Reviewerly/exHarmony huggingface dataset:
Multiple target data-association algorithm based on Takagi–Sugeno intuitionistic fuzzy model

To solve the problem of data association in multiple target tracking in a densely cluttered environment, a multiple target data association algorithm based on Takagi–Sugeno (T-S) intuitionistic fuzzy model is proposed. In the proposed algorithm, the new multiple target T-S intuitionistic fuzzy model (MTTS-IFM) is constructed by incorporating intuitionistic fuzzy sets and intuitionistic fuzzy numbers. To identify the premise parameters for MTTS-IFM, a new intuitionistic index is defined and a modified form of the intuitionistic fuzzy C-means clustering algorithm is proposed. Furthermore, to solve the multiple target data association for the MTTS-IFM approach, the input variables of MTTS-IFM are defined using the maximum intuitionistic fuzzy entropy, and a data association approach is proposed to deal with the uncertainty between targets and measurements. Finally, simulation results show that the performance of the proposed algorithm is better than that of algorithms designed for multiple target tracking in either a clutter-free or cluttered environment. Moreover, the real-time performance was clearly better than that of the joint probabilistic data association filter (JPDAF) and slightly better than that of maximum entropy fuzzy (MEF-JPDAF), Fitzgerald-JPDAF, and a T-S intuitionistic fuzzy model (TS-FM).
Anti-aliasing deep image classifiers using novel depth adaptive blurring and activation function

Deep convolutional networks are vulnerable to image translation or shift, partly due to common down-sampling layers, e.g., max-pooling and strided convolution. These operations violate the Nyquist sampling rate and cause aliasing. The textbook solution is low-pass filtering (blurring) before down-sampling, which can benefit deep networks as well. Even so, non-linearity units, such as ReLU, often re-introduce the problem, suggesting that blurring alone may not suffice. In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring. To this end, we propose a novel Depth Adaptive Blur-pool (DAB-pool) module to replace existing down-sampling methods. Second, we introduce a novel activation function – with a built-in low pass filter, as an additional measure, to keep the problem from reappearing. From experiments, we observe generalisation on other forms of transformations and corruptions as well, e.g., rotation, scale, and noise. We evaluate our method under three challenging settings: (1) a variety of image translations; (2) adversarial attacks – both ℓp bounded and unbounded; and (3) data corruptions and perturbations. In each setting, our method achieves state-of-the-art results and improves clean accuracy on various benchmark datasets.
Randomized block subsampling Kaczmarz-Motzkin method

By introducing a subsampling strategy, we propose a randomized block Kaczmarz-Motzkin method for solving linear systems. Such strategy not only allows one to determine the block size in advance, but also combines and extends two famous strategies, i.e., randomness and greed, and hence can inherit their advantages. Theoretical analysis shows that the proposed method converges linearly in expectation to the least-Euclidean-norm solution. Several numerical examples are reported to verify the efficiency and feasibility of the new method.
MACnet: Mask augmented counting network for class-agnostic counting

Class agnostic counting involves counting the instances of any user-defined class. It is also usually phrased as a matching problem wherein the model finds all the matching objects in a query image given exemplar patches containing the target object. Typically, users define exemplar patches by placing bounding boxes around the target object. However, defining exemplars using bounding boxes inevitably captures both the target object (foreground) and its surrounding background. This would unintentionally match patches similar to the background, leading to an inaccurate count. Moreover, objects poorly represented by a bounding box (e.g., non-axis aligned, irregular, or non-rectangular shapes) may capture a significantly disproportionate amount of background relative to the foreground within the exemplar patch, leading to poor matches. In this paper, we propose to utilize segmentation masks to separate target objects from their background. We derived these segmentation masks from extreme points, which requires no additional annotation effort from the user compared to annotating bounding boxes. Moreover, we designed a module that learns the mask features as residual to the object features, allowing the network to learn how to better incorporate the segmentation masks. Our model improves upon state-of-the-art methods by up to 3.7 MAE points on the FSC-147 benchmark dataset, showing the effectiveness of our masking approach.
Quantile-based Clustering for Functional Data via Modelling Functional Principal Components Scores

Abstract Clustering tasks of functional data arise naturally in many applications, and efficient classification approaches are needed to find groups. The current paper combines the quantile-based model with the principal component analysis of functional data (FPCA). In our proposed procedures, the projection of functional data is first approximated based on (rotated) FPCA. The quantile-based model is then implemented on the space of rotated scores to identify the potential features of underlying clusters. The proposed method overcomes the limitation of using direct basis function expansion such as Fourier, B-spline, or linear fitting, besides representing a nonparametric clustering alternative based on a quantile approach. The proposed method’s performance has been evaluated in a comprehensive simulation study and afterward compared with existing functional and non-functional clustering methods. The simulation study results showed that the proposed method performs well in terms of correct classification rate and computing time average. Finally, a real-world application concerning temporal wind speed data has been analyzed to demonstrate the proposed method’s advantages and usefulness.
Small area estimation of general finite-population parameters based on grouped data

This paper proposes a new model-based approach to small area estimation of general finite-population parameters based on grouped data or frequency data, often available from sample surveys. Grouped data contains information on frequencies of some pre-specified groups in each area, for example, the numbers of households in the income classes. Thus, grouped data provide more detailed insight into small areas than area-level aggregated data. A direct application of the widely used small area methods, such as the Fay–Herriot model for area-level data and nested error regression model for unit-level data, is not appropriate since they are not designed for grouped data. Our novel method adopts the multinomial likelihood function for the grouped data. In order to connect the group probabilities of the multinomial likelihood and the auxiliary variables within the framework of small area estimation, we introduce the unobserved unit-level quantities of interest. They follow a linear mixed model with random intercepts and dispersions after some transformation. Then the probabilities that a unit belongs to the groups can be derived and are used to construct the likelihood function for the grouped data given the random effects. The unknown model parameters (hyperparameters) are estimated by a newly developed Monte Carlo EM algorithm which uses an efficient importance sampling. The empirical best predicts (empirical Bayes estimates) of small area parameters are calculated by a simple Gibbs sampling algorithm. The numerical performance of the proposed method is illustrated based on the model-based and design-based simulations. In the application to the city-level grouped income data of Japan, we complete the patchy maps of the Gini coefficient as well as mean income across the country.
Semi-Supervised Active Learning for Anomaly Detection in Aviation

Anomaly detection in commercial aviation is an extremely challenging yet crucial task. Accurately detecting operationally significant anomalies can save civilian lives and/or result in significant savings in maintenance cost. The current practice uses manually tuned rule-based mechanisms to flag exceedances from predefined safety boundaries. However, this system cannot identify unknown risks and emerging vulnerabilities. Recently, innovative approaches based on machine learning have been used to automate anomaly detection. However, there are limits to their applicability in the field of aviation due to several challenges: 1) Properly reviewed data are scarce in aviation and, as a result, supervised learning cannot reach optimal performance. 2) Operationally significant anomalies do not coincide with statistically significant ones and, as a result, unsupervised learning fails to provide reliable and robust performance. In this paper, we propose a semi-supervised active learning framework for anomaly detection (SALAD), which detects operationally significant anomalies in flight operational quality assurance data. The developed framework works with vast amounts of unlabeled data as well as a small quantity of labeled data reviewed by subject matter experts to reliably identify safety anomalies in flight operations. Moreover, the model’s active learning strategy allows it to detect unknown anomalies that might emerge in the system. We validate the performance of the SALAD with a real-world case study of anomaly detection during the approach to landing of commercial aircraft. We specifically show that the proposed framework reaches reliable performance when only 1% of the data is labeled and can identify unknown anomalies effectively.
Energy efficient federated learning in internet of vehicles: A game theoretic scheme

Abstract A promising alternative to conventional centralized machine learning that protects privacy is federated learning. Energy efficiency, meanwhile, appears as a major problem in federated learning for the Internet of Vehicles due to frequent model aggregations. To reduce the convergence time, existing research focuses on improving vehicular communication. However, it increases the computational and communication load on servers and disregards the true energy costs of vehicles. Thus, we propose an energy‐efficient federated learning scheme based on two‐stage game theories. Specifically, the payoff function first takes into account the energy cost as well as the contribution of each vehicle, which wins the fair payment based on the Shapley value via a cooperative game. Second, under the fairness constraint, energy‐efficient vehicles cooperatively form several coalitions for further federated worker selection through multiple rounds of the hedonic game. Additionally, we also develop a multiagent reinforcement learning algorithm that is leveraged for coalition formation to lower the energy cost in the game. Furthermore, we demonstrate that the game in our scheme is Nash stable. Extensive experimental results show that the scheme improves energy efficiency by around 68.8% in the best case compared to the most widely used method.
Towards diverse and contextually anchored paraphrase modeling: A dataset and baselines for Finnish

Abstract In this paper, we study natural language paraphrasing from both corpus creation and modeling points of view. We focus in particular on the methodology that allows the extraction of challenging examples of paraphrase pairs in their natural textual context, leading to a dataset potentially more suitable for evaluating the models’ ability to represent meaning, especially in document context, when compared with those gathered using various sentence-level heuristics. To this end, we introduce the Turku Paraphrase Corpus, the first large-scale, fully manually annotated corpus of paraphrases in Finnish. The corpus contains 104,645 manually labeled paraphrase pairs, of which 98% are verified to be true paraphrases, either universally or within their present context. In order to control the diversity of the paraphrase pairs and avoid certain biases easily introduced in automatic candidate extraction, the paraphrases are manually collected from different paraphrase-rich text sources. This allows us to create a challenging dataset including longer and more lexically diverse paraphrases than can be expected from those collected through heuristics. In addition to quality, this also allows us to keep the original document context for each pair, making it possible to study paraphrasing in context. To our knowledge, this is the first paraphrase corpus which provides the original document context for the annotated pairs. We also study several paraphrase models trained and evaluated on the new data. Our initial paraphrase classification experiments indicate a challenging nature of the dataset when classifying using the detailed labeling scheme used in the corpus annotation, the accuracy substantially lacking behind human performance. However, when evaluating the models on a large scale paraphrase retrieval task on almost 400M candidate sentences, the results are highly encouraging, 29–53% of the pairs being ranked in the top 10 depending on the paraphrase type. The Turku Paraphrase Corpus is available at github.com/TurkuNLP/Turku-paraphrase-corpus as well as through the popular HuggingFace datasets under the CC-BY-SA license.
The Landscape of Scholarly Book Publishing in Croatia: Finding Pathways for Viable Open Access Models

(1) Background: Open access to scholarly works is globally recognized as a goal to be achieved as soon as possible; however, there is not yet a general understanding of how to achieve open access for books. In considering the most appropriate models of transition, an accurate and detailed insight into national and regional specifics can be of great importance. The aim of this research is to show the current state of scholarly book publishing in Croatia: recognising the key stakeholders, their characteristics, and the current level of open access to scholarly books. (2) Methods: The existing data from two different sources were used: the data about the public subsidies for book publishers by the Ministry of Science and Education and the data on published books from the Croatian Scientific Bibliography CROSBI, both for the period from 2018 to 2021. (3) Results: In the four-year period, 224 Croatian publishers were awarded subsidies to publish 2359 book titles. The majority of the publishers received support for only a small number of titles and relatively low amounts of subsidies. More than half of the titles are published by small private commercial publishers. However, the uptake of digital publishing among commercial publishers is very modest. Open access to scholarly books is almost entirely in the domain of non-commercial publishers. Most open access titles are available on the websites of their publishers. (4) Conclusions: The analysis of the data from these two sources have resulted in an overview of the current state of book publishing in Croatia. Such an overview provides a good basis for designing future measures and creating a national open science plan and can also be a useful contribution to international discussions.
FSopt_k: Finding the Optimal Anonymization Level for a Social Network Graph

k-degree anonymity is known as one of the best models for anonymizing social network graphs. Although recent works have tried to address the privacy challenges of social network graphs, privacy levels are considered to be independent of the features of the graph degree sequence. In other words, the optimal value of k is not considered for the graph, leading to increasing information loss. Additionally, the graph may not need a high privacy level. In addition, determining the optimal value of k for the graph in advance is a big problem for the data owner. Therefore, in this paper, we present a technique named FSopt_k that is able to find the optimal value of k for each social network graph. This algorithm uses an efficient technique to partition the graph nodes to choose the best k value. It considers the graph structure features to determine the best privacy level. In this way, there will be a balance between privacy and loss in the anonymized graph. Furthermore, information loss will be as low as possible. The evaluation results depict that this algorithm can find the optimal value of k in a short time as well as preserve the graph’s utility.
Improved whole rock low detection limit gold analysis by LA-ICP-MS utilizing pressed-powder-pellets

Analysis of Au in geological materials has been proven useful for understanding geological processes related to the formation of various Au-rich ore deposits. However, the Au concentration in the related source rocks is extremely low (∼<1 ng g−1) and the methodologies allowing analysis at sub-ng g−1 levels are time-consuming, costly and generally involve the handling of hazardous chemicals. Here we present an improved method for low Au concentration analysis by laser-ablation inductively-coupled-plasma mass-spectrometry (LA-ICP-MS) of pressed-powder-pellets (PPP). Fine instrumental tuning for key parameters such as ICP-MS sample gas flow, torch position, RF power, laser frequency, ablation style and laser gas flow allows increasing the Au sensitivity while keeping oxide production low. The latter is critical when analyzing low Au concentrations as the ICP-MS analysis can be impaired by 181Ta16O and 180Hf16O1H interferences. Reducing oxide production and quantifying interferences by analyses of matrix-matched Ta- and Hf-doped pellets, allow for efficient corrections. The method improves the method limit of detection with values as low as 0.10–0.05 ng g−1 Au and routine analysis of reference materials shows that Au analysis by PPP-LA-ICP-MS is reliable. This contribution highlights that the PPP-LA-ICP-MS method is suitable for investigating Au distribution and behavior in Au-depleted rocks.
A dataset of sandstone detrital composition from Qinghai‐Tibet Plateau

Abstract As a hot topic in Earth sciences, the Qinghai‐Tibet Plateau has accumulated a large amount of sedimentary‐related data. We constructed a dataset of detrital components for Qinghai‐Tibet Plateau from 63 peer‐reviewed publications. The dataset thus comprises 1813 Late Proterozoic to Pleistocene sandstones from 84 stratigraphic units. For each sample, we present details on reference, detrital composition, GPS, geographic location, depositional age, tectonic setting and depositional environment. It becomes a high‐quality dataset after the information on each sandstone sample was standardized and reviewed by sedimentary experts. The dataset can be used for regional geoscience studies, exploring the general laws of the source‐to‐sink process. The dataset may also be useful in the field of utilities, such as assisting in finding suitable building stones, helping oil and gas and mineral exploration, and so forth.
Loop and distillation: Attention weights fusion transformer for fine‐grained representation

Abstract Learning subtle discriminative feature representation plays a significant role in Fine‐Grained Visual Categorisation (FGVC). The vision transformer (ViT) achieves promising performance in the traditional image classification filed due to its multi‐head self‐attention mechanism. Unfortunately, ViT cannot effectively capture critical feature regions for FGVC due to only focusing on classification token and adopting the strategy of one‐time image input. Besides, the advantage of attention weights fusion is not applied to ViT. To promote the performance of capturing vital regions for FGVC, the authors propose a novel model named RDTrans, which proposes discriminative region with top priority in a recurrent learning way. Specifically, proposed vital regions at each scale will be cropped and amplified as the next input parameters to finally locate the most discriminative region. Furthermore, a distillation learning method is employed to provide better supervision for elevating the generalisation ability. Concurrently, RDTrans can be easily trained end‐to‐end in a weakly‐supervised learning way. Extensive experiments demonstrate that RDTrans yields state‐of‐the‐art performance on four widely used fine‐grained benchmarks, including CUB‐200‐2011, Stanford Cars, Stanford Dogs, and iNat2017.
An integrated security approach for vehicular networks in smart cities

Fog computing, which is an extension of cloud computing is one of the cornerstone for Internet of Things, that witnessed rapid growth because of its ability to enhance several difficult problems such as network congestion, latency, and the lack of regional autonomy. However, privacy concerns and the resulting inefficiency are causing the performance of fog computing to suffer. While suffering from poisoning attacks, the vast majority of current works do not take into consideration of proper balance between them. Specifically, we present blockchain‐enabled secure federated learning in vehicle network (BSFLVN) system model for traffic flow prediction in urban computing to overcome the aforementioned difficulties and narrow the gap. When vehicle devices trade local learning updates, BSFLVN enables them to be exchanged with a blockchain‐based global learning model, which is confirmed by miners. To improve accuracy of classification we proposed federated learning‐based gated recurrent unit for local model update using FL‐GRNN algorithm and global model update using FL‐AGG algorithm. The BSFLVN, which is built on top of this, allows autonomous deep learning‐based GRU to take place without the need for a centralized authority to maintain the global model and coordinate by using the PoW consensus mechanism of blockchain. For preserving privacy and security of local and global model updates we employ the LDP mechanism. For the analysis of the latency performance of BSFLVN, as well as the derivation of the best block production rate we consider communication, consensus delays, and computing cost. The results of a thorough examination demonstrate that BSFLVN outperforms its competitors in terms of privacy protection, efficiency, and resistance to poisoning attacks, among other areas. Various deadline time iteration process is simulated for result evaluation using Fashion MINIST data in which over 93% of accuracy is obtained.
Obstacle-avoidance trajectory planning and sliding mode-based tracking control of an omnidirectional mobile robot

Trajectory planning and tracking control play a vital role in the development of autonomous mobile robots. To fulfill the tasks of trajectory planning and tracking control of a Mecanum-wheeled omnidirectional mobile robot, this paper proposes an artificial potential field-based trajectory-planning scheme and a discrete integral terminal sliding mode-based trajectory-tracking control strategy. This paper proposes a trajectory-planning scheme and a trajectory-tracking control strategy for a Mecanum-wheeled omnidirectional mobile robot by using artificial potential field and discrete integral terminal sliding mode, respectively. First, a discrete kinematic-and-dynamic model is established for the Mecanum-wheeled omnidirectional mobile robot. Then, according to the positions of the robot, target, and obstacles, a reference an obstacle-avoidance trajectory is planned and updated iteratively by utilizing artificial potential field functions. Afterward, a discrete integral terminal sliding mode control algorithm is designed for the omnidirectional mobile robot such that the robot can track the planned trajectory accurately. Meanwhile, the stability of the control system is analyzed and guaranteed proved in the sense of Lyapunov. Last, simulations are executed in the scenarios of static obstacles and dynamic obstacles. The simulation results demonstrate the effectiveness and merits of the presented methods.