Reviewerly/exHarmony huggingface dataset:
Deep semi-supervised clustering based on pairwise constraints and sample similarity

Semi-supervised clustering methods enhance the performance of completely unsupervised clustering tasks by incorporating pairwise relationship information from a subset of samples. However, previous methods based on pairwise constraints have struggled to effectively leverage available prior knowledge and fully exploit the similarity relationships between samples. To address these issues, this paper proposes an advanced approach called Deep Semi-Supervised Clustering based on Pairwise Constraints and Sample Similarity (DSCPS). Specifically, DSCPS consists of two training stages: the coarse clustering stage and the fine clustering stage. In the coarse clustering stage, DSCPS assigns probabilities to each sample indicating its belongingness to each cluster by computing the distance to the cluster centers. In the fine clustering stage, DSCPS introduces a predictor network in the clustering space to predict the class labels of each sample. Simultaneously, the thresholds for similarity and dissimilarity between samples are determined based on pairwise constraints. Finally, the encoder and prediction network are further optimized using sample similarity relationships and pairwise constraints as loss conditions, resulting in the final clustering results. Extensive experiments demonstrate that DSCPS outperforms state-of-the-art methods, achieving the highest performance.
A Graph Neural Network with Context Filtering and Feature Correction for Conversational Emotion Recognition

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

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

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.
Local centric energy estimate model for clean and affordable power generation in a solar integrated passenger vehicle

Clean energy generation from Vehicle Integrated Photovoltaic (VIPV) technology is prudent to foster future smart transportation in passenger cars. A VIPV vehicle remits a reliable energy generation scenario in static and dynamic modes. The estimation of irradiance hovering over a vehicle's surface exposure metrics is based on the diurnal sun-path at the local centric coordinate system (LCCS). By inferring the locations' analemma curve, the irradiance patterns can be derived. Literature reports that the irradiance patterns on a parked static vehicle (PSV) are unexplored till date, particularly under southern Indian conditions. In this study, we propose an LCCS for a static vehicle to understand the irradiance over the vehicle when parked in two different cardinal orientations at Vellore (12.9717° N, 79.1654° E), India. Further, experiments were conducted over a PSV facing due south of the location to validate the theoretical claims and factual irradiance patterns. The results reveal that theoretical observations and energy estimates were in proximity with experimental measurements by a deviation of <15 %. An estimated solar potential energy yield of up to 1.66 to 2.09 kWh per day can be attained from a PSV when integrated with thin-film flexible solar modules over the lateral surfaces.
Granular fuzzy rule-based model construction under the collaboration of multiple organizations

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.
Semi-supervised generative adversarial networks for improved colorectal polyp classification using histopathological images

Early and accurate detection of dysplasia in colorectal polyps can improve prognosis and increase survival chances. Recently, automated learning-based approaches using histopathological images have been adopted for improved classification of polyps. The supervised learning approaches do not provide a reliable classification performance due to limited annotated samples. But, in unsupervised learning, some hidden features are extracted from the unlabeled data which may not be effective in discriminating the complex patterns of the dataset. A generative adversarial network (GAN) is proposed in this work based on a semi-supervised framework for colorectal polyp classification using histopathological images. Our framework learns the discriminating features in an adversarial manner from the limited labeled and huge unlabeled data. In the supervised mode, the discriminator of the proposed model is trained to classify the real histopathological images, whereas, in the unsupervised mode, it tries to discriminate between real and fake images, similar to the classical GAN network. By training in unsupervised mode, the discriminator can identify and extract the subtle features from unlabeled images, to develop a generalized robust model. Our technique yielded classification accuracies of 87.50% and 76.25% using 25% and 50% majority voting schemes, respectively, on the UniToPatho dataset.
A provably stable neural network Turing Machine with finite precision and time

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

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

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

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

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

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

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

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%.