Reviewerly/exHarmony huggingface dataset:
Soil organic matter content prediction based on two-branch convolutional neural network combining image and spectral features

Authors:
raw_author_name: Yongyan Cao
raw_affiliation_string: Key Laboratory of Smart Agriculture System, Ministry of Education, China Agricultural University, Beijing 100083, China
raw_author_name: Wei Yang
raw_affiliation_string: Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China
raw_author_name: Minzan Li
raw_affiliation_string: Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China

Abstract: Soil organic matter (SOM) is the main source of soil nutrients. Rapid determination of SOM content is of great significance for guiding field management. The change of SOM content will change the physical properties of soil, such as color and porosity, and then lead to obvious differences in soil reflectance in the visible and near infrared range. In this study, 417 topsoil (5–15 cm) samples were collected from five experimental fields in North China, and spectral data in the range of 350–1700nm and image data of sampling points were obtained. Multi-CNN network was established to realize the feature fusion of image and spectrum based on two-branch CNN to predict SOM content. Firstly, the spectral feature extraction module based on the improved CNN was constructed, and the spectral information from 768 to 140 bands was compressed by using Auto Encoder, and the feature was extracted by CNN. The feature extraction of the soil image is realized by the ResNet50 network, and finally the feature information of the image and the spectrum is fused by the bilinear pooling fusion module in the fully connected layer to complete the SOM content prediction and output the results. After experimental verification, BLMulti CNN had the highest accuracy in predicting organic matter models, in which the prediction set R2 is 0.910, 0.017 higher than that of the Multi-CNN based on concat, 0.075 higher than that of the prediction method of AE-CNN and 0.10 higher than that of the image prediction model based on ResNet50. The BLMulti-CNN effectively improves the prediction accuracy of the model and provides technical guidance for the rapid detection of SOM content in the field.
The Concordance Index decomposition: A measure for a deeper understanding of survival prediction models

Authors:
raw_author_name: Abdallah Alabdallah
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden
raw_author_name: Mattias Ohlsson
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden; Department of Astronomy and Theoretical Physics, Lund University, Sweden
raw_author_name: Sepideh Pashami
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden; RISE Research Institutes of Sweden, Stockholm, Sweden
raw_author_name: Thorsteinn Rögnvaldsson
raw_affiliation_string: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Sweden

Abstract: The Concordance Index (C-index) is a commonly used metric in Survival Analysis for evaluating the performance of a prediction model. In this paper, we propose a decomposition of the C-index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition enables a finer-grained analysis of the relative strengths and weaknesses between different survival prediction methods. The usefulness of this decomposition is demonstrated through benchmark comparisons against classical models and state-of-the-art methods, together with the new variational generative neural-network-based method (SurVED) proposed in this paper. The performance of the models is assessed using four publicly available datasets with varying levels of censoring. Using the C-index decomposition and synthetic censoring, the analysis shows that deep learning models utilize the observed events more effectively than other models. This allows them to keep a stable C-index in different censoring levels. In contrast to such deep learning methods, classical machine learning models deteriorate when the censoring level decreases due to their inability to improve on ranking the events versus other events.
Adaptive learning on hierarchical data streams using window-weighted Gaussian probabilities

Authors:
raw_author_name: Eduardo Tieppo
raw_affiliation_string: Instituto Federal do Paraná (IFPR), Pinhais, Brazil; Programa de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná, Curitiba, Brazil
raw_author_name: Júlio Cesar Nievola
raw_affiliation_string: Programa de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná, Curitiba, Brazil
raw_author_name: Jean Paul Barddal
raw_affiliation_string: Programa de Pós-Graduação em Informática (PPGIa), Pontifícia Universidade Católica do Paraná, Curitiba, Brazil

Abstract: The hierarchical data stream classification task addresses challenges in both hierarchical and data stream classification primary areas. In these scenarios, machine learning models must simultaneously deal with class hierarchies and adapt to respond to nonstationary data. Given such a challenging set of traits, existing techniques are deficient, as they perform incremental learning and are slow to adapt to newer data, thus not capturing their dynamics in a timely fashion. In this study, we propose two novel adaptive Gaussian Naive Bayes classifiers tailored to classify hierarchical data streams. The models use window-weighted Gaussian probabilities to consider current and historical data and improve the adaptability of the classifiers, especially for nonstationary data streams. As a result of our research, we introduce a unified protocol for evaluating and comparing hierarchical data stream classifiers and establish a benchmark for the hierarchical data stream classification task encompassing the proposed methods and state-of-the-art classifiers. The results demonstrate that our proposed algorithms achieve better prediction correctness than their state-of-the-art counterparts while responding more swiftly to changes in data distribution.
CBReT: A Cluster-Based Resampling Technique for dealing with imbalanced data in code smell prediction

Authors:
raw_author_name: Praveen Singh Thakur
raw_affiliation_string: Department of CSE, MNIT Jaipur, 302017, India
raw_author_name: Mahipal Jadeja
raw_affiliation_string: Department of CSE, MNIT Jaipur, 302017, India
raw_author_name: Satyendra Singh Chouhan
raw_affiliation_string: Department of CSE, MNIT Jaipur, 302017, India

Abstract: Code smell refers to substandard design patterns in software's source code that may lead to faults-prone implementation. Machine learning-based code smell prediction models suffer from data imbalance problems, i.e., one class contains significantly more instances than another. The existing oversampling approaches, such as SMOTE (Synthetic Minority Over-sampling Technique), have been used for balancing the code smell dataset by generating synthetic samples for the minority class. However, the distribution of classes of code smell datasets is overlapped; hence, randomly generated instances can damage the decision boundary between both classes. This paper addresses this issue and proposes a novel Cluster-Based Resampling Technique, CBReT, that generates synthetic instances by considering the distribution of the code smell data. The CBReT first formulates clusters (containing minority and majority instances) based on the data distribution using Gaussian Mixture Model (GMM). Next, each cluster is balanced separately by synthesizing minority instances. While balancing the clusters, the CBReT also checks the validity of the synthetic instances so that each synthetic instance holds similar properties as the other minority instances. To assess the performance of CBReT, extensive experiments have been conducted on the four publicly available benchmark code smell datasets. We have used various performance metrics to evaluate our model's performance. The experimental results show that the CBReT technique significantly increased the performance of the code smell prediction model by 0.18% (min) and 9.08% (max) compared to the state-of-the-art imbalance learning approaches.
Mineral prediction based on prototype learning

Authors:
raw_author_name: Liang Ding
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, China
raw_author_name: Yuelong Zhu
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, China
raw_author_name: Hai Dong
raw_affiliation_string: School of Computing Technologies, RMIT University, Melbourne, Australia
raw_author_name: Pengcheng Zhang
raw_affiliation_string: College of Computer and Information, Hohai University, Nanjing, China

Abstract: In the field of mineral resource prediction, acquiring labeled data and bearing high annotation costs pose significant challenges. Moreover, distinct characteristics are present in different types of data, including geophysical, geochemical, and geological data. However, conventional deep learning methods often treat all data uniformly, neglecting the specificities inherent in various domains of knowledge. To address these issues, we propose Geo-Meta, an optimized prototype learning model that combines prototype learning, metric learning, and meta-learning strategies to classify multi-source geological samples. Geo-Meta utilizes tailored network architectures for different data types to fuse features and compute prototypes in the metric space. Additionally, it employs a generative model to identify subtle anomaly features and incorporates a semi-supervised algorithm based on label propagation to refine the initial prototypes, enhancing their robustness and category-awareness. Furthermore, Geo-Meta incorporates a dynamic distance metric module to quantify the similarity between sample features and class prototypes, facilitating effective category partitioning. Extensive experimentation demonstrates that our proposed model achieves remarkable performance. It attains an impressive Area Under the Receiver Operator Curve (AUC) of 98% without data augmentation, using a significantly limited number of samples. Moreover, it accurately identifies approximately 93% of ore deposits within only 7% of the study area, surpassing similar models in both accuracy and efficiency. The mineral resource prediction map generated by Geo-Meta aligns with the spatial distribution of mineral resources and adheres to geological knowledge, providing valuable technical support for decision-making in target area exploration. Our proposed model enables the adaptation of deep learning algorithms to geological big data, offering substantial potential for cost and time reduction in mineral prediction. Thus, it holds great relevance and practicality for the mining industry.
Performance improvement in reservoir computing by using HfZrO<sub>2</sub> FeFETs through operating voltage optimization

Authors:
raw_author_name: Shin-Yi Min
raw_affiliation_string: Department of Electrical Engineering and Information Systems, School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
raw_author_name: Kasidit Toprasertpong
raw_affiliation_string: Department of Electrical Engineering and Information Systems, School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
raw_author_name: Eishin Nako
raw_affiliation_string: Department of Electrical Engineering and Information Systems, School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
raw_author_name: Ryosho Nakane
raw_affiliation_string: Department of Electrical Engineering and Information Systems, School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
raw_author_name: Mitsuru TAKENAKA
raw_affiliation_string: Department of Electrical Engineering and Information Systems, School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan
raw_author_name: Shinichi TAKAGI
raw_affiliation_string: Department of Electrical Engineering and Information Systems, School of Engineering, The University of Tokyo, Bunkyo, Tokyo 113-8656, Japan

Abstract: Abstract We have investigated how the parameters of an input gate voltage ( V g ) waveform and a drain voltage ( V d ) impact the performance of reservoir computing (RC) using a Hf 0.5 Zr 0.5 O 2 ferroelectric FET (FeFET). The RC performance is maximized by the high swing amplitude of the V g and the most symmetrical polarization switching condition in the triangular-shaped input waveform, obtained by the center V g of 0.5 V, because of the enhanced polarization switching of the FeFETs. Regarding the V d dependence, the amount of the drain current and polarization switching have a trade-off relationship. As a result, a moderate V d of 1.0 V becomes optimum in terms of the RC performance because a difference in drain current responses between different gate input patterns is maximized with this V d . Furthermore, high computing capacities are achieved by combining the above optimal bias condition with drain current responses to both original and inverted gate input patterns.
Object aroused emotion analysis network for image sentiment analysis

Authors:
raw_author_name: Jing Zhang
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
raw_author_name: Jiangpei Liu
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
raw_author_name: Weichao Ding
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China
raw_author_name: Zhe Wang
raw_affiliation_string: Department of Computer Science and Engineering, East China University of Science and Technology, Shanghai, China

Abstract: From cognitive psychology, objects are closely related to emotions, and inherently possess the ability to arouse human emotions. Hence, fully utilizing the relationships between objects and emotions can help achieve more accurate visual emotion recognition. In this paper, we propose a novel object aroused emotion analysis network to realize image sentiment classification by investigating the interactions between objects and emotions. To quantify the various emotion potencies of each object, a novel object emotion distribution module is proposed to explore the mapping among objects and emotions, and quantitatively demonstrate how various objects arouse different emotions. An object emotion-modeling mapping module is proposed to analyze the effect of objects and object combinations with emotion information on image sentiment; this module maps common objects to emotion dimensions and improves image sentiment classification with abundant object combination information. Then, we fuse the object-emotion mapping relation and multi-model features using BiGRU, thus realizing more accurate emotion recognition. Extensive experiments on widely used emotion datasets prove that our proposed method achieves excellent performance and outperforms most state-of-the-art image sentiment classification methods.
Artificial Intelligence (AI) in Tourism

Authors:
raw_author_name: Seden Doğan

Abstract: Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.
Estimating the structural diversity introduced by decision forest algorithms : A probabilistic approach

Authors:
raw_author_name: Ryan H.L. Ip
raw_affiliation_string: School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2650, Australia
raw_author_name: Michael Bewong
raw_affiliation_string: School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2650, Australia
raw_author_name: Md. Nasim Adnan
raw_affiliation_string: Department of Computer Science and Engineering, Jashore University of Science and Technology (JUST), Jashore 7408, Bangladesh
raw_author_name: Md. Zahidul Islam
raw_affiliation_string: School of Computing, Mathematics and Engineering, Charles Sturt University, Bathurst, NSW 2795, Australia

Abstract: Structurally diverse decision trees are important for knowledge discovery and classification/prediction accuracy. Over the years, researchers have devoted much effort to the development of algorithms to increase diversity among the trees within an ensemble. While Kappa is commonly used to measure diversity among the decision trees, it does not measure the ability of the tree building algorithms to introduce diversity. Further, Kappa does not consider the structural diversity amongst the trees. Instead, Kappa measures the diversity of the predictions made from the trees produced, and are dependent on the datasets used. This paper presents a novel data-independent metric, called R index, for measuring the diversity that can be introduced by a decision forest algorithm without building the entire decision forest. The proposed measure is applied to five well-known algorithms that involve bagging and random subspacing. An efficient practical approach for calculating the R index empirically - R finder - is also proposed, and is implemented. Both R finder and Kappa were applied to thirty-two publicly available benchmark datasets under various algorithms to estimate the resulting diversity. The results indicate a generally strong negative correlation between R finder and Kappa, implying that R finder is effective at estimating the diversity of trees without the added computational costs associated with calculating Kappa.
Temporal ECDSA: A timestamp and signature mask enabled ECDSA algorithm for IoT client node authentication

Authors:
raw_author_name: Shelbi Joseph
raw_affiliation_string: School of Engineering, Cochin University of Science and Technology, Kochi, 682022, Kerala, India
raw_author_name: Bharat Bhushan
raw_affiliation_string: School of Engineering and Technology, Sharda University, Greater Noida, 201310, Uttar Pradesh, India

Abstract: Internet of Things (IoT) networks are vulnerable to various security threats, especially in client authentication. The current authentication methods require significant resources and are vulnerable to specific attacks. The Non-Interactive Zero Knowledge Proof (NIZKP) concept suits IoT device authentication. Elliptic Curve Digital Signature (ECDSA) is a popular algorithm for IoT device authentication based on elliptic curve cryptography (ECC) and NIZKP. However, an intelligent attacker who captures the ECDSA signatures generated by the same random number can recover the private key. This paper proposes a timestamp based approach to prevent signature reuse and fake signature generation in the ECDSA algorithm. The signature proof is generated differently at each occurrence to prevent the generation of valid signatures from leaked private keys. The proposed approach eliminates the expensive modular inversion operations in signature generation and verification phases, enhancing execution efficiency. The analysis results indicate that the proposed Temporal ECDSA algorithm effectively prevents most attacks on client authentication in IoT networks. Various metrics, including computational complexity and security measures, were used to evaluate the effectiveness of the proposed approach, demonstrating that it outperforms most ECDSA variants.
Classification of direct optical signal inputs by Ag<sub>2</sub>S island network reservoir

Authors:
raw_author_name: Tsuyoshi HASEGAWA
raw_affiliation_string: Department of Pure and Applied Physics, Graduate School of Advanced Science and Engineering, Waseda University, 3-4-1 Okubo, Shinjuku-ku, Tokyo 169-8555, Japan

Abstract: Abstract We have reported that a physical reservoir with a silver sulfide island network can classify simple patterns of an irradiated light without converting it to a voltage signal input. In this study, we conducted experiments to verify whether the detection of dynamical change in an irradiating light, e.g., moving in a reservoir layer, can be available. We also investigated the possibility that the reservoir could detect a position of light exposure, in addition to the dependence on the wavelength and the exposure time. The technique was applied to a task of whether character-shaped light patterns could be recognized even if the irradiated position was changed.