Reviewerly/exHarmony huggingface dataset:
A Multiinterest and Social Interest-Field Framework for Financial Security

Online payment has become an influential method of transaction. While improving convenience, this method of payment also brings great financial risks. Prevalidating transactions can be effective in reducing the number of frauds. For this reason, recommendation algorithms have been introduced to measure the credibility of transactions by predicting users’ ratings of items. However, most algorithms deal with the relationships in social networks without distinction, mixing positive and negative information into the recommender system, which brings huge noise. And, they only generate a single interest representation for each user to measure the similarity between users and spread interest, ignoring the diversity of user interests. Moreover, they did not consider the propagation of different interests would be different. In this article, we propose a multiinterest and social interest-field framework (MISIF) for social recommendations in financial security, which introduces capsule networks into social recommendation and extends the traditional single-interest representation to user multiinterest embedding by dynamic routing (DR) and other methods to improve the expressiveness of user embedding. After that, we construct social interest fields to integrate social interests based on multiinterest embedding, which alleviates the noise in social networks and user data sparsity problems. Finally, we aggregate user multiinterest embedding and additional information through neural networks to obtain the final prediction scores. Experiments with three publicly available datasets show that our proposed MISIF framework outperforms the state-of-the-art social recommendation methods.
When parsing and interpretation misalign: A case of WH-scope ambiguity in Mandarin

A large amount of sentence-processing work has focused on revealing how the parser incrementally integrates each incoming word into the current linguistic representation. It is often explicitly or implicitly assumed that the structure endorsed by the parser should determine the ultimate interpretation of the sentence. The current study investigates whether the interpretive bias in sentence comprehension necessarily tracks the parsing bias. Our case study concerns the locality bias in nonlocal dependencies, specifically Mandarin wh-in-situ scope dependencies. Our findings suggest a misalignment between parsing and interpretative decisions at the global level. In particular, for Mandarin wh-in-situ constructions that involve scope ambiguity, there is a locality bias in parsing, but an antilocality bias in interpretation. Building upon the rational speech act framework, we propose a Bayesian pragmatic analysis to account for these findings. Under our proposal, the seeming conflict between parsing and interpretation will ultimately disappear because parsing preferences will be naturally embedded under the pragmatic reasoning process to generate the ultimate interpretation. The current study therefore makes novel contributions, both empirically and theoretically, to addressing the broader question about the relationship between parsing and interpretation.*
Exposure to and Speaking Up Against Online Ethnic Hate Speech: The Role of Xenophobia in a Three-Wave Longitudinal Study

Nowadays, adolescents have extensive access to Information and Communication Technologies, which allow them to engage in social networking activities that may expose them to Online Hate Speech (OHS). While there are few cross-sectional studies about the effects of OHS Exposure on attitudes and aggressive behavior, no study aims to analyze the tendency to Speak Up when exposed to certain content (e.g., reporting, etc.). In addition, no instruments have yet been validated to assess these constructs. The aim of the present study, focused on Online ethnic Hate Speech (OeHS), is double: (a) develop a scale to measure OeHS Exposure and the tendency to Speak Up and analyze its psychometric properties; (b) analyze the longitudinal association between Xenophobia (XEN), OeHS Exposure, and Speaking Up against OeHS, while taking into account gender differences and the nested nature of the data. Six hundred sixty-six Italian high school students (52.7 percent male; MAge = 15[0.64]), nested in 36 ninth grade classes (10 schools), took part in the longitudinal study. The first wave of data collection occurred in early 2020, before the COVID-19 pandemic. The second and third waves took place 12 and 15 months later, respectively. Findings suggest that the OeHS Scale has good psychometric properties. Moreover, according to the findings, while the three variables of interest are always cross-sectionally correlated, a longitudinal negative association have been found between XEN and both Exposure and Speaking Up. Regarding the impact of OeHS Exposure, the good news is related to the absence of a longitudinal association with both XEN and Speaking Up.
Long Text QA Matching Based on ESIM of Fusion Convolution Feature

QA matching is a popular research direction of NLP, but the current research on QA matching is focused more on short text matching. Generally speaking, the long text has more information than the general short text, but it is accompanied by more interference information, which leads to the decrease of generalization ability of the short text matching model in the long text matching task. This paper constructs the psychological counseling question answering database and verifies the long text QA matching methods based on deep learning. At the same time, we introduce Convolutional Neural Network to improve the Inference layer of ESIM (Enhanced Sequential Inference Model), so that it can better extract long text features to improve the inference analysis ability of the model. Experimental results show that the improved ESIM is effective in QA long text matching in psychological counseling.
Abnormal Flow Monitoring of Industrial Control Network Based on Neural Network

Aiming at the network security problem caused by the rapid development of network, this paper uses a network traffic anomaly detection method of industrial control system based on convolutional neural network. In the traditional machine learning algorithm, the processing of features has a high impact on the performance of the model, and the model is highly dependent on features. This method uses the characteristics of convolutional neural network to autonomously learn features, which avoids this problem. In order to verify the superiority of the model, this paper takes accuracy as the evaluation index, and compares it with the traditional machine learning algorithm. The results show that the overall accuracy of the method is 99.88 %, which has higher accuracy than traditional machine learning algorithms such as decision tree algorithm (ID3), adaptive boosting tree (Adboost) and naive Bayesian model. Therefore, this method can be better applied to the anomaly detection of network traffic in industrial control system, and has practical application value.
Password Guessing Attack Based on Probabilistic Context Free Algorithm

Password is the primary way of identity authentication at present. The password security is closely related to more than 4 billion netizen all over the world. Password contains lots of semantic information, so how to extract the semantic of password and apply it to password guessing algorithm can further uncover the behavior preference of users in creating passwords, and improve the cracking rate of guessing attacks. We analyze the background and present situation of password security research, and determine the general steps and basic framework of password guessing algorithm based on natural language processing technology. We introduce the relevant preparatory knowledge and make statistical analysis on many password data sets. The popular password, password grammar, password pattern, character composition, length distribution, character distribution and semantic information of password data set are analyzed. We propose a password guessing algorithm based on probabilistic context free algorithm. The actual leaked password data set is selected for training and testing, and several groups of password guessing contrast experiments are set up. The results prove the effectiveness of proposed algorithm.
A Study on Development of Test and Evaluation Methods for AI-based Image Surveillance Systems in Defense

Purpose: This study proposes a method of data set collection and utilization in conjunction with the pathway of test and evaluation (T&E) methods for the military use of AI-based image surveillance systems, which is a crucial application of AI technology.BRMethods: The inadequacy of current T&E methods used in AI-based image surveillance system in the military was revealed by indicating the incompatibility of current acquisition process and identifying characteristics of the AI-based image surveillance systems for military purposes.BRResults: A data set collection method as well as alternative T&E methods and an acquisition procedure for the military use of the AI-based image surveillance system are proposed. BRConclusion: It is expected that the consensus can be reached for the importance of the development of the T&E methods along with the improvement of the relevant policies and regulations. Moreover, it would serve as a guideline for setting up the procedure of the T&E methods for the AI-based image surveillance system in defense.
A Study on the Correlation between Warship Displacement and Reliability Using Field Data RAM Analysis Results

Purpose: To examine the correlation between warship displacement–associated with total life cycle cost and warship capability–and reliability using field data Reliability, Availability, and Maintainability (RAM) analysis results.BRMethods: The Defense Agency for Technology and Quality recorded data on existing warships beginning in 2014. A field data RAM analysis was conducted on the data using Microsoft Excel correlation analysis and trend line. The results were analyzed to determine the correlation between warship displacement and reliability.BRResults: The correlation between warship light displacement and basic reliability was greater than mission reliability; the correlation between the warship displacement and basic reliability was declining. The basic reliability decreased by -0.0017 as the light displacement increased. BRConclusion: The effective target reliability setting method can be discovered by; (1) utilizing the correlation between warship light displacement and basic reliability, (2) conducting additional research to discover other factors affecting reliability and (3) applying various statistical techniques on the data.
Dynamic Network Representation Learning Method Based on Improved GRU Network

As social networks have been rapidly growing, traditional network representation learning methods are struggling to accurately characterize their dynamic changes, and to output effective node classification and link prediction. To address this problem, this paper proposes IproGRU, a dynamic network representation learning method based on an improved Gated Recurrent Unit (GRU) network to improve the dynamic network representation. First, the method quickly generates embedding for an influenced node by sampling and aggregating features of its neighboring nodes when the network changes. Second, it updates the embedding of the influenced node on time series by the improved GRU network to fully adapt to the changes of the dynamic network. Experimental results on node classification and link prediction for three datasets of dynamic networks show that the proposed method improves the accuracy by 5–10 % on average from those of the traditional Node2vec and GraphSAGE methods and has a slight advantage over Graph Convolutional Networks (GCNs). The results demonstrate that our method is effective for dynamic network representation.
Unsupervised Learning

Under unsupervised learning, clustering or cluster analysis is first studied. Clustering methods are grouped into non-hierarchical (including K-means clustering) and hierarchical clustering. Self-organizing maps can be used as a clustering method or as a discrete non-linear principal component analysis method. Autoencoders are neural network models that can be used for non-linear principal component analysis. Non-linear canonical correlation analysis can also be performed using neural network models.
A Scalable Tree Based Path Planning for a Service Robot

Path planning plays a vital role in a mobile robot navigation system. It essentially generates a shortest traversable path between two given points. There are many path planning algorithms proposed by researchers all over the world, however, there are very little work focussing on path planning for a service environment. The general assumption are either the environment is fully known or unknown. Both the cases will not be suitable for a service environment. Afully known environment will restrict further expansion in terms of number of navigation points and unknown environment would give an inefficient path. Unlike other environments, service environments have certain factors to be considered, like user friendliness, repeatability, scalability and portability which are very essential for a service robot. In this paper a simple, efficient, robust and environment independent path planning algorithm for an indoor mobile servicerobot is presented. Initially the robot is trained to navigate to all the possible destinations sequentially with minimal user interface which will ensure the robot knows partial paths in the environment. With the trained data the path planning algorithm maps all the logical paths between all the destinations, which helps in autonomous navigation. The algorithm is implemented and tested using 2D simulator Player/Stage. The proposed system is tested with two differentservice environment layouts and proved to have features like scalability, trainability, accuracy and repeatability. The algorithm is compared with various classical path planning algorithms and the results shows that proposed path planning algorithm is in par with the other algorithms in terms of accuracy and efficient path generation.
Revisiting the Initial Peak P-Wave Displacement and the Ground Motion Characteristic Period with Signal-To-Noise Ratios: A Case Study Using a Low-Cost Sensor Network in Taiwan

Early warning systems can estimate the possible-shaking intensity of the earthquake based on the initial P-waves on-site. Researchers have used the following two indicators: the initial peak P-wave displacement (Pd) and the ground motion characteristic period (τc). In this study, we re-examine the performance of the two indicators by employing ground motion data from the P-Alert low-cost network in Taiwan. We have found that the signal-to-noise ratio of displacement (SNRd) is effective for data selection. In addition, we have observed the irregular behavior of τc in the P-Alert dataset.
Development of Software Education Program for Fostering Computational Thinking in the Era of the 4th Industrial Revolution

The purpose of this study is to develop a software education program to foster computational thinking for the previous generation of software education. In an environment that is rapidly changing due to the 4th industrial revolution, developing computational thinking skills and the ability to use software are important core competencies that both future and previous generations should have. Therefore, research on educational programs that allow previous generations of software education to easily access software education is needed. Software education is conducted in various ways, and the ultimate goal of software education is to enhance computing thinking by enhancing problem-solving skills based on computing thinking. Software education methods include various teaching and learning methods such as physical computing, unplugged computing, storytelling, and flip learning. Methods using physical or unplugged computing have the advantage of being able to train software without a computer without restrictions on location. In this study, a software education program was applied to parents and students of elementary schools, which were previous generations of software education, and positive results were obtained through a post-satisfaction survey. It is expected that it will be used as a useful educational material when various educational places such as companies want to organize software education programs to cultivate computational thinking.
Duality of Codes over Non-unital Rings of Order Four

In this paper, we present a basic theory of the duality of linear codes over three of the non-unital rings of order four; namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">I, E</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</i> as denoted in (Fine, 1993). A new notion of duality is introduced in the case of the non-commutative ring <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> . The notion of self-dual codes with respect to this duality coincides with that of quasi self-dual codes over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> as introduced in (Alahmadi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> , 2022). We characterize self-dual codes and LCD codes over the three rings, and investigate the properties of their corresponding additive codes over F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> . We study the connection between the dual of any linear code over these rings and the dual of its associated binary codes. A MacWilliams formula is established for linear codes over <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</i> .
EPR-ML: E-Commerce Product Recommendation Using NLP and Machine Learning Algorithm

Using tags and other forms of textual information, online retailers can create their product listings, descriptions, and categories. E-commerce information services, such as search and product recommendation, depend significantly on textual features to assist buyers in finding the items they want. This research focuses on “tags,” which often use textual descriptions of items. We assume that merchants are not always the “best” suppliers of item tag information, either because they are ill-equipped to do so (since they have not been “trained”) or because they are purposefully attempting to rig the system by using misleading or erroneous tags to sell their commodities (tag spam). To address these concerns, we may use automated tag recommendation techniques to enhance the precision with which we suggest tags for every specific product. We proposed EPR-ML for E-commerce product recommendation using NLP and ML algorithms. This research employed a product sentiment dataset normalized using NLP; the best features were selected using Logistic regression (LR). The classification was performed using various machine learning algorithms, including Linear support vector machine (L- SVM) and Gaussian nave Bayes (GNB), to determine which model is most accurate at predicting the number of days it will take a video to trend from the time it was uploaded and the number of days it will trend on the trending list. Using LSVM, the research achieved a maximum accuracy of 96%.
Bounds and Protocols for Graph-Based Distributed Secret Sharing

Distributed Secret Sharing is a (multi) secret sharing model in which the shares are distributed over storage nodes of a network and each participant is able to reconstruct a specific secret by accessing a subset of the storage nodes. In this work, we provide new Distributed (multi) Secret Sharing Protocols for a specific class of access structures, namely those that can be described with a graph. The protocols improve on previous results allowing a faster encoding and decoding phase while maintaining optimal storage requirements. Moreover, our protocols can manage any kind of graph, while previous protocols have been designed only for complete graphs, and we provide a complete characterization of graph-based protocols. We also prove some tight bounds on the size of the information held in the storage nodes and communication complexity by using an information-theoretic approach. Finally, we also introduce a computationally secure technique for the general case that allows improvements in the size of the needed disk space if secrecy is computational, that is, if the scheme is robust against resource-bounded adversaries.
Non-stationary neural signal to image conversion framework for image-based deep learning algorithms

This paper presents a time-efficient preprocessing framework that converts any given 1D physiological signal recordings into a 2D image representation for training image-based deep learning models. The non-stationary signal is rasterized into the 2D image using Bresenham’s line algorithm with time complexity O(n). The robustness of the proposed approach is evaluated based on two publicly available datasets. This study classified three different neural spikes (multi-class) and EEG epileptic seizure and non-seizure (binary class) based on shapes using a modified 2D Convolution Neural Network (2D CNN). The multi-class dataset consists of artificially simulated neural recordings with different Signal-to-Noise Ratios (SNR). The 2D CNN architecture showed significant performance for all individual SNRs scores with (SNR/ACC): 0.5/99.69, 0.75/99.69, 1.0/99.49, 1.25/98.85, 1.5/97.43, 1.75/95.20 and 2.0/91.98. Additionally, the binary class dataset also achieved 97.52% accuracy by outperforming several others proposed algorithms. Likewise, this approach could be employed on other biomedical signals such as Electrocardiograph (EKG) and Electromyography (EMG).
Perspective effect enhancement for light field refocusing using depth-guided optimization

Light field (LF) image can improve perspective effect and immersive experience. It also provides new capabilities such as depth estimation, post-capture refocusing, and 3D modelling among which refocusing is potentially very useful in many applications such as smart phone. This paper addresses a novel depth-guided enhancement of light field images to improve depth contrast. The proposed method is formulated as an optimization problem that can consider not only the desired inter-depth contrast constraints of depth map, but also intra-depth luminance intensity contrast and color saturation contrast constraints in each depth layer. Experiment result shows that it outperforms the state-of-the-art methods in its depth contrast and detail preservation in each depth layer. It is observed also that the proposed method can enlarge luminance range by 7~15% and improve color contrast by 3~8%.
Accurate dynamic 3D deformation measurement based on the synchronous multiplexing of polarization and speckle metrology

An accurate dynamic 3D deformation measurement method realized by the combination of phase-shifting speckle interferometry and speckle correlation is proposed. By converting the speckle field and the reference field into a circular polarized and linear polarized state, the three-step phase-shifting speckle interferograms and one specklegram were recorded directly and simultaneously within a single image by using a polarization camera. Then, the out-of-plane deformation was demodulated from the synchronous phase-shifting fringe patterns, and the in-plane deformation was measured by performing correlation calculations by using specklegrams with the effect of the reference field ignored. Thus, the full-field 3D deformation was obtained precisely. Experimental results demonstrated the accuracy and dynamic measurement ability of the proposed method, which is compact and feasible for actual dynamic scenes.