Reviewerly/exHarmony huggingface dataset:
A solver of single-agent stochastic puzzle: A case study with Minesweeper

People have enjoyed solving puzzles for decades because of the challenge and the satisfaction derived from solving problems. However, although previous researches focused on the puzzle’s complexity, strategy, and solving automatically, few studies worked on sorting out puzzles from a solvability way related to the stochastic elements among the solving process. The contribution of the study is twofold. Firstly, a single-agent stochastic puzzle definition is established via Minesweeper testbed, a well-known puzzle synonymous with Microsoft Windows. Secondly, this study proposes an artificial intelligence (AI) solver based on the obtained information on the board, called the ‘PAFG’ strategy, which stands for the primary reasoning, the advanced reasoning, the first action strategy, and the guessing strategy. The first two strategies take advantage of knowledge-based rules and linear system transformation (Gauss–Jordan elimination algorithms) to determine the probability of making a move independently. The last two strategies explore the beginning and ways to determine hidden puzzle states to enhance the winning rate of the AI solver. The experimental simulation of various configurations and the AI solver with PAFG strategy yielded a high-level winning rate of 96.4%, 86.3%, and 45.6% for the 9×9|10, 16×16|40, and 16×30|99 Minesweeper board configuration, which is comparable to the state of the art study. Thus, such an AI solver could contribute to classifying single-agent stochastic puzzles and establishing the boundary of the puzzle-solving and game-playing paradigm.
Efficient Implementation of Artificial Neural Networks for Sensor Data Analysis Based on a Genetic Algorithm

The reliability of many industrial processes depends on the sensor system. However, these sensors can be affected by noise, perturbations and failures. Hence, sensor monitoring and diagnosis are fundamental to guarantee the quality of an industrial process. Nowadays, artificial neural networks (ANN) are widely used in sensor signal processing and diagnosis. However, those ANNs usually require many artificial neurons, being difficult to implement in software and hardware due to their high computational costs. This paper presents an optimized implementation of artificial neurons in ANNs for sensor data analysis using a Genetic Algorithm (GA). The objective of GA is to find an adequate segmentation to reduce the activation function approximation error. One of the advantages of the proposed approach is that the cost function used in GA considers the effect of factors such as the ANN architecture or the number of bits used in arithmetic operations. The proposed ANN implementation technique aims to get the best possible approximation for a specific ANN architecture, making easier its implementation in software and hardware. Simulation and experimental results using FPGA (Field Programmable Gate Array) prove the advantages of the proposed approach for implementing sensor data analysis systems based on ANNs.
Replicated Computational Results (RCR) Report for “A New Test for Hamming-Weight Dependencies”

In the paper “A New Test for Hamming-Weight Dependencies”, Blackman and Vigna propose a new statistical test for pseudorandom number generators (PRNG) . Compared with the state-of-the-art tests, the proposed test could find statistical bias in the Hamming weights of the output of the generator. The proposed test is evaluated by using generators in TestU01 [ 2 ]. The authors provide the C code used in the tests and the code successfully reproduced the results shown in the article.
Photovoltaic Panels and Solar Collectors in Cityscape of Bialystok (Poland) and Cordoba (Spain)

Abstract The technologies using of renewable sources energy like photovoltaic panels and solar collectors were used in Europe, mainly in southern countries (like Spain) until recently. EU policy of sustainable development and technological development impact on increase of popularity of those technologies in northern European countries like Poland. The research problem of the work is to determinate the appropriate conditions for the location of above-mentioned technology (panels) in the cityscape. The numerous examples of its destruction were noticed because panels were located using only energy efficiency conditions. The research methodology was based on “in situ” research (Bialystok-Poland, Cordoba – Spain) in order to determinate the categories of urban landscape in which panels are located. Technical, climatic, landscape and composition conditions were analysed in order to establish assessment of completed investments. The result of the science work are categories of the urban landscape views in Bialystok and Cordoba, ie architectural and landscape encloses (streets, squares, public gardens), city panoramas. The proposed methodology of assessment of cityscape impact are applicable to the planning, designing and investment process for Bialystok and Cordoba. They could be used to another cities also in necessary condition of taking into account the individual characteristic of cityscape. The proposed conclusions can be used in the planning, designing and investment process of another cities due to protection of their cityscape values.
Distinguishing Hate Speech from Sarcasm

In this contemporary world, people use social media tremendously to communicate and have fun with their friends and family. Over the past few years, the ethical use of social media has been exploited and people have started to post comments or tweets expressing their hatred towards other communities. While some people claim them to be sarcastic comments, others feel they are offensive and depict hate speech. This has become a major issue as these are dependent on an individual&#x0027;s perspective. We have constructed a dataset that contains sentences from one of the four classes-&#x201D; Hate Speech&#x201D;, &#x201C;Offensive&#x201D;, &#x201C;Sarcasm&#x201D;, &#x201C;Normal&#x201D;. The dataset has been evaluated using various state-of-the-art models like Bag of Words, TF-IDF, BERT and ERNIE. The best accuracy was achieved by BERT accounting to around 80&#x0025;.
Num-Symbolic Homophonic Social Net-Words

Many excellent studies about social networks and text analyses can be found in the literature, facilitating the rapid development of automated text analysis technology. Due to the lack of natural separators in Chinese, the text numbers and symbols also have their original literal meaning. Thus, combining Chinese characters with numbers and symbols in user-generated content is a challenge for the current analytic approaches and procedures. Therefore, we propose a new hybrid method for detecting blended numeric and symbolic homophony Chinese neologisms (BNShCNs). Interpretation of the words’ actual semantics was performed according to their independence and relative position in context. This study obtained a shortlist using a probability approach from internet-collected user-generated content; subsequently, we evaluated the shortlist by contextualizing word-embedded vectors for BNShCN detection. The experiments show that the proposed method efficiently extracted BNShCNs from user-generated content.
A new attention mechanism based on inner product for medical image classification

This paper introduces a novel method to refine the features in a deep learning (DL) model which is built for medical image classification. By framing it as a fine-grained visual categorization task, we propose an attention mechanism based on the inner product between feature column vector and the learnable attention vector. The proposed method is conducted on two open-source benchmark datasets. The results of the experiment suggest that the model be quite stable and accurate under the indicators of accuracy, precision, recall, and F1-score. It is found that inserting the inner product attention not only enhances the overall classification accuracy and efficiency, but also improves the recognition ability of learned features. Indeed, employing the proposed attention mechanism can increase the feature extraction capability of the DL models.
Steganalysis for DCT inserts with the Koch-Zhao steganographic method in low stego-payload images

Abstract This study focuses on the problem of detecting and locating secret data embedded using the Koch-Zhao steganographic method with discrete embedding. The proposed method of steganalysis is based on discrete cosine transform and enables to control and verify JPEG and MPEG images of stego-payload not exceeding 25%. Mid-frequency components are considered as the components targeted by the Koch-Zhao steganographic method. The proposed method of steganalysis delivers strong detection and locating accuracy and performs in 10-% stego-payload cases with sufficient reliability. Both gray-scale and color images can be verified on being a carrier or not being when using the detecting method proposed in the study.
SMART-PHONE ENHANCED CLASSROOM: A STUDENT-CENTRED BLENDED LEARNING DESIGN FOR CHINESE UNDERGRADUATES

Y.F. Yue Peoples' Friendship University of Russia (RUDN University) / University of Science and Technology Liaoning (USTL) (CHINA)
A high precision measurement method of large-size turbine blade based on structured light 3D measurement

The large-size blade is the core component of nuclear power and ultra-supercritical steam turbine, and its existing dimension detection methods generally have the problems of low detection efficiency or insufficient accuracy. So, an efficient and high-precision measurement technology of full profile based on multi view structured light 3D measurement is proposed in this paper. And several key factors affecting the measurement accuracy are analyzed. Then a highlight elimination technology combining multi view and phase shift is proposed to eliminate the effect of specular on the smooth metal surface of the blade. A non-overlapping point cloud registration method based on feature constraints is presented to solve the difficulty caused by the lack of overlapping point cloud on the thin inlet and outlet edges of blade and other areas. Finally, a platform for the whole profile of large-size turbine blade is constructed to realize the full profile measurement and evaluation of large-size turbine blades. The measurement results show that the full surface point cloud of an about 900mm long blade is completely obtained by the measurement platform, and the measurement accuracy of 70μm is achieved through the comparison test of high-precision gauge blocks and some standard balls.
Self-attention perceptron personalized recommendation model with automatic feature correlation

How to automatically obtain cross-features with different weight values is a significant issue in the research of recommendation models. Traditional recommendation models cannot automatically learn the deep-level features of users and items to obtain cross-features. The mixed processing of dense numerical features and sparse categorical features will result in more information loss during dimensionality reduction. Cross features occupy the same weight in the recommendation process, which will lead to the non-prominence of critical features and reduce the accuracy of model recommendations. This paper proposes a personalized recommendation model (MSRN) for self-attention perceptron with automatic feature correlation. The model first processes the numerical features and category features in double towers to reduce the loss of feature information. Numerical cross-feature matrix and category cross-feature matrix use multilayer perceptrons to automatically mine the hidden knowledge and relationships between features. The model uses the Hadamard product to process it to obtain the cross feature matrix and uses the self-attention mechanism to assign different weights to the extracted cross-features. The experimental results on the public data set show that the recommended evaluation indicators of this model, MAE, and RMSE, are better than the current advanced recommendation models and have better accuracy and stability.
Simultaneous Brightness and Contrast Enhancement Using Derived Inputs and Residual Squeeze Network

In this paper, we propose a method to simultaneously improve the contrast and brightness of low-light images by using the concept of derived inputs and a residual squeeze and excitation block (RSEB) network. The proposed method decomposes the input image into reflectance and illumination based on Retinex theory. Decomposed illumination, gamma corrected illumination, and histogram equalized illumination are combined to create Derived inputs. Then, the RSEB network receives the Derived inputs as input and adaptively rescales the feature map to show the resulting image with enhanced brightness and contrast. Experimental results demonstrates that the proposed method provides excellent result images for both quantitative and qualitative measurements.
Envisaging an Intelligent Blockchain Network by Intelligence Sharing

Blockchain Technology is gaining popularity throughout various industry verticals due to its data decentralization and tamper-evident nature. Machine Learning (ML) is all about embedding a learning capability to computing machines so that the machine can learn based on historical data in a way how human beings learn things. An important part of ML is the process of learning which needs humongous processing capability and hence it is time-consuming. Significant benefits have been predicted from the integration of these two technologies. Making a complete blockchain network intelligent in a simple and efficient way is a major challenge. In this work, a Multi Layer Perceptron (MLP) model is implanted in every node of the blockchain network. An efficient technique is proposed to make an intelligent blockchain network in minimum possible time and using minimum processing power. During the network formation, every node of the network has knowledge of the model architecture. At some point in time, the model of the randomly selected node gets trained. After completion of the training of that node, the intelligence is replicated to the entire network.
Design of High-Reliablitity IoT System Delivery Platform based on Blockchain and System Prototyping

In recent years, an IoT (Internet of Things) system that supports human activities based on various types of real-world data (e.g., temperature, location, etc.) has been attracting attention in various fields. The operation of an IoT system is determined based on the real-world data measured and collected by sensor devices placed in various locations in the real world. If the real-world data is tampered within the process of measurement, collection and analysis, the reliability of the IoT system cannot be guaranteed. Therefore, we propose a new blockchain-based service delivery platform to support the development of IoT systems that can guarantee the reliability of both the real-world data and the procedure for analyzing the data. Furthermore, we design and prototype an IoT system based on the proposed platform for evaluating the effectiveness. The prototype system monitors the environmental information of offices and other facilities and analyzes the real-world data to identify risk of coronavirus infection. In the prototype system, the proposed platform provides the user with the function for validating that the analytical result is derived by applying the correct process to the real-world data.
Dimensional Reduction Methods Comparison for Clustering Results of Indonesian Language Text Documents

High-dimensional data refers to a dataset with numerous attributes. When text documents are pre-processed, each word becomes an attribute, thereby making it high-dimensional data. Clustering is one of the data mining processes used to group high-dimensional data into classes of similar objects for proper analysis. However, the use of this technique in the mining text document is challenging because it does not use the regular clustering algorithms. One common way to enhance text document clustering is by reducing the data dimensions through features selection and extraction. Therefore, this study reduced clustering in Indonesian text documents by implementing Singular Value Decomposition and Principal Components Analysis as feature selection and extraction methods against the k-Means clustering algorithm. Comparisons were made with Davies Bouldin Index, iterations, and computation times. The results showed significant differentiation between k-Means, (SVD + k-Means), and (PCA + k-Means) for the Davies Bouldin Index and computational time.
A Kernel-Expanded Stochastic Neural Network

Abstract The deep neural network suffers from many fundamental issues in machine learning. For example, it often gets trapped into a local minimum in training, and its prediction uncertainty is hard to be assessed. To address these issues, we propose the so-called kernel-expanded stochastic neural network (K-StoNet) model, which incorporates support vector regression as the first hidden layer and reformulates the neural network as a latent variable model. The former maps the input vector into an infinite dimensional feature space via a radial basis function kernel, ensuring the absence of local minima on its training loss surface. The latter breaks the high-dimensional non-convex neural network training problem into a series of low-dimensional convex optimization problems, and enables its prediction uncertainty easily assessed. The K-StoNet can be easily trained using the imputation-regularized optimization algorithm. Compared to traditional deep neural networks, K-StoNet possesses a theoretical guarantee to asymptotically converge to the global optimum and enables the prediction uncertainty easily assessed. The performances of the new model in training, prediction and uncertainty quantification are illustrated by simulated and real data examples.
Sentence Boundary Disambiguation for Tibetan Based on Attention Mechanism at the Syllable Level

Tibetan is a low-resource language with few existing electronic reference materials. The goal of Tibetan sentence boundary disambiguation (SBD) is to segment long text into sentences, and it is the foundation for downstream tasks corpora building. This study implemented the Tibetan SBD at the syllable level to avoid word segmentation (WS) errors affecting the accuracy of SBD. Specifically, the attention mechanism is introduced based on a recurrent neural network (RNN) to study Tibetan SBD. The primary objective is to determine, using a trained model, whether the shad contained in Tibetan text is the ending of the sentence, and implement experiments on syllable embedding and component embedding to measure the model's performance. The highest accuracy for Tibetan syllable embedding and component embedding is 96.23% and 95.40 %, respectively, and the F1 score reaches 96.23% and 95.37%, respectively. The experimental results demonstrate that the proposed method can achieve better results than the established rule-based and statistical methods without considering various syntactic and part-of-speech (POS) tagging rules. German and English data from the Europarl corpus and Thai data from the IWSLT2015 corpus are validated to prove the models’ reliability and generalizability. The results demonstrate that this method is efficient not only for low-resource languages but also for high-resource languages. More importantly, we can formally apply the experimental results of this study to the research of downstream tasks, such as machine translation and automatic summarization.
Binary differential evolution with self learning and deep neural network for breast cancer classification

Abstract Early classification of breast cancer helps to treat the patient effectively and increases the survival rate. The existing methods involve applying the feature selection methods and deep learning methods to improve the performance of the breast cancer classification. In this research, the binary differential evolution with self learning (BDE‐SL) and deep neural network (DNN) method is proposed to improve the performance of the breast cancer classification. The BDE‐SL feature selection method involves selecting the relevant features based on the measure of probability difference for each feature and non‐dominated sorting. The DNN method has the advantage which effectively analysis the non‐linear relationship among the selected features and output. The BI‐RADS MRI breast cancer dataset was applied to test the performance of the proposed method. The adaptive histogram equalization and region growing applied in the input images to enhance the image. The dual‐tree complex wavelet transform, gray‐level co‐occurrence matrix, and local directional ternary pattern were the feature extraction method used for the classification. This result shows that BDE‐SL with the DNN method has an accuracy of 99.12% and the existing convolutional neural network has 98.33% accuracy.
Cracking CAPTCHAs using Deep Learning

In this decade, digital transactions have risen exponentially demanding more reliable and secure authentication systems. CAPTCHA (Completely Automated Public Turing Test to tell Computers and Humans Apart) system plays a major role in these systems. These CAPTCHAs are available in character sequence, picture-based, and audio-based formats. It is very essential that these CAPTCHAs should be able to differentiate a computer program from a human precisely. This work tests the strength of text-based CAPTCHAs by breaking them using an algorithm built on CNN (Convolution Neural Network) and RNN (Recurrent Neural Network). The algorithm is designed in such a way as an attempt to break the security features designers have included in the CAPTCHAs to make them hard to be cracked by machines. This algorithm is tested against the synthetic dataset generated in accordance with the schemes used in popular websites. The experiment results exhibit that the model has shown a considerable performance against both the synthetic and real-world CAPTCHAs.
Self-service analytics and the processing of hydrocarbons

This paper describes the use of self-service analytics on time series process data for the troubleshooting and optimization of refinery operations in the context of data visualization principles and best practices. Refining-relevant examples are used to demonstrate how end-users can access real-time and historical process data and apply the following analytics operations across several refining functions, including (1) incident troubleshooting – identifying periods of interest and methods available to investigate related plant data, patterns, events and disturbances leading up to the incident, and (2) data cleansing – filtering sensor data to remove outliers and bad quality data, splicing and aligning data streams for more accurate analysis and to improve the confidence in the outputs of subsequent analysis, such as the outputs of multivariate, regression-based system identification. The paper also provides examples of how ad hoc analyses can be scaled up to plantwide analytics and evolve into routine, automated tasks. The importance of analytic provenance and collaboration in sharing new insights from data is also discussed. To address the human factors associated with self-service analytics innovation, the paper concludes with lessons learnt, observations and adaptations compared to the traditional “business-as-usual approaches, best practices for data governance, and the implications for engineers that operate in a safety-critical environment.