep convolutional neural networks to estimate the crowd count. By combining a suitable loss function and distribution matching, we propose an original architecture which increases not only counting accuracy, especially for congested scenes, but robustness across datasets as well. These improvements have been shown on Shanghaitech and UCF datasets in terms of mean absolute error and root mean square error measures. Based on the same measures, our model also exhibits superiority against many recent studies especially on the UCF dataset, which consists of highly congested scenes.
A Transparent Immutable and More Secured Voting System Using Blockchain

Abstract Blockchain is a cryptocurrency system with a transaction record of several computers in a peer-to-peer network. Blockchain In this paper, we propose a transparent, immutable, and more secured voting system using blockchain that collectively approaches security and cost issues. In Ballot voting, there is no security, immutability between the voter and the system. The proposed system uses blockchain technology to increase the efficiency of the voting system. Here the algorithm used is blockchain which includes a private key and public key encryption, SHA-256, Merkle hashing tree, and for security, one-time password (OTP) authentication can be done. It permits the voter to vote from any place, which increases the overall attendance percentage. The proposed system gives improved security and manages several data in the blockchain. In this system, we proposed security voting by using blockchain technology. In the existing system easy to vote for the ones who have not voted? I, vote fraudulent is happening on every century, some securities have been implemented but in parallel fraudulent is also increasing.
ZJUKLAB at SemEval-2021 Task 4: Negative Augmentation with Language Model for Reading Comprehension of Abstract Meaning

This paper presents our systems for the three Subtasks of SemEval Task4: Reading Comprehension of Abstract Meaning (ReCAM).We explain the algorithms used to learn our models and the process of tuning the algorithms and selecting the best model.Inspired by the similarity of the ReCAM task and the language pre-training, we propose a simple yet effective technology, namely, negative augmentation with language model.Evaluation results demonstrate the effectiveness of our proposed approach.Our models achieve the 4th rank on both official test sets of Subtask 1 and Subtask 2 with an accuracy of 87.9% and an accuracy of 92.8%, respectively 1 .We further conduct comprehensive model analysis and observe interesting error cases, which may promote future researches.
REFLECTIONS REGARDING ANATOMY TEACHING WITH THE USE OF DIGITAL TOOLS

On March 11, 2020, the World Health Organization - WHO declared COVID-19 as a pandemic. To maintain interaction and ensure that students access quality content, teachers have started to use digital platforms. The purpose of this work is to report on the experience provided by the III Workshop of Maranhense Anatomical Techniques. The Center for Advanced Morphophysiological Studies - NEMO of the State University of the Tocantina Region of Maranhão - UEMASUL and State University of Maranhão - UEMA, held the workshop associated with the discipline of Anatomical Techniques of the Postgraduate Course in Animal Science, being one international online event through the YouTube platform. The way of teaching animal and human anatomy has evolved towards active methodologies, to accompany the didactic-pedagogical evolution with the advances in digital technology. Anatomy teachers feel the need to redefine the objectives of this discipline. The professors showed mastery over the lectures and the use of the platform used to broadcast the event, managing to talk about Animal and Human Anatomy in a completely different way, combining theory with practice. Under these circumstances, an innovative teaching-learning method, supported by the internet, has been serving an unlimited number of students interested in short courses. Keywords: Anatomical techniques. Continuing Education. Web conference.
Ecological Risk Assessment of Heavy Metals in Soil of Lagos State University, Epe, Lagos State, Nigeria

Soil contaminated with heavy metals poses an ecological risk especially to public health. The aim of this work is to assess the ecological risk of heavy metals in soil of Lagos State University (LASU), Epe, Lagos State, Nigeria. Soil samples were collected from 8 different points in LASU which were stored in various polythene bags labeled SS1 – SS8. A control sample was taken 1 km away from LASU and was labeled SC9. All the sampling locations were identified using handheld Global Positioning System (GPS) for the purpose of universal identification. The soil samples were analysed for nickel (Ni), lead (Pb), copper (Cu), Zinc (Zn), chromium (Cr), cadmium (Cd) and Iron (Fe) using Atomic Absorption Spectrophotometer. Enrichment factor (EF), potential ecological risk (Ei) and ecological risk index (RI) were carried out using the data from the analysis. The results revealed that all the EF values were less than 2 except that of lead in SS1. The Ei values were less than 40 except that of lead in SS1. Moreover RI values of all the soil samples investigated were less than 150. It was obvious from this work that the soil of LASU, Epe, Lagos State, were not enriched with heavy metals and have a low potential ecological risk.
COREFERENCE-AWARE DIALOGUE SUMMARIZATION

Summarizing conversations via neural approaches has been gaining research traction lately, yet it is still challenging to obtain practical solutions. Examples of such challenges include unstructured information exchange in dialogues, informal interactions between speakers, and dynamic role changes of speakers as the dialogue evolves. Many of such challenges result in complex coreference links. Therefore, in this work, we investigate different approaches to explicitly incorporate coreference information in neural abstractive dialogue summarization models to tackle the aforementioned challenges. Experimental results show that the proposed approaches achieve state-of-the-art performance, implying it is useful to utilize coreference information in dialogue summarization. Evaluation results on factual correctness suggest such coreference-aware models are better at tracing the information flow among interlocutors and associating accurate status/actions with the corresponding interlocutors and person mentions.
Effectiveness of VR immersive applications for public speaking enhancement

Augmented and Virtual Reality (VR) technology has recently proved to be useful in many learning and training scenarios. VR applications designed to practice communication skills (also known as Public Speaking Training or PST) are currently among the newest and still-unexplored solutions whose effectiveness is still to be tested. The current paper evaluates the Quality of Experience for a public speaking VR system where speakers can experience a talk session in front of a wide audience that actively reacts to their statements. A double stimulus experiment (with a real and virtual audience) was carried out in order to measure the visual quality, the immersiveness and the effectiveness of the approach. Objective evaluations, users’ feedback and public speaking metrics showed that the VR set-up enhanced speakers’ gesture and speech control when compared to performing in front of a real audience.
PERBANDINGAN ALGORITMA K-NEAREST NEIGHBOR, DECISION TREE, DAN NAIVE BAYES UNTUK MENENTUKAN KELAYAKAN PEMBERIAN KREDIT

The banking world in terms of providing credit to customers is a regular activity that has a large effect. In its application, non-performing loans or bad loans are often created due to poor credit analysis in the credit granting process, or from bad customers. The purpose of this study is to compare the results of algorithm accuracy between K-Nearest Neighbor (K-NN), Decision Tree, and Naive Bayes which results in the best accuracy will be implemented to determine creditworthiness. The attributes used in this study consisted of 11 attributes, namely marital status, number of dependents, age, last education, occupation, monthly income, home ownership, collateral, loan amount, length of loan and information as result attributes. The methods used in this research are K-Nearest Neighbor, Decision Tree, and Naive Bayes. From the results of evaluation and validation using k-5 fold that has been carried out using RapidMiner tools, the highest accuracy results from a comparison of 3 algorithms is using a decision tree (C4.5) of 98% in the 3rd test.
An Innovative Crack Detection Algorithm to Support Automated Inspection of Nuclear Reactor Cores

Throughout the nuclear sector, Condition Monitoring and Inspection (CM&I) is used to evaluate, assess and quantify the health and condition of key components and critical infrastructure. Of particular interest is the inspection of nuclear reactors which, depending on reactor design, can be achieved via Remote Visual Inspections (RVI) whereby a camera is deployed within a nuclear reactor to gather images and videos for visualization and analysis. The current manual analysis process is labour-intensive and extremely time-consuming. It is also subjective, prone to human error and difficult to repeat. More recent practices on the detection and analysis of such defects, focus on automating the process, but assign a label on the whole image or a patch of it (image classification). In this paper, we establish a framework for automatically detecting defects from inspection footage to a pixel level, by implementing and modifying a well-known deep neural network. After creating a dataset of images acquired from software that processes and stitches the raw video footage, we train and asses our model. We evaluate the trained model and report the testing accuracy on unseen data. The results are promising and represent an attempt for an automatic pixel-level crack and defect detection system to a field where this family of methods has scarcely been explored: nuclear inspection. This approach provides a low-level localization of cracks, removes bias that is often present with a classification or subjectivity associated with manual approaches and allows repeatability of results.
Zero-Shot Turkish Text Classification

The method frequently used for text classification is supervised modeling with a large training set with labels. In some cases, we may not have labeled data. Modeling in the absence of labeled data for target classes is called zero-shot modeling. For zero-shot text classification natural language inference is utilized which is another branch of natural language processing. In order to measure the effectiveness of zero-shot classification, a reference is generated with BERT method which contrarily uses labeled data. Two Turkish data sets are employed. TTC-3600 dataset is composed of 6 news categories. In this study we have generated a new Twitter sentiment dataset TTD, which is composed of weakly labeled Twitter messages in 2 classes as positive and negative depending on contained emoticons. The results show that without using any labeled data we can obtain classification accuracies of %65.5 and %91 for TTC-3600 and TTD respectively.
Research Cloud Bazaar: A software defined cloud workflow cost management tool

Research workflows will benefit from a hybrid computing environment that offers seamless integration between on-campus and off-campus cloud resources. Commercial and Federal and commercial clouds provide researchers a rich set of computing platforms that allow opportunities to improve workflows and reduce the time to research. The large number of cloud offerings, however, makes cost management, and workflow transitions to appropriate platforms challenging. Successfully mapping workflows from on-campus resources to the cloud and leveraging the available cost structures to find economical cost models are critical steps to enabling researcher access to this vast resource. To address these concerns, here we introduce the Research Computing Bazaar (RCB) software application for resource mapping and cost estimation. RCB is a software-as-a-service platform that is an elastic, scalable, and fault tolerant system. It is developed using actual data from research computing workloads and can be easily configured to be used by users or system administrators in computing environments that use Slurm. In this pilot, we inform researchers about opportunities offered by RCB to leverage flexible workload orchestration in managing cloud costs on a major cloud service provider. An extension into predictive capacities with machine learning mechanisms is being developed.
Findings of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering

We describe the second SIGMORPHON shared task on unsupervised morphology: the goal of the SIGMORPHON 2021 Shared Task on Unsupervised Morphological Paradigm Clustering is to cluster word types from a raw text corpus into paradigms.To this end, we release corpora for 5 development and 9 test languages, as well as gold partial paradigms for evaluation.We receive 14 submissions from 4 teams that follow different strategies, and the best performing system is based on adaptor grammars.Results vary significantly across languages.However, all systems are outperformed by a supervised lemmatizer, implying that there is still room for improvement.
Android based Digital Steganography Application using LSB and PSNR Algorithm in Mobile Environment

Data security plays an important and integral part in communication technology.The demand for data security remains as high as ever as technologies evolve, especially in this era of rapid changing technology.A method of securing data can be seen steganography, which is the art of hiding data.This paper proposes for two algorithms, which are Least Significant Bit (LSB) and Peak Signal to Noise Ratio (PSNR), to be combined and utilized together to create an Android based Digital Steganography Application.As such, the contributions in this paper will benefit the field of data security particularly in the mobile environment as to the implementation of the two algorithms.