Reviewerly/exHarmony huggingface dataset:
iMCOD: Incremental multi-class outlier detection model in data streams

Multi-class outlier detection from multi-class data streams is a crucial yet challenging task for many real-life applications and services. Various detection methods are available e.g., derivation from one-class classification algorithms, prior knowledge of the underlying data distribution, method-specific hyperparameters tuning for different data sets. However, almost all of these methods are designed to work in the batch learning mode. Therefore, it is difficult to apply the existing methods to multi-class data stream applications. To overcome these limitations, this study proposes a new incremental multi-class outlier detection model (iMCOD). Unlike previous approaches, iMCOD incorporates an incremental support vector machine (iSVM) with an incremental local outlier factor (iLOF) in a unified framework. The incoming query sample was classified using iSVM, and the iLOF score was computed using the newly developed class-based nearest neighbor (NN) concept. Employing the class-based median of the NN average deviation metric, we determined whether the query sample is an outlier, and the iSVM was updated accordingly for the next sample. The iMCOD can detect outliers from multi-class data streams without the need for hyperparameter tuning. Extensive experimental and statistical analyses on 15 real-world data sets demonstrated that iMCOD outperformed 13 competitors.
Multi-view clustering with orthogonal mapping and binary graph

The multi-view clustering problem has attracted considerable attention over recent years for the remarkable clustering performance due to exploiting complementary information from multiple views. Most existing related research work processes data in the decimal real value space that is not the most compatible space for computers. Binary code learning, also known as hashing technology, is well-known for fast Hamming distance computation, less storage requirement and accurate calculation results. The Hamming space is most enjoyed by computers because of binary/hash codes. Several studies combine multi-view clustering with binary code learning for improving clustering performance. However, there is much redundant information contained in the learned binary codes, which negatively affects the clustering performance, but these studies ignore eliminating redundant information for learning compact codes. In addition, they don’t give a unified (one-step) clustering framework with binary graph structure, which doesn’t lead to the optimal clustering result due to the information loss during the two-step process. In this paper, to cope with the two issues, we propose an orthogonal mapping binary graph method (OMBG) for the multi-view clustering problem, which makes the mapping matrix of every view orthogonalize for eliminating redundant information and embeds a binary graph structure into the unified binary multi-view clustering framework for extracting local geometric structure information of binary codes and achieving the optimal clustering result. Furthermore, we design an effective optimization algorithm based on alternating direction minimization to solve the model of OMBG. Extensive experiments performed on four frequently-used benchmark multi-view datasets illustrate the superiority of OMBG which is compared with some state-of-the-art clustering baselines.
A concealed poisoning attack to reduce deep neural networks’ robustness against adversarial samples

A poisoning attack method manipulating the training of a model is easily to be detected since the general performance of a model is downgraded. Although a backdoor attack only misleads the decisions on the samples with a trigger but not any samples, the strong association between the trigger and the class ID exposes the attack. The weak concealment limits the damage of current poisoning attacks to machine learning models. This study proposes a poisoning attack against deep neural networks , aiming to not only reduce the robustness of a model against adversarial samples but also explicitly increase its concealment, defined as the accuracy of the contaminated model on untainted samples. In order to improve the efficiency of poisoning sample generation, we propose training interval, gradient truncation, and parallel process mechanisms. As a result, the model trained on the poisoning samples generated by our method is easily misled by slight crafting, and the attack is difficult to be detected since the contaminated model performs well on clean samples. The experimental results show that our method significantly increases the attack success rate without a substantial drop in classification accuracy on clean samples. The transferability and instability of our model are confirmed experimentally.
Efficient Private Federated Submodel Learning

We investigate the problem of private federated submodel learning, where a machine learning model is divided into M submodels and stored in N databases, from which a given user privately reads, updates and writes back an arbitrary submodel. We consider information-theoretic privacy of the updated submodel index as well as the values of the updates. We provide an efficient private read update write (PRUW) scheme which achieves a lower total communication cost compared to the state-of-the-art. Our scheme significantly reduces the writing cost by combining all updates into a single bit in a way that it can be privately decomposed and placed at the relevant positions at the databases. This is achieved by over-designing the system with additional random noise terms in storage, which in turn provides additional security to the submodels. The scheme is designed for arbitrary privacy and security requirements.
Attention assessment based on multi‐view classroom behaviour recognition

Abstract In recent years, artificial intelligence has been applied in many fields, and education has attracted more and more attention. More and more behaviour detection and recognition algorithms are applied in the field of education. Students' attention in class is the key to improving the quality of teaching, and classroom behavior is a direct manifestation of students' attention. In view of the problem that the accuracy of students' classroom behavior recognition is generally low, we apply deep learning to multi‐view behavior detection, which can detect and recognize behaviors from different perspectives, to evaluate students' classroom attention. First, an improved detection model based on YOLOv5 is proposed, which improves the CBL module throughout the entire network to optimize the model and uses SIoU as the loss function to improve the convergence speed of the prediction box. Second, a quantitative evaluation standard for students' classroom attention is established and then training and verification are conducted by collecting multi‐view classroom datasets. Finally, the environment variation in the training model phase is increased to make the model have better generalization ability. Experiments demonstrate that our method can effectively identify and detect students' behaviours in the classroom from different angles, and it has good robustness and feature extraction capabilities.
Robotic Manipulations of Cylinders and Ellipsoids by Ellipse Detection With Domain Randomization

A lot of objects commonly found in industrial and household environments are represented by cylindrical shapes (flange plates) and ellipsoids (oranges). The visual tops of cylindrical objects and the outlines of ellipsoids are approximately formed by elliptic shape primitives. Thus, a robot can manipulate these objects by ellipse detection. However, it poses a challenge for existing methods to detect ellipses that are largely interfered in cluttered environments. The supervised learning method provides a potential solution for ellipse detection in such environments, but manually labeling the ground truth for training the learning model costs much time, money and workload. In this work, we propose a novel approach to employing three-dimensional point-cloud models of cylindrical objects to construct synthetic images with ellipses based on domain randomization technology for training the supervised learning model. Using synthetic data generated in this manner, we build an end-to-end deep neural network with a detection backbone, rotating filters and the rotated region proposal net to do ellipse detection. To our knowledge, this is the first supervised learning model trained only on synthetic data for ellipse detection, enabling a robot to grasp cylindrical and ellipsoid objects. To demonstrate the capabilities of the proposed detector, the performance of the proposed method is superior to those of two state-of-the-art detectors on synthetic and public datasets. The proposed model for ellipse detection and data generation pipeline based on domain randomization in a simulation are evaluated by a series of robotic manipulations implemented in real application scenarios. The results illustrate a high success rate on real-world grasp attempts despite having only been trained on a synthetic dataset.
Vecchia-Approximated Deep Gaussian Processes for Computer Experiments

Deep Gaussian processes (DGPs) upgrade ordinary GPs through functional composition, in which intermediate GP layers warp the original inputs, providing flexibility to model nonstationary dynamics. Two DGP regimes have emerged in recent literature. A “big data” regime, prevalent in machine learning, favors approximate, optimization-based inference for fast, high-fidelity prediction. A “small data” regime, preferred for computer surrogate modeling, deploys posterior integration for enhanced uncertainty quantification (UQ). We aim to bridge this gap by expanding the capabilities of Bayesian DGP posterior inference through the incorporation of the Vecchia approximation, allowing linear computational scaling without compromising accuracy or UQ. We are motivated by surrogate modeling of simulation campaigns with upwards of 100,000 runs—a size too large for previous fully-Bayesian implementations—and demonstrate prediction and UQ superior to that of “big data” competitors. All methods are implemented in the deepgp package on CRAN. Supplementary materials for this article are available online.
Introducing the Rank-Biased Overlap as Similarity Measure for Feature Importance in Explainable Machine Learning: A Case Study on Parkinson’s Disease

Feature importance is one of the most common explanations provided by Machine Learning (ML). However, different classification algorithms or different training sets could produce different rankings of predictive features. Thus, the quantification of differences between feature importance is crucial for assessing model trustworthiness. Rank-biased Overlap (RBO) is a similarity measure between incomplete, top-weighted and indefinite rankings, which are all characteristics of feature importance. In RBO, tuning persistence p allows to truncate rankings at any arbitrary depth, so to evaluate their overlapping size at increasing number of features. Classification of Parkinson’s disease (PD) with Explainable Boosting Machine (EBM) was chosen here as case study for introducing RBO in ML. An imbalanced dataset, 168 healthy controls (HC) and 396 PD patients, with 178 among clinical and imaging features was obtained from PPMI. Imbalanced, undersampled (K-Medoids) and oversampled (SMOTE) datasets were used for training EBMs, obtaining their respective feature importance. RBO score was calculated between ranking pairs incrementally increasing the depth by five features, from 1 to 178. All classifiers reached excellent AUC-ROC (~1) on test set, demonstrating the EBM prediction stability when trained on imbalanced datasets. RBO revealed that the maximum size of overlapping (80%) among rankings was obtained truncating at top 40 features, while their similarity decreased asymptotically to 50% when more than 45 features were considered. Thanks to RBO it was possible to demonstrate that, for the same accuracy, the more similar are the feature importance, the more stable is the model and the more reliable is the ML interpretability.
Investigating hardware acceleration for simulation of CFD quantum circuits

Among the many computational models for quantum computing, the Quantum Circuit Model is the most well-known and used model for interacting with current quantum hardware. The practical implementation of quantum computers is a very active research field. Despite this progress, access to physical quantum computers remains relatively limited. Furthermore, the existing machines are susceptible to random errors due to quantum decoherence, as well as being limited in number of qubits, connectivity and built-in error correction. Simulation on classical hardware is therefore essential to allow quantum algorithm researchers to test and validate new algorithms in a simulated-error environment. Computing systems are becoming increasingly heterogeneous, using a variety of hardware accelerators to speed up computational tasks. One such type of accelerators, Field Programmable Gate Arrays (FPGAs), are reconfigurable circuits that can be programmed using standardized high-level programming models such as OpenCL and SYCL. FPGAs allow to create specialized highly-parallel circuits capable of mimicking the quantum parallelism properties of quantum gates, in particular for the class of quantum algorithms where many different computations can be performed concurrently or as part of a deep pipeline. They also benefit from very high internal memory bandwidth. This paper focuses on the analysis of quantum algorithms for applications in computational fluid dynamics. In this work we introduce novel quantum-circuit implementations of model lattice-based formulations for fluid dynamics, specifically the D1Q3 model using quantum computational basis encoding, as well as, efficient simulation of the circuits using FPGAs. This work forms a step toward quantum circuit formulation of the Lattice Boltzmann Method (LBM). For the quantum circuits implementing the nonlinear equilibrium distribution function in the D1Q3 lattice model, it is shown how circuit transformations can be introduced that facilitate the efficient simulation of the circuits on FPGAs, exploiting their fine-grained parallelism. We show that these transformations allow us to exploit more parallelism on the FPGA and improve memory locality. Preliminary results show that for this class of circuits the introduced transformations improve circuit execution time. We show that FPGA simulation of the reduced circuits results in more than 3× improvement in performance per Watt compared to the CPU simulation. We also present results from evaluating the same kernels on a GPU.
The Sentiment Analysis of Human Behavior on Products and Organizations using K-Means Clustering and SVM Classifier

Sentiment analysis Sentiment analysis is the process of extracting information from the text and it is considered as opinion mining. Machine learning experiments have been shown in the study that involves review, blog, for some texts that are written in different languages including Dutch, English and French. Set of example sentence have been set that are manually labeled, neutral, positive or negative. The study covers the curiosity of the consumer regarding the specific consumption products. Categorization models have been developing that has been used in the study. Number of issues that includes noisy nature of the text has been discussed in the study. With an accuracy of roughly 83 percent for English texts, we can determine positive, negative, and neutral sentiments toward the subject under investigation using unigram features augmented with linguistic information. The role of active learning approaches in minimizing the number of instances that need to be manually annotated is discussed in this article. Our studies also give data on the transferability of learned models between domains and languages. Here, Sentiment analysis of a particular person has studied using a K-Means Clustering and SVM classifier to classify the sentiment of a person from the text
Sparse random projection isolation forest for outlier detection

Isolation Forest has a low computational complexity, hence has been widely applied to detect outliers in large-scale data. However, it suffers from the artifacts caused by the hyperplanes chosen, thereby failing to detect outliers in some specific regions. To tackle this problem, we propose the random-projection-based Isolation Forest, which works in two steps. First, we transform the data using the random projection technique. Then, we employ the Isolation Forest to identify outliers using the transformed data. Experimental results show that the proposed methods outperform 12 state-of-the-art outlier detectors.
A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting

For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. Thus, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. Therefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. These features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods.
Integrating implicit and explicit linguistic phenomena via multi-task learning for offensive language detection

The analysis and detection of offensive content in textual information have become a great challenge for the Natural Language Processing community. Most of the research conducted so far on offensive language detection have addressed this task as a sole optimization objective. However, other linguistic phenomena that are arguably correlated with offensive language and therefore could be beneficial to recognize this type of problematic content on the Web, have not been explored in depth so far. Thus, the goal of this study is to investigate whether explicit and implicit concepts involved in the expression of offensive language help in the detection of this phenomenon and how to incorporate these concepts in a computational system. We propose a multi-task learning approach that includes such concepts according to the relevance shown by a feature selection method called mutual information. Our experiments show that some phenomena such as constructiveness, target group and person, figurative language (sarcasm and mockery), insults, improper language, and emotions combined together help to optimize the offensive language detection task, outperforming a state-of-the-art method (the transformer BETO) that we use as our baseline to compare the results.
Recursive-NeRF: An Efficient and Dynamically Growing NeRF

View synthesis methods using implicit continuous shape representations learned from a set of images, such as the Neural Radiance Field (NeRF) method, have gained increasing attention due to their high quality imagery and scalability to high resolution. However, the heavy computation required by its volumetric approach prevents NeRF from being useful in practice; minutes are taken to render a single image of a few megapixels. Now, an image of a scene can be rendered in a level-of-detail manner, so we posit that a complicated region of the scene should be represented by a large neural network while a small neural network is capable of encoding a simple region, enabling a balance between efficiency and quality. Recursive-NeRF is our embodiment of this idea, providing an efficient and adaptive rendering and training approach for NeRF. The core of Recursive-NeRF learns uncertainties for query coordinates, representing the quality of the predicted color and volumetric intensity at each level. Only query coordinates with high uncertainties are forwarded to the next level to a bigger neural network with a more powerful representational capability. The final rendered image is a composition of results from neural networks of all levels. Our evaluation on public datasets and a large-scale scene dataset we collected shows that Recursive-NeRF is more efficient than NeRF while providing state-of-the-art quality. The code will be available at https://github.com/Gword/Recursive-NeRF.
A survey on participant selection for federated learning in mobile networks

Federated Learning (FL) is an efficient distributed machine learning paradigm that employs private datasets in a privacy-preserving manner. The main challenges of FL are that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID). Due to limited communication bandwidth and unstable availability of such devices in a mobile network, only a fraction of end devices (also referred to as the participants or clients in a FL process) can be selected in each round. Hence, it is of paramount importance to utilize an efficient participant selection scheme to maximize the performance of FL including final model accuracy and training time. In this paper, we provide a review of participant selection techniques for FL. First, we introduce FL and highlight the main challenges during participant selection. Then, we review the existing studies and categorize them based on their solutions. Finally, we provide some future directions on participant selection for FL based on our analysis of the state-of-the-art in this topic area.
What moves us? The intrinsic memorability of dance.

Our most moving experiences, the ones that "stick," are hardly ever static but are dynamic, like a conversation, a gesture, or a dance. Previous work has shown robust memory for simple actions (e.g., jumping or turning), but it remains an open question how we remember more dynamic sequences of complex and expressive actions. Separately, with static images, previous work has found remarkable consistency in which images are remembered or forgotten across people-that is, an intrinsic "memorability"-but it is unclear whether semantically ambiguous and expressive actions might similarly be consistently remembered, despite the varying interpretations of what they could mean. How do we go from static memories to more memorable dynamic experiences? Using the test case of a rich and abstract series of actions from dance, we discover memorability as an intrinsic attribute of movement. Across genres, some movements were consistently remembered, regardless of the perceiver, and even regardless of the dancer. Among a comprehensive set of memory, movement, and aesthetic attributes, consistency in which movements people remembered was most predicted by subjective memorability, and importantly by both subjective (observer ratings) and objective (optical flow analysis) measures of the scale of motion, such that the less overall motion in a dance segment, the more memorable the movements tended to be. Importantly, we discover that memorability of a sequence is additive, where the memorability of individual snapshots and constituent moments ultimately contribute to the memorability of longer sequences. (PsycInfo Database Record (c) 2023 APA, all rights reserved).