Reviewerly/exHarmony huggingface dataset:
Prior Knowledge Augmentation Network for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis is a popular task in Natural Language Processing (NLP). Many methods utilize attention mechanisms and graph neural networks on dependency trees to identify the most relevant opinion words for aspects. However, improvements are limited due to the presence of rare words in small datasets and the inability to guarantee that each token will have a high probability of interacting with aspects. To address these challenges, we propose a Prior Knowledge Augmentation Network (PKAN) model that incorporates rich semantic information from knowledge graphs, syntactic structures, and prior probabilistic position embeddings. Specifically, to better comprehend semantic information, we design a knowledge augmentation module that constructs an adjacency matrix based on knowledge graphs, dependency trees and dataset co-occurrences. Additionally, to capture opinion words more comprehensively, we propose a probabilistic relative position embedding strategy. This strategy assumes that the semantic distribution of tokens at varying distances from the central word obeys a gaussian distribution and constrains the distance between words and aspects within one standard deviation. Experimental results on five public datasets validate the effectiveness of our model.
A Multi-dimension and Multi-granularity Feature Fusion Method for Chinese Microblog Sentiment Classification

Chinese microblog comments exhibit a wide range of expressions, including brief terms and internet slang. Traditional single-feature models may not capturing this diversity comprehensively and in effectively discerning the usage of sentiment words, language structure, and contextual information. Consequently, they tend to exhibit suboptimal performance, particularly when dealing with informally expressed datasets in this domain. This study introduces a novel approach for Chinese microblog sentiment classification that leverages multi-dimensional (common text features and sentiment features) and multi-granularity (word-level and character-level) sentiment features. To achieve this, we first employ word2vec to train separate word-level and character-level embedding vectors. And then, we introduce LSTM (Long Short-Term Memory) neural networks to capture sentence-level representations. Additionally, self-attention mechanisms are incorporated to assess the importance of sentiment words at both word and character levels, finally integrating them into the LSTM network to capture the sentimental features that incorporate contextual semantics. We conduct experiments on two different datasets. Our experimental results reveal that our method performs comparably to competing approaches on high-quality datasets. However, on lower-quality datasets, it outperforms other methods, demonstrating the robustness of our proposed approach. Our innovative method provides an effective solution to address the challenges of Chinese microblog sentiment classification, with wide-ranging potential for practical applications.
Missing Mark Compound Sentence Relationship Recognition Based on ERNIE-BiLSTM

Relation identification of complex sentences is the core issue in the research of Chinese complex sentences. Its main goal is to distinguish the types of semantic relationships between sentences, especially in the absence of explicit connectives. In order to improve the accuracy of identifying missing-marked complex sentence relationships and lay the foundation for subsequent Chinese discourse processing tasks.This study proposes a network structure based on the large-scale pre-training model ERNIE and bidirectional long short-term memory. This method first uses the ERNIE pre-trained model to obtain word vectors, and then inputs them into Bi-LSTM to effectively extract the semantic information in the sentence. Next, by introducing a self-attention mechanism, weighted representations are generated to better capture the semantic relationships between sentences by taking into account the dependencies at different positions. Compared to other experimental findings from the Chinese Discourse Tree Bank (CDTB), this approach demonstrated notable performance, yielding an accuracy of 81.77%, reflecting a significant increase of approximately 6.07% compared to the prior approach. Moreover, the average macro F1 score stands at 81.81%, manifesting an enhancement rate of around 7.41%.
A Novel Ranking Method for Textual Adversarial Attack

Adversarial examples highlight the potential weaknesses in text classifiers, and can be used to improve the robustness of classifiers. Previous attack models which use pre-trained language models to generate context-aware adversarial examples typically solely adopt gold label probability to find vulnerable words to be perturbed, resulting in the waste of other labels probabilities and inefficient attack methods. This paper proposes a novel textual adversarial attack model, which leverage all label probabilities to find vulnerable words, and generate the substitute words of vulnerable words by utilizing Roberta. Extensive experiments demonstrate that our model outperforms the previous best model in terms of attack success, textual similarity and perplexity.
CAPPST:Chinese AMR Parsing with Parameter-efficient Fine-tuned Pre-trained Language Model for Particular Sentence Types

As a semantic representation, Abstract Meaning Representation (AMR) is closely related to grammar. CAMR parsing, a Chinese sentences parsing task, aims to extract the core semantics of sentences from syntactic facts. Parameter-Efficient Fine-Tuning (PEFT) is a cost-effective method and it has been shown effective in improving the parsing model’s performance. However, existing research directly applies PEFT without considering grammatical perspective. In this paper, we train different LoRAs and combine them into parsing model to address this issue, then propose a Chinese AMR Parsing model for Particular Sentence Types (CAPPST). This model can individually capture the semantic features of the interrogative, exclamation and imperative sentences. Our experiments show that CAPPST improves the baseline model’s Align-Smatch by 0.10, 0.28 and 7.19 respectively, and it also improves the Align-Smatch by 0.23 for the other type of sentences. By assembling the results of all our particular sentence parsing, the Align-Smatch of our model is 0.22 higher than the baseline model. We enhance the overall parsing performance of the model.
Implicit Chinese Binary Compound Sentence Relation Recognition Based on DERIA

The relationship of a complex sentence refers to the logical connection and semantic relationship between clauses in the sentence. Implicit complex sentences have certain relationships between clauses, but lack obvious conjunctions, requiring inference and identification through retrieval of other semantic information between the clauses. Therefore, the identification of complex sentence relationships is a challenging problem in the field of natural language processing. This paper proposes a model based on deep enhanced representation for recognizing the relationships between implicit binary clauses. The model utilizes the BERT model to transform the input sequence into word vectors, which are then fed into a multi-layer encoder module to capture the contextual features between sentences. This module is composed of stacked Bi-LSTM layers, with residual connections between each layer. In addition to being fed into the next layer encoder, the output of each layer also enters an interaction attention layer, aiming to better capture the relevance between two arguments. The outputs of the interaction attention layers are concatenated and then passed through a fully connected layer and softmax layer to perform the task of identifying implicit complex sentence relationships. The model was tested on a dataset extracted from the Chinese Complex Sentence Corpus (CCCS) and the Chinese Discourse Treebank (CDTB), achieving a highest accuracy of 79.34% and a highest macro-average F1 value of 78.77%, indicating the effectiveness of the method proposed in this paper.
Cancer Survival Prediction by Multimodal Disentangled Representation Learning

Precise prediction of cancer survival is of paramount importance in aiding clinicians in formulating tailored treatment strategies. Such strategies have the potential to enhance the quality of life for individuals with cancer and lower the mortality rates associated with this disease. Recent research has underscored the significance of integrating data from various sources, including genomics and histopathologic images, as a pivotal step towards enhancing the accuracy of cancer survival prediction. Although these studies have achieved promising results, utilizing the consensus and complementarity of multimodal data for efficient learning of comprehensive multimodal representations poses an enormous challenge. In response to this challenge, we propose a Multimodal Disentangled Representation Learning (MMDR) method, aimed at acquiring modality-specific and shared representations, thus affording a comprehensive perspective of multimodal data. Empirical findings confirm that this approach yields notable enhancements in performance when compared to alternative methodologies.
Clustering Social Media Data for Bitcoin Price Prediction with Transformer Model

This paper explores the integration of social media data and natural language processing methods, specifically utilizing the Transformer model, to predict Bitcoin price movements. We aim to evaluate the effectiveness of using social media data and the Transformer model in forecasting market trends for Bitcoin. By analyzing social media posts and incorporating them into predictive models, we demonstrate the potential of the Transformer architecture in capturing complex dependencies and patterns within sequential Bitcoin prices. Additionally, different clustering methods are applied to process the original social media data in a rolling manner. The evaluation of Transformer-based models on historical data showcases their predictive performance compared to various social media data clustering approaches. Furthermore, the impact of incorporating outliers of social media data into the Transformer model is explored to improve prediction accuracy. The results of this study demonstrate the potential of clustering on social media data and the Transformer model for forecasting market trends.1
Introduction

To develop pattern recognition and vision method for medical images has become one of the most challenging tasks in view of practical as well as industrial interest.
Evaluation of geological structures and geothermal resources in the North Tanzania Volcanic area using remote sensing and gravity data analysis

Problems Statement and Purpose. Northern Tanzania Volcanic terrain has been a subject of evaluation for geothermal potential in the last four decades. The region is characterized by Neogene to Recent volcanic and tectonic activities. This preliminary study based on remote sensing, water chemistry, gravity data, geological structures and volcanic centers distribution reports the geothermal manifestations identified and discusses the implications on geothermal fluid pathways. Oxygen-hydrogen isotope data from water samples indicate that there were involved in the hydrothermal system. Tectono-Volcanic Structures. The Northern Tanzania Divergence (NTD) area characterized by Neogene to Recent volcanic and tectonic activities. Recent volcanic and tectonic activities are ash cone and lava dome eruption at the floor of Meru crater a century ago, dyke intrusion and volcanic eruption south of Gelai volcano, and Oldoinyo-Lengai volcano, respectively. Fumarolic activities and hot springs are dominant in a relatively young volcanic area to the north-eastern and northern part of the NTD. Data and Methods. Shuttle Radar Topography Mission (SRTM), Landsat 8 Operational Land Imager (OLI) image, water isotope analysis and gravity data were used to extract and analyze the surface and subsurface geological lineaments and map the hydrothermal alteration zones in the study area. The hydrothermal alteration is used to evaluate and identify the permeable structures. Analysis and interpretation of the length and trends of extracted lineaments were used to investigate the tectonic evolution. Geological map of a study area was digitized from the existing geological maps and the age of rocks to delineate volcanic activity and associated lineaments based on the age of the lithological domain. Digital image processing was applied to enhance the visual interpretation. Gravity data were used to give insight into the subsurface structure in the study area. Results and Discussion. The higher δ 18O values and large deviation from meteoric water lines suggest that is due to the interaction of fluids with host rocks at elevated temperatures. These are consistent with open structures that act as conduits for fluid flow. The potential field gravity data reveal a basin-like structure trending in the NNW direction. The gravity data show that the basement units gradually deepen towards the central part and that it is controlled by two main fault systems that trend N-S and NW-SE respectively. The gravity data presented here provides new constraints on the tectonic evolution and geothermal resources of the study area.
The Influence of Steganographic Methods and QR Code Resolutions on Data Hiding in Cover Images

Abstract This work deals with digital image processing using the steganography technique in real life. Various steganography techniques face data authentication, data security, and copyright issues. Due to the fact that the problem of data security is currently very topical, these techniques are often sought after. This is related to the constant development of new algorithms that provide solutions that will safely hide the secret message in the image and at the same time will be resistant to various types of attacks. The motivation for writing this work lies in the technique of representing a secret message using a Quick Response code. Later in this work is comparison between our results and results of approach made by scientific community, which works with data hiding within steganographic principles. The secret message is written in the least significant bit plane of the cover image, which ensures that the modification of the image cannot be detected by the naked eye. The proposed solution is evaluated by mathematical quality assessment parameters Peak signal-to-noise ratio, Mean squared error, Normalized cross-correlation and Structural similarity index.
Community-Driven Moderation in Blockchain-Based Social Media

Decentralized blockchain-based social media (BSM) applications require different moderation strategies that suit the business models. Conventional social media uses centralized universal moderation strategies, which are human-extensive and time-consuming. Apart from that shortcomings, the absence of centralized authorities pushes the need for effective and efficient decentralized moderation strategies. This study proposed a novel community-driven moderation technique for decentralized blockchain-based social media. The proposed technique manage and review user-generated content (UGC) and activity directly at the moment of action. Performance evaluation of the proposed technique is crucial for feasible implementation in blockchain-based social media applications. The community-driven moderation technique on speed, cost, and user satisfaction is tested on the Polygon blockchain. The results indicate the cost of between 51,112 and 719,999 gas units. The average response time is 5.083 seconds, and the average gas fee is 4.2E-8 Matic. End User Computing Satisfaction (EUCS) Questionnaires on the proposed technique achieved high satisfaction scores for content and accuracy and low satisfaction scores for ease of use.
Forecasting of daily global solar radiation in Dumaguete, Philippines using NARX-LSTM Hybrid Network

Forecasting models are used to produce Energy Planning Models (EPMs) in developing renewable energy farms. One such application is the forecasting of daily global solar radiation, which is the energy being converted into PV power. In this study, results show that Gaussian process regression (GPR) and nonlinear autoregressive network with exogenous inputs (NARX) are equally proficient forecasting models for daily global solar radiation in Dumaguete, Philippines using only a relatively small dataset. A long short-term memory (LSTM) network was also trained to forecast global solar radiation. Combining the two produced a NARX-LSTM hybrid with a root mean square error (RMSE) of approximately 0.10. The global solar radiation forecast from this study could be used in calculating the amount of energy the PV cells from solar energy farms can harness in a day. Furthermore, the forecasting model can be used in the further development and improvement of energy planning models.
Improve word segmentation performance from unknown language by decreasing meaningless segmentation

This study discussed text analysis for the preservation of minority languages. Text analysis consists of some steps for the analysis of parsing, etc., but word segmentation is necessary before their advanced analysis. We then focused on word segmentation as the first step in the text analysis. For word segmentation of minority languages without prior knowledge, we considered NPYLM effective, which is an unsupervised method. However, NPYLM caused meaningless segmentation if the training texts were insufficient. Meaningless segmentation is an error where a word is segmented into pieces. In this article, we proposed a method to improve meaningless segmentation. The basic idea of the proposal is that meaningless segmentation is caused by the inappropriate or insufficient growth of words, and we can control the growth of words by selecting words for replacement. We performed simple experiments. The results showed that the proportion of correct words in the obtained word groups is improved: vanilla NPYLM gives about 25%, and the proposed method gives about 50%. Thus, the proposed method suppresses meaningless segmentation.
On Model Performance Estimation in Time Series Anomaly Detection

The usual way to quantify the performance of a novel algorithm in the field of classification, especially in time series anomaly detection, is to compare its performance against selected baseline competitors on selected data sets. There is a common sense in the community which data sets and baselines should be considered when evaluating the algorithm’s performance. Nevertheless, on which basis data sets and baselines should be selected is frequently discussed. In this paper, we propose an index for univariate time series data in anomaly detection based on information theory. The index shows an association with the AUC-score of an anomaly detection algorithm that is trained on the data, meaning that, the index can be used as a proxy for the “difficulty” for the classification task, this data set holds. A workflow to quantify this association using an interpretable classifier that relies on the index and a derived performance baseline is developed. The classifier performs within the margin of error of this performance baseline, meaning that we were not able to clearly mathematically show the association between index and AUC-score. We believe that our work, which unites mathematical concepts from information theory, physics, and computer science, is innovative and generally points in a promising direction that is worth investigating.
Multiple Garbage Detection using Mask R-CNN in TensorFlow for Household Waste Classification

At the moment, 2.01 billion tons of solid trash are produced annually in all cities throughout the world. Based on research findings, a typical person generates approximately 0.74 kilograms of garbage on a daily basis. The researchers decided to help the community by providing information on household waste detection and classification. By means of implementing the Mask R-CNN framework, it became feasible to obtain a segmented mask as well as a bounding box for every identified household waste item in a photo. Bounding boxes were used to identify multiple wastes, things with different scales, and objects that overlap in an image. Bounding boxes were made of various household wastes. The wastes were classified into biodegradable, non-biodegradable, and recyclable which has the potential to mitigate human labor and expedite the waste segregation process. The Mask R-CNN model was used to improve detection and demonstrate how household trash may be appropriately identified in a photo with several objects. The model for detecting objects encountered challenges in detecting household wastes caused by their deformity, cluttered and occluded environment, and poor lighting conditions. The Mask R-CNN model produced a 38.25% mean average precision, 65.50% mean average recall, and 48.29% F1 score. The proposed model can be utilized in detecting and classifying household wastes.
Sentiment Analysis using a Long Short-Term Memory Recurrent Neural Network on Filipino Tweets: A Case Study for Bank A

Social media has emerged as a significant source of big data, offering a vast amount of social data and evidence. However, much of this data remains untapped and underutilized. Nowadays, various organizations, including corporations, government agencies, and data enthusiasts, are actively seeking to extract and leverage this data to create data models that drive actionable insights, helping them achieve their goals. This study focuses on examining customer sentiments towards Bank A, one of the largest banking and financial institutions in the Philippines with a history spanning over 50 years. By employing natural language processing techniques, we aimed to gain a comprehensive understanding of the general sentiment towards the bank's products, services, and overall brand satisfaction. To accomplish this, we annotated the sentiments and utilized them as training and testing data for a Long Short-Term Memory (LSTM) Recurrent Neural Network (RNN). This model was designed to classify tweets in both English and Filipino languages into two polarities and demonstrated an accuracy rate of 88.50%.
Applying Larger N-Tuple Networks to EinStein Wurfelt Nicht!

EinStein würfelt nicht! (abbr. EWN) is a stochastic game with a die rolled in each ply which makes it full of uncertainty. It does not look easy to design a good game engine using traditional alpha-beta-based approaches. In this paper, an EWN game engine is formed by combining n-tuple networks, trained by features extracted from two billion random games, with Monte Carlo Tree Search. Similar work has been proposed previously, but in this work, we compare performances on versions with n = 4, 6 and 9. We discover that 4-tuple networks add no extra strength in comparison with a plain baseline. However, a larger n does bring extra benefit. The jump of performance is noticeable from n = 4 to n = 6 and is very visible from n = 6 to n = 9. The tuple’s size n increases from 4 and 6 to 9. When n = 9, the size of the feature set is much larger than before and cannot be stored plainly in the main memory. One of our main contributions is to come out with a good encoding design taking care of various symmetry or equivalence properties so that it can be used in real-time game playing. Using our scheme, the number of distinct features is reduced to 20,244,124,980, which is 21.21% less than the original 95,440,494,357 features for 9-tuple networks.