Reviewerly/exHarmony huggingface dataset:
A Graph Neural Network with Context Filtering and Feature Correction for Conversational Emotion Recognition

Authors:
raw_author_name: Chenquan Gan
raw_affiliation_string: School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
raw_author_name: Qingyi Zhu
raw_affiliation_string: School of Cyber Security and Information Law, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
raw_author_name: Deepak Kumar Jain
raw_affiliation_string: Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education, Dalian University of Technology, Dalian, 116024, China
raw_author_name: Vitomir Štruc
raw_affiliation_string: Faculty of Electrical Engineering, University of Ljubljana, Trzaska cesta 25, SI-1000 Ljubljana, Slovenia

Abstract: Conversational emotion recognition represents an important machine-learning problem with a wide variety of deployment possibilities. The key challenge in this area is how to properly capture the key conversational aspects that facilitate reliable emotion recognition, including utterance semantics, temporal order, informative contextual cues, speaker interactions as well as other relevant factors. In this paper, we present a novel Graph Neural Network approach for conversational emotion recognition at the utterance level. Our method addresses the outlined challenges and represents conversations in the form of graph structures that naturally encode temporal order, speaker dependencies, and even long-distance context. To efficiently capture the semantic content of the conversations, we leverage the zero-shot feature-extraction capabilities of pre-trained large-scale language models and then integrate two key contributions into the graph neural network to ensure competitive recognition results. The first is a novel context filter that establishes meaningful utterance dependencies for the graph construction procedure and removes low-relevance and uninformative utterances from being used as a source of contextual information for the recognition task. The second contribution is a feature-correction procedure that adjusts the information content in the generated feature representations through a gating mechanism to improve their discriminative power and reduce emotion-prediction errors. We conduct extensive experiments on four commonly used conversational datasets, i.e., IEMOCAP, MELD, Dailydialog, and EmoryNLP, to demonstrate the capabilities of the developed graph neural network with context filtering and error-correction capabilities. The results of the experiments point to highly promising performance, especially when compared to state-of-the-art competitors from the literature.
Topological equivalence classification and enumeration of n-input linearly separable Boolean functions

Authors:
raw_author_name: Qinbin He
raw_affiliation_string: School of Electronics and Information Engineering, Taizhou University, Taizhou, 318000, PR China
raw_author_name: Fangyue Chen
raw_affiliation_string: School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, PR China

Abstract: A perceptron, as an “artificial neuron”, plays a crucial role in the research of neural networks. As a mathematical model of linearly separable Boolean functions, perceptron neurons can realize any nonlinearly separable Boolean function. Linearly separable Boolean functions can be divided into a smaller number of the same functional classes. These functional classes are equivalent to the core basic units (AND, NOT, OR, and other components) that make up the circuit. Therefore, the research on the classification problem of linearly separable Boolean functions is very important. In this study, a novel algorithm, called a topological equivalence classification algorithm, is proposed for classifying balanced linearly separable Boolean functions. By the proposed algorithm, the total number of the topological equivalence classes and the number of balanced linearly separable Boolean functions in each of the topological equivalence classes are obtained. According to the one-to-one correspondence between n-input balanced linearly separable Boolean functions and n-1-input linearly separable Boolean functions, we get the topological classification of linearly separable Boolean functions. As for n=9, the accurate results of classification and counting the number of linearly separable Boolean functions are obtained: There are 53,063,448 topological equivalence classes, and the total number of linearly separable Boolean functions is 144,380,202,286,068,288.
Applied geochemistry for the discrimination between mafic and ultramafic rocks in Cu-Ni-PGE-bearing layered complexes: A case study at the la Jovita-Las Águilas belt, Sierra Grande de San Luis, Argentina

Authors:
raw_author_name: María Luján Ganuza
raw_affiliation_string: Instituto de Ciencias e Ingeniería de la Computación (ICIC, UNS-CONICET), Departamento de Ciencias e Ingeniería de la Computación, Universidad Nacional del Sur, San Andrés 800, 8000, Bahía Blanca, Buenos Aires, Argentina

Abstract: The Virorco intrusive body is comprised in La Jovita-Las Águilas mafic-ultramafic belt, Sierra Grande de San Luis, Argentina. The ultramafic rocks of this belt carry a base metal sulphide mineralization and platinum group minerals. Macroscopic description of mafic and ultramafic rocks, done by the mining exploration companies, can not differentiate the mineral percentages. Therefore, considering the geochemical composition of the already classified mafic-ultramafic rocks of surface samples from the mentioned belt, we established compositional ranges of geochemical parameters (MgO, Al2O3, Na2O, Sr and Cr) for both mafic and ultramafic rocks. Linking the geochemical data of drill cores made by Castillian Resources Corp. company over the Virorco body, with the mentioned ranges, it was possible to discriminate between mafic and ultramafic rocks present in such drill cores. This provides a useful prospecting tool to identify the mafic and ultramafic units that carry the mineralization and to correlate mineralized units from the different drill cores. This method can be applied, not only on the bodies of the mentioned belt, but in other similar deposits elsewhere. The methodology proposed in the present contribution was validated by the Machine Learning algorithm Random Forrest with a 91% success rate.
Granular fuzzy rule-based model construction under the collaboration of multiple organizations

Authors:
raw_author_name: Bingsheng Liu
raw_affiliation_string: College of Management and Economics, Tianjin University, Tianjin 300072, China; School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
raw_author_name: Boyang Wang
raw_affiliation_string: College of Management and Economics, Tianjin University, Tianjin 300072, China
raw_author_name: Yinghua Shen
raw_affiliation_string: School of Economics and Business Administration, Chongqing University, Chongqing 400044, China
raw_author_name: Witold Pedrycz
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Alberta, Edmonton T6R 2V4, Canada

Abstract: In the real world, phenomena are often observed and recorded by multiple organizations which results in multiple sources of data. When dealing with such data, the centralized modeling approach aims to achieve collaborative modeling by fusing multiple sources of data into a single data set, which may pose challenges to data privacy. Unlike centralized modeling, the distributed modeling approach can effectively solve the privacy issue. However, modeling approaches based on this idea still suffer from either low prediction accuracy or high communication costs. In this study, we propose a collaborative modeling strategy for multi-source data based on fuzzy rule-based models (FRBMs) to balance the needs of both model prediction accuracy and efficiency. First, we adopt the concept and algorithm of collaborative fuzzy clustering (CFC) to improve prediction accuracy and reduce communication costs by improving the CFC algorithm. Then, we construct a granular FRBM for multi-source data based on the principle of justifiable granularity (PJG) by integrating local models into a more robust and perfect global model. Finally, we improve the performance evaluation index of the existing granular FRBM and propose two model optimization schemes to further improve the performance of the model. We conduct experiments on both synthetic and publicly available data sets to demonstrate the effectiveness of the proposed method.
A provably stable neural network Turing Machine with finite precision and time

Authors:
raw_author_name: Ankur Mali
raw_affiliation_string: Computer Science and Engineering, University of South Florida, Tampa, FL, 33620, USA
raw_author_name: C. Lee Giles
raw_affiliation_string: Information Sciences and Technology, Pennsylvania State University, University Park, PA, 16802, USA

Abstract: We introduce a neural stack architecture with a differentiable parameterized stack operator approximating stack push and pop operations. We prove the stability of this stack architecture for arbitrarily many stack operations, showing that the state of the neural stack still closely resembles the state of a discrete stack. Using the neural stack with a recurrent neural network, we devise a neural network Pushdown Automaton (nnPDA). A new theoretical bound shows that an nnPDA can recognize any PDA using only finite precision state neurons in finite time. By using two neural stacks to construct a neural tape together with a recurrent neural network, we define a neural network Turing Machine (nnTM). Just like the neural stack, we show these architectures are also stable. Furthermore, we show the nnTM is Turing complete. It requires finite precision state neurons with an arbitrary number of stack neurons to recognize any TM in finite time, thus providing a new and much stronger computational upper bound for neural networks that are Turing complete.
Enhancing logistic chaotic map for improved cryptographic security in random number generation

Authors:
raw_author_name: Moatsum Alawida
raw_affiliation_string: Department of Computer Sciences, Abu Dhabi University, Abu Dhabi 59911, United Arab Emirates

Abstract: The use of chaotic maps for cryptography is advantageous due to their desirable characteristics, including complexity, unpredictability, sensitivity to initial values and control parameters, and ergodicity. However, most chaos-based systems have limitations that make them impractical, such as low efficiency, insufficient security, and small keyspace. To overcome these challenges, this paper proposes an enhanced logistic map (ELM), a one-dimensional digital chaotic map based on the logistic map and anterior perturbation method. The ELM map leverages perturbations of chaotic states, leading to enhanced randomness, data distribution, and low correlation, with improved security properties. The ELM map is simple yet capable of generating large chaotic behavior. The findings show that the ELM map is highly secure and can be used in various cryptographic applications. A new pseudorandom number generator (PRNG) is also proposed based on a combination of ELM chaotic maps. The resulting PRNG is highly dependent on the chaotic properties of the maps and the combination rule. Statistical analysis shows that the proposed method is statistically secure, highlighting the potential of the ELM map for other cryptographic applications.
Identifying wind turbines from multiresolution and multibackground remote sensing imagery

Authors:
raw_author_name: Xuehong Chen
raw_affiliation_string: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
raw_author_name: Xin Cao
raw_affiliation_string: State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Abstract: The wind energy industry has expanded in recent years. Promotion of the Sustainable Development Goals (SDGs) is expected to further increase the scale of the wind energy industry. Determining the location and quantity of wind turbines is crucial for monitoring the development status of the wind energy industry and evaluating wind energy production. In this study, we propose a method for simultaneously detecting and positioning wind turbines in remote sensing images, namely, Wind Turbine YOLO (WT-YOLO), based on the You Only Look Once version 5 model (YOLOv5). The wind turbine hub, base, and shadow hub are treated as key points in the proposed method. Regression terms are incorporated into the head of the YOLOv5 basic framework to predict the location of these three key points. The base point is utilized to determine the exact position of the wind turbine. A multibackground and multiresolution wind-turbine image dataset is constructed by sampling high-resolution images from Google Earth. The WT-YOLO method outperforms existing methods in both wind turbine detection and positioning on different types of land cover backgrounds and multiresolution images of the constructed dataset. In the spatial resolution range of 0.6 m to 5.4 m, WT-YOLO exhibits enhanced wind-turbine detection, where the average precision (AP) is 5.92 % to 15.43 % higher than that of existing wind-turbine detection methods. Wind-turbine positioning by WT-YOLO has a mean distance error (MDE) that is 16.06 m to 21.59 m lower than that of existing wind-turbine positioning methods. A comparative analysis showed that the shadow and the three key points are effective features for wind-turbine detection. The proposed WT-YOLO model can support detection, positioning and counting for wind turbines worldwide.
Camera-aware re-identification feature for multi-target multi-camera tracking

Authors:
raw_author_name: Jinlong Zhu
raw_affiliation_string: Changchun Normal University, 130032 Changhun, China
raw_author_name: Changbo Gao
raw_affiliation_string: Changchun Normal University, 130032 Changhun, China
raw_author_name: Yu Ge
raw_affiliation_string: Changchun Normal University, 130032 Changhun, China
raw_author_name: Ke Xu
raw_affiliation_string: Changchun Normal University, 130032 Changhun, China

Abstract: Multi-target multi-camera tracking is an important research topic in intelligent surveillance to achieve re-identification (Re-ID) of moving targets across different cameras. However, Re-ID faces significant challenges owing to variations in cameras/viewpoints, making it difficult to learn discriminative feature representations for targets captured from different cameras/viewpoints. A new pipeline is introduced by balancing the across-cameras sample feature space in camera-aware Re-ID framework. Specifically, different sampling strategies play a crucial role on the performance under the same baseline and identify the feature discrepancy between cameras/viewpoints as a crucial factor. This proposed sampling strategy is called camera equalization sampling to learn enhanced feature disparities, which balances cameras under identity rather than randomly sampling in a batch. The sampled images are combined with non visual cues(cameras position encoding) to reduce intra class variance. For the mechanism of camera equalization sampling, the improved camera centric loss function can better reduce the negative impact of individual samples and provide stable learnable features.Our proposed method consists of three modules. (i) Camera equalization (CE) ensures that each batch collects at least one image from each camera for every identity, thereby enabling robust features. (ii) Camera position embedding (CPE) mitigates the scene bias caused by different cameras/viewpoints by encoding camera indices. (iii) Camera Center triplet loss(CCL) based on CE improves higher robustness to outliers and noisy labels. We demonstrated the proposed method's effectiveness on popular datasets such as DukeMTMC-reID, Market-1501, and MSMT17, achieving state-of-the-art performance.
Service application model design for users using fuzzy semantic ontology model in cloud computing

Authors:
raw_author_name: V. Arulkumar
raw_affiliation_string: Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, Tamilnadu, India
raw_author_name: Sini Anna Alex
raw_affiliation_string: Ramaiah Institute of Technology, Bangalore, Karnataka, India
raw_author_name: R. Lathamanju
raw_affiliation_string: SRM Institute of Science and Technology, Ramapuram, Chennai, TamilNadu, India

Abstract: In an era marked by the widespread adoption of cloud services, individuals and businesses face the daunting task of navigating a complex landscape to make informed choices. The inherent opacity of the cloud service environment underscores the need for methods that can effectively handle imprecise information. This research presents a novel and superior approach to aid customers in selecting the most suitable cloud services. Our work introduces a distinctive fuzzy decision-making paradigm, surpassing current methodologies. We leverage an innovative analytic hierarchy process technique to quantify the semantic similarity between concepts and employ a fuzzy ontology to elucidate the uncertain relationships among database items, facilitating precise service matching. Furthermore, we present a multi-faceted evaluation framework for ranking cloud services. To substantiate the efficacy of our similarity matching based on the fuzzy ontology, we conduct comprehensive testing. The results of our experiments provide compelling evidence of the viability and effectiveness of the proposed method. This research offers a valuable contribution to the challenging realm of cloud service selection, empowering individuals and organizations to make well-informed decisions amidst the cloud service abundance.
Multi-scale fusion public gathering recognition based on residual network

Authors:
raw_author_name: Yicheng Liu
raw_affiliation_string: School of Artificial Intelligence, Anhui University, Hefei, China
raw_author_name: Zewei Hu
raw_affiliation_string: Graduate School, Lyceum of the Philippines University, Manila, Philippines

Abstract: With the rapid economic development and high concentration of urban population, people’s income level and quality of life continue to improve, resulting in more and more crowded scenes caused by people going out. Especially in urban commercial centers, transportation hubs, sports venues during important events, tourist attractions, etc., crowd gatherings occur frequently. However, accidents involving crowd gatherings in public places occur frequently, causing heavy casualties and property losses. Therefore, for crowd recognition, this paper proposes a new method to accurately estimate the number of dense crowds. In this method, a density map with accurate pedestrian locations is first generated using the focal inverse distance transform and used as ground truth labels for network training. Then, a multi-scale feature fusion algorithm based on residual network is designed, combining spatial and channel attention mechanisms to improve the accuracy and stability of crowd density estimation. In dense crowds, the phenomenon of overlapping and occlusion of people is very common and serious, making it difficult for existing pedestrian detection methods to distinguish each individual and accurately count the flow of people. To solve this problem, this paper proposes a density map-based method that uses a local maximum detection strategy and a K-nearest neighbor algorithm to convert the density map into the corresponding dense head bounding box. This method can effectively reduce the impact of occlusion and improve the accuracy of people counting. In order to further improve the estimation accuracy, a pattern recognition density peak clustering algorithm is introduced to study the clustered crowds. By treating the head bounding box as an element point, the distance between each element point is calculated, and the density of each point is calculated. Then perform clustering to find the cluster center with the highest density in each class. Finally, by comparing the density of each cluster center with the corresponding density threshold and adopting the corresponding decision-making method, the accuracy of people counting is further improved.
GDJN: Global density joined network for abnormal event detection in surveillance videos

Authors:
raw_author_name: T.S. Sivarani
raw_affiliation_string: Department of Electrical and Electronics Engineering, Arunachala College of Engineering for Women, Vellichanthai, Tamilnadu, India

Abstract: Detecting abnormal events in surveillance involves identifying unexpected behavior through video analysis. This involves recognizing patterns or deviations from normal behavior and taking actions to mitigate potential risks. However, the distribution of data can change over time, leading to concept drift, which can make it challenging to accurately detect abnormal events. To address this issue, a new approach using a global density network (GDN) has been proposed. The GDN allows for more efficient identification of object distributions in surveillance videos, leading to improved accuracy in abnormal event detection. The proposed method combines features extracted by a backbone network with a global density joined network (GDJN), which refines density features using dilated convolutional networks. A multistage long short-term memory (LSTM) network is then used to classify abnormal events. The experimental results are conducted on two datasets, UMN and UCSD Ped2. The achieved F1 scores were 93.42 and 94.46 respectively, with corresponding AUC values of 93.5 and 94.8.
Evolutionary U-Net for lung cancer segmentation on medical images

Authors:
raw_author_name: S. Krishna Mohan
raw_affiliation_string: Department of Mechanical Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamil Nadu, India

Abstract: Patients with lung cancer can only be diagnosed and treated surgically. Early detection of lung cancer through medical imaging could save numerous lives. Adding advanced techniques to conventional tests that offer high accuracy in diagnosing lung cancer is essential. U-Net has excelled in diversified tasks involving the segmentation of medical image datasets. A significant challenge remains in determining the ideal combination of hyper parameters for designing an optimized U-Net for detailed image segmentation. In our work, we suggested a technique for automatically generating evolutionary U-Nets to detect and segregate lung cancer anomalies. We used three distinct datasets, namely the LIDC-IRDC Dataset, Luna 16 Dataset, and Kaggle Dataset, for training the proposed work on lung images. Our results, examined with six distinct evaluation criteria used for medical image segmentation, consistently demonstrated the highest performance. More specifically, the GA-UNet outperforms conventional approaches in terms of an impressive accuracy rate of 97.5% and a Dice similarity coefficient (DSC) of 92.3%.