Reviewerly/exHarmony huggingface dataset:
Ballot character recognition based on image processing

Abstract According to the relevant provisions of the "Election Law of the People's Republic of China", the engineering software required for the implementation of large-scale conference elections can set voting regulations for "differential elections", "equal elections", and "alternative elections". This paper proposes an automatic ballot recognition system based on image processing, which is suitable for all levels of people’s congresses, party and government meetings or other meetings. It has complete functions and stable recognition. It can automatically count and summarize the voting results and display the voting results on the big screen. Show on the display system. Firstly, the paper ballot is scanned by a scanner to obtain the image of the ballot. Secondly, the system will preprocess the image of the ballot and segment the image into regions.Thirdly, according to the handwritten character recognition algorithm based on image processing and Shen filter proposed in this paper, the handwritten characters "√" and "×" in the subregion are recognized.The final experimental results show that the recognition accuracy of the system is as high as 99.82%, which has an absolute advantage over the traditional manual voting, and can meet the performance requirements of high efficiency, accuracy, objectivity and security of large-scale conference election.
A Modified Decision Tree and its Application to Assess Variable Importance

This paper presents an approach to further improve the data reduction abilities of the traditional C4.5 algorithm by integrating the information gain ratio and forward stepwise regression algorithms. Motivated by the fact that the traditional C4.5 algorithm utilizes a full set of antecedent attributes without taking into consideration irrelevant attributes which is a precursor to spurious predictive model estimates. This study aims to overcome this drawback by developing and evaluating the performance of an importance-based attribute selection algorithm called the C4.5-Forward Stepwise (C4.5-FS) for improving the data reduction abilities of the traditional C4.5 classifiers. Five datasets with dimensionality ranging from 6 to 10,000 attributes were employed to evaluate the model performance. The goodness of fit for the modified and traditional C4.5 classifier was done using k-fold cross-validation based on a confusion matrix. Experimental results revealed that the C4.5-FS algorithm trained on fewer antecedent attributes improved the data reduction capabilities of the traditional C4.5 algorithm trained on a full set of antecedent attributes by achieving higher accuracy.
The Efforts for Engineer Ethics Education at the Atomic Energy Society of Japan

This paper introduces the recent topics of the Atomic Energy Society of Japan Ethics Committee. In addition, referring to the topics, the abilities and sensibilities that engineers should have in the future will be described.
Self-Attentive Recommendation for Multi-Source Review Package

With the diversified sources satisfying users' needs, many online service platforms collect information from multiple sources in order to provide a set of useful information to the users. However, existing recommendation systems are mostly designed for single-source data, and thus fail to recommend multi-source review packages since the interplay between the reviews of different sources is not properly modeled. In fact, modeling the interplay between different sources is challenging because 1) two reviews may conflict with each other, 2) different users have different preferences on review sources, and 3) users' preferences to each source may change under different scenarios. To address these challenges, we propose Self-Attentive Recommendation for multi-source review Package (SARP), for predicting how useful the user feels to the package, while simultaneously reflecting how much the user is affected by each review. Specifically, SARP jointly considers the relationships of every user, purpose, and review source to learn better latent representations. A self-attention module is further used for integrating source representations and the review ratings, following a multi-layer perceptron (MLP) for the prediction tasks. Experimental results on the self-constructed dataset and public dataset demonstrate that the proposed model outperforms the state-of-the-art approaches.
CAB-Net: Channel Attention Block Network for Pathological Image Cell Nucleus Segmentation

In histopathological image analysis, cell nucleus segmentation plays an important role in the clinical analysis and diagnosis of cancer. However, due to the different morphology of cells, uneven staining and the existence of a large number of dense nuclei, it is still challenging to accurately segment the nucleus. In order to learn more specific key feature information during the training process, this paper proposed a network model called the CAB-Net that uses channel attention to enhance the learning of feature information on each channel. The network uses the channel attention mechanism to extract the key features in each channel, generates weights to judge the importance of the features, and then weights them into the original image. This aims to strengthen the extraction of key information, thereby generating a more characteristic feature map, and enabling the model to make more accurate judgments. We added a boundary smoothness constraint, in order to better identify cell nuclei with unclear boundaries and achieve more accurate cell nucleus segmentation. The experimental results show that the method in this paper achieves good performance on the cell segmentation data set.
No-reference Panoramic Image Quality Assessment based on Adjacent Pixels Correlation

With the popularity of virtual reality, the distortion measurement plays an important role in the processing of panoramic image. The distortion of the panoramic image can make the statistical characteristics of the image change. However, most existing panoramic image quality assessment (IQA) methods ignore the statistical characteristics of panoramic image. Therefore, we propose to utilize adjacent pixels correlation (APC) feature to calculate the statistical characteristics and blindly assess panoramic image quality. Specifically, we find that the distortion can change the proportion of low-frequency information in the panoramic image. We calculate the probability distribution of adjacent pixel difference map using Markov chains to build APC feature, which captures the change in statistical characteristics. Ultimately, the APC feature are fed into support vector regression (SVR) for training and predicting the image quality score. Experiments show that our proposed algorithm is more accurate than the existing algorithms.
Curved path planning based on 3D vision water immersion ultrasonic nondestructive testing

Abstract Compared with traditional 2D image processing, 3D point cloud processing has become a hot technology in the industry, but there are few researches applied to nondestructive testing of curved workpieces. This article aims at the non-destructive testing of curved workpieces, using 3D point cloud and robotic arm for water immersion ultrasonic non-destructive testing. Aiming at the 3D point cloud with many outliers, using the commonly used filter in two-dimensional images-the guide filter, the algorithm is improved and used for the filtering and downsampling of the 3D point cloud, and the adjustment of the curved surface workpiece and the robot arm's pose is introduced. Finally, the workpiece was scanned by a robotic arm with a water immersion ultrasonic nondestructive testing system to prove the feasibility of the experiment and improve the detection efficiency of traditional nondestructive testing technology.
Adversarial Imbalance Classification with Class-Specific Diverse Instance Generation

Data augmentation is an effective technique for imbalance classification. However, it still suffers from two key issues. Firstly, data augmentation and classifier construction are performed separately, where classifier construction may not benefit from the augmentation strategies. Secondly, low variations in generated instances may lead to overfitting problem. In this paper, an Adversarial Imbalance Classification method with Class-specific Diverse Instance Generation is proposed (AICIG). It is a framework that unifies adversarial classifier construction and class-specific diverse instance generation, where these two stages enforce each other seamlessly. Concretely, a specially designed adversarial classifier drives the generation of class-specific instances by class-specific adversarial process. Meanwhile, the distribution of latent variables is assumed to be a Gaussian mixture, which enables to generate diverse instances with limited data. With augmented class-specific and diverse instances, the adversarial classifier can obtain better generalization performance. We conduct experiments on four widely-used imbalanced image datasets and compare them with the state-of-the-art methods. The experimental results exhibit that our method can effectively prevent overfitting and obtain better performance on imbalance classification tasks.
CRF-ERSVM: An Effective and Robust Support Vector Machine Based on Complete Random Forest

Classification is an important part of image recognition, so the accuracy and efficiency of the classifier are required. Class noise is an important factor to make the original data inseparable and seriously affects the validation accuracy of a classifier. A complete random forest method-based class noise filtering learning (CRF-NFL) is proposed, which can effectively filter out the class noise and combine with various classifiers to train the filtered training set. However, this method has two weaknesses: (1) the CRF is not optimized and cannot make the verification accuracy of the classifier better; (2) it is focused only on the combination of various classifiers. To solve these problems, an effective and robust support vector machine based on the complete random forest (CRF-ERSVM) is proposed. Firstly, the proposed method improved the complete random forest (CRF) by means of optimizing the voting percentage. Secondly, the proposed method modified the model of the SVM. At last, the effectiveness of the proposed method is verified by experiments on UCI datasets.
Mobile Application Recommendation based on Demographic and Device Information

The number of people with access to mobile devices, as well as applications to these devices (i.e., apps), has been increasing significantly. Thus, users have to choose among a high number of apps, those that better serve them. However, this is not a trivial task, as we have seen an increasing number of apps proposing to do the same functions. In the same way, companies are facing difficulties to attract users through a usual marketing campaign. A possible solution to this problem is the adoption of recommendation systems, where it is possible to compare the similarities of user profiles. Meanwhile, these systems often consider only users' preferences to create a profile, or request sensitive data (e.g., call and message logs). However, the installation of an app may involve other factors like the capacity of the mobile device (e.g., memory and processing power) and the demographic information of the user's living area. This work investigates the impact of using demographic and handset information on app recommendation. To do that, we use this information to enrich a user profile that has only easy-to-obtain data (i.e., installed apps, approximate location, and handset model). Besides, our proposal was evaluated on three different recommending approaches: Latent Dirichlet Allocation (LDA), Markov Transition Matrix (MTM), and Collaborative Filtering. The general results reveal that the LDA approach achieved the highest efficacy when added information about the user's region mean wage, in terms of precision (approximately 64%) and recall (approximately 28%).
VISUALIZATION OF DATA ANALYSIS FOR EVALUATION OF THE LEVEL OF IT-SPECIALISTS COMPETENCIES’ CREATION

In the study the authors show how visualization and analysis of education data could be used in simulation and adjustment of education program curriculum for future evaluation of the formation levels the graduates competences as specified by educational standard.The authors suggest the scenario that demonstrate the possibility of automatic evaluation of graduate competences formation.This study is a viable teaching tool that can be used to simulation of the pedagogical activity scenario in order to eliminate all kinds of gaps in the training of the graduates and the evaluation of their professional competencies.The approach proposed in the study can be used to build a more result-oriented educational program by its redesigning to eliminate identified gaps in the consideration of important learning outcomes (the formation level of graduates' professional competencies) and evaluate the educational program using an updated visual model of evaluation activity.The relevance of this work is also based on its contribution to expanding the capabilities of multidimensional analysis and visualization of the results in relation to solving professional educational tasks.
Extracting quantum dynamical resources: consumption of non-Markovianity for noise reduction

Abstract A great many efforts are dedicated to developing noise reduction and mitigation methods. One remarkable achievement in this direction is dynamical decoupling (DD), although its applicability remains limited because fast control is required. Using resource theoretic tools, we show that non-Markovianity is a resource for noise reduction, raising the possibility that it can be leveraged for noise reduction where traditional DD methods fail. We propose a non-Markovian optimisation technique for finding DD pulses. Using a prototypical noise model, we numerically demonstrate that our optimisation-based methods are capable of drastically improving the exploitation of temporal correlations, extending the timescales at which noise suppression is viable by at least two orders of magnitude, compared to traditional DD which does not use any knowledge of the non-Markovian environment. Importantly, the corresponding tools are built on operational grounds and can be easily implemented to reduce noise in the current generation of quantum devices.
Minimization of Dynamic Programming Algorithm by Three-dimension Model Collison Test Fitting

In the collision test of simulating object in reality in Unity 3D, the common method taken for non-convex model is convex decomposition. While there exist various kinds of de composition towards models of different forms, it is a combination optimization issue which haven't got the optimized algorithm and which is tedious and time-consuming. While the method of directly judging whether the point is in the inner model or not shows clearer logic and faster judging speed. This judging method can be used in the establishment of non-convex model in computer. The non-convex model, after convex decomposed, is converted into several convex models of fitting model which is then operated collision test. This test method shows a characteristic of simplicity, high-speed and high university.
Demo: A WhatsApp Bot for Citizen Journalism in Rural India

Increasing penetration of Internet-enabled smartphones in low-resource areas makes them an attractive platform for engaging emerging users. In this paper, we demonstrate how a voice forum for citizen journalism in rural India– previously accessible via an Interactive Voice Response (IVR) system– can be naturally supported and enriched using a chatbot. Implemented using the WhatsApp Business API, the bot enables submission of both audio (with or without image) and video stories. Following review by moderators, stories are published on a website and social media sites, and can also be browsed interactively using the WhatsApp bot. This multi-way, intermediated model of communication expands the scope and functionality of typical WhatsApp groups while offering significant cost savings relative to IVR systems. In the first 9 weeks of a long-term deployment, the bot demonstrated high usability and acceptance and resulted in 218 published stories from 27 users.
Missing Data Imputation for Solar Radiation Using Generative Adversarial Networks

Solar power is among one of the major renewable energy sources, and there are various applications depending on solar energy. These applications require continuous time series radiation data to access its usefulness, so missing values in solar dataset can have a significant effect on the performance of solar energy systems. This paper is focused on investigating a machine learning-based data imputation method using generative adversarial networks (GANs). Inspired by the success of GANs in other domains, it is being used for data imputation. The incomplete data adversely effects any time series study and forms hindrance in the path of advancements. The solar insolation missing values in the solar dataset are imputed in this work. The paper uses GAN with the concept of mask and hint matrix to ease the process of imputation. The work is demonstrated on the real-world solar dataset from three different regions. The competence of the model is judged by using the new imputed dataset for training different time series forecasting models. The performance of the model is also compared against frequently used imputation methods.
Object-Orient Semantic-to-Visual Generation Model for Stickers Synthetizing

Stickers are used by social users to express online chat. The retrieval, matching, and generation of stickers have significant research and application value. However, the current stickers are mainly obtained through “semantic-visual” cross-modal search matching in a pre-defined sticker library. The retrieved images from the pre-defined sticker library are fixed and limited, and often do not conform to the contextual semantics of chatting. This work focuses on synthesizing non-existent stickers instead of retrieving images. The Object-Orient Semantic-to-Visual Generation Model(OSVGAN) is proposed to instantly synthesize the corresponding sticker according to the user's semantic description. First, the intrinsic semantic relationship of each object in the sticker is established by an Object-Orient Meta Model. Then a Semantic-to-Visual model is used to synthesize corresponding images according to each object's text feature, and a Fusion Model fuses each object image to form a complete image. Finally, to enhance the semantic consistency of the generated images, the text-image alignment module(T2I) is applied to the Semantic-to-Visual model and the Fusion Model. OSVGAN can effectively reduce the demand for training samples. On the sticker dataset, our method has achieved good semantic consistency, and is comparable to the existing methods in terms of synthetic image quality.
An Iterated Two-Step Sinusoidal Pitch Contour Formulation for Expressive Speech Synthesis

Intonation generation in expressive speech such as storytelling is essential to produce high quality Malay language expressive speech synthesizer. Intonation generation, for instance explicit control, has shown good performance in terms of intelligibility with reasonably natural speech; thus, it was selected in this research. This approach modifies the prosodic features, such as pitch contour, intensity, and duration, to generate the intonation. However, modification of pitch contour remains a problem because the desired pitch contour is not achieved. This paper formulated an improved pitch contour algorithm to develop a modified pitch contour resembling the natural pitch contour. In this work, the syllable pitch contours of nine storytellers were extracted from their storytelling speeches to create an expressive speech syllable dataset called STORY_DATA. All the shapes of pitch contours from STORY_DATA were analyzed and clustered into the standard six main pitch contour clusters for storytelling. The clustering was performed using one minus the Pearson product moment correlation. Then, an improved iterative two-step sinusoidal pitch contour formulation was introduced to modify the pitch contours of a neutral speech into an expressive pitch contour of natural speeches. Overall, the improved pitch contour formulation was able to achieve 93 percent high correlated matches, indicating the high resemblance as compared to the previous pitch contour formulation at 15 percent. Therefore, the improved formula can be used in a text-to-speech (TTS) synthesizer to produce a more natural expressive speech. The paper also discovered unique expressive pitch contours in the Malay language that need further investigations in the future.
Understanding the Feasibility of a Digital Intervention for Social Behaviour Change to Prevent COVID-19 and Improve Health and Nutrition Outcomes

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State Prediction of Chaotic Time-Series Systems Using Autoregressive Integrated with Adaptive Network-Fuzzy

In this research, advanced technology is used to monitoring chaotic time-series signals. The combination of autoregressive with adaptive network-fuzzy algorithms is suggested for chaotic signal prediction. The autoregressive prediction algorithm is recommended for chaotic time-series prediction. This technique is linear, and the modeling prediction accuracy has a limitation. To reduce the root means square (RMS) error of prediction, the order of autoregressive prediction should be increased which is caused to increase the number of parameters and nonlinearity as well. Thus, the combination of autoregressive prediction with an adaptive network-fuzzy algorithm is suggested to reduce the prediction error in chaotic time-series signals. To test the power of the proposed prediction algorithm, the 2nd order proposed method is compared with the 2nd order and 6th order of AR technique, and the RMS error in these three algorithms are 0.0967, 0.4953, and 0.3159, respectively. So far and compared to the classical autoregressive method, the proposed prediction model is efficient for chaotic time-series signals.
An improved ant colony optimization algorithm with negative feedback

To improve the ability for solving complex problems, an improved ant colony optimization algorithm with negative feedback strategy was proposed. Based on an inspired matrix, the new negative feedback strategy was designed to change the basic ant colony algorithm pheromone matrix to the opposite damper matrix. That was, even more dampers occurred on the shorter path found by the colony, reducing the probability that each section on the path was selected in the next time. Because of the enlightening matrix, the probability of more short circuit segments on the path was still greater in the process of optimization again, while the probability of longer path selected was decreased which was not easy to be selected in the next process. It was suitable to do further optimize. Through the experiment of TSP, it showed that the ant colony algorithm with combining the damper matrix and the original pheromone matrix can improve the searching ability obviously.
Verifiably-Extractable OWFs and Their Applications to Subversion Zero-Knowledge

An extractable one-way function (EOWF), introduced by Canetti and Dakdouk (ICALP 2008) and generalized by Bitansky et al. (SIAM Journal on Computing vol. 45), is an OWF that allows for efficient extraction of a preimage for the function. We study (generalized) EOWFs that have a public image verification algorithm. We call such OWFs verifiably-extractable and show that several previously known constructions satisfy this notion. We study how such OWFs relate to subversion zero-knowledge (Sub-ZK) NIZKs by using them to generically construct a Sub-ZK NIZK from a NIZK satisfying certain additional properties, and conversely show how to obtain them from any Sub-ZK NIZK. Prior to our work, the Sub-ZK property of NIZKs was achieved using concrete knowledge assumptions.