Reviewerly/exHarmony huggingface dataset:
STMMOT: Advancing multi-object tracking through spatiotemporal memory networks and multi-scale attention pyramids

Multi-object Tracking (MOT) is very important in human surveillance, sports analytics, autonomous driving, and cooperative robots. Current MOT methods do not perform well in non-uniform movements, occlusion and appearance-reappearance scenarios. We introduce a comprehensive MOT method that seamlessly merges object detection and identity linkage within an end-to-end trainable framework, designed with the capability to maintain object links over a long period of time. Our proposed model, named STMMOT, is architectured around 4 key modules: (1) Candidate proposal creation network, generates object proposals via vision-Transformer encoder-decoder architecture; (2) Scale variant pyramid, progressive pyramid structure to learn the self-scale and cross-scale similarities in multi-scale feature maps; (3) Spatio-temporal memory encoder, extracting the essential information from the memory associated with each object under tracking; and (4) Spatio-temporal memory decoder, simultaneously resolving the tasks of object detection and identity association for MOT. Our system leverages a robust spatio-temporal memory module that retains extensive historical object state observations and effectively encodes them using an attention-based aggregator. The uniqueness of STMMOT resides in representing objects as dynamic query embeddings that are updated continuously, which enables the prediction of object states with an attention mechanism and eradicates the need for post-processing. Experimental results show that STMMOT archives scores of 79.8 and 78.4 for IDF1, 79.3 and 74.1 for MOTA, 73.2 and 69.0 for HOTA, 61.2 and 61.5 for AssA, and maintained an ID switch count of 1529 and 1264 on MOT17 and MOT20, respectively. When evaluated on MOT20, it scored 78.4 in IDF1, 74.1 in MOTA, 69.0 in HOTA, and 61.5 in AssA, and kept the ID switch count to 1264. Compared with the previous best TransMOT, STMMOT achieves around a 4.58% and 4.25% increase in IDF1, and ID switching reduction to 5.79% and 21.05% on MOT17 and MOT20, respectively.
<i>No Solamente Ellos Aprenden, Aprendemos Nosotros También</i> : Latina Mothers’ Experiences with Co-Design Approaches in Creating a Math Activity for Their Children

ABSTRACTCo-design approaches have roots in participatory research design methodologies, amplifying participants' experiences, cultures, and beliefs by co-designing or co-creating an artifact or activity for and with other people. Little research has incorporated Spanish-speaking Latine families in co-designing early math tools and resources. Therefore, this study explored how six Latina mothers engaged in co-creating a math activity for their children. The results revealed that Latina mothers used cultural repertoires to navigate the co-design sessions and construct mathematical knowledge. Additionally, this study found that co-design methods centered Latina mothers as designers and users of their own math learning experience, positioning them as experts in their own learning and allowing for the co-creation of a meaningful math learning experience. These results provide insights into how co-design methods herald a different, more engaging way to involve Latine families in the design of math learning experiences and tools to standard family engagement interventions.KEYWORDS: Co-design researchearly mathLatine familiesfamily mathearly childhood Disclosure statementNo potential conflict of interest was reported by the author(s).
Hyperbolic Tangent Like Based Univariate Banach Space Valued Neural Network Approximation

Here we examine the univariate quantitative approximation of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. We perform also the related Banach space valued fractional approximation.
Banach Space Valued Multivariate Multi Layer Neural Network Approximation Based on q-Deformed and $$\lambda $$-Parametrized Hyperbolic Tangent Function

Here we study the multivariate quantitative approximation of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N}$$ , $$N\in \mathbb {N},$$ by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators. We investigate also the case of approximation by iterated multilayer neural network operators of the last four types. These approximations are achieved by establishing multidimensional Jackson type inequalities involving the multivariate modulus of continuity of the engaged function or its partial derivatives. Our multivariate operators are defined by using a multidimensional density function induced by a q-deformed and $$\lambda $$ -parametrized hyperbolic tangent function, which is a sigmoid function. The approximations are pointwise and uniform. The related feed-forward neural network are with one or multi hidden layers.
Parametrized Arctangent Activated Banach Space Valued Multi Layer Neural Network Multivariate Approximation

Here we examine the multivariate quantitative approximations of Banach space valued continuous multivariate functions on a box or $$\mathbb {R}^{N},$$ $$N\in \mathbb {N}$$ , by the multivariate normalized, quasi-interpolation, Kantorovich type and quadrature type neural network operators.
Approximation by Neural Networks of Brownian Motion

The author recently derived several approximation results by neural network operators see the monograph [16]. There, the activation functions are induced by the arctangent, algebraic, Gudermannian and generalized symmetrical sigmoid functions. The results we apply here are univariate on a compact interval, regular and fractional.
Banach Space Valued Ordinary and Fractional Neural Network Approximation Based on q-Deformed and $$\beta $$-Parametrized Half Hyperbolic Tangent

Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach space valued neural network operators. These approximations are derived by establishing Jackson type inequalities involving the modulus of continuity of the engaged function or its Banach space valued high order derivative of fractional derivatives. Our operators are defined by using a density function generated by a q-deformed and $$\beta $$ -parametrized half hyperbolic tangent function, which is a sigmoid function. The approximations are pointwise and of the uniform norm. The related Banach space valued feed-forward neural networks are with one hidden layer.
Blockchain and Emerging Distributed Ledger Technologies for Decentralized Multi-robot Systems

Abstract Purpose of Review: Distributed ledger technologies (DLTs), particularly blockchain, are paving the way to securing and managing distributed and large-scale systems of autonomous agents. We look into how these technologies are moving out of the lab and into the real world within the robotics field. Recent Findings: Despite the scalability and real-world applicability concerns, new solutions have emerged that show resilience to intermittent connectivity, as well as scalable solutions for managed or permissioned networks. Summary: We present a review on the various use cases that different DLTs can support in multi-robot systems. We argue that the majority of the work to date on open and permissionless blockchains is only applicable to a subset of robotics use cases, with novel DLT architectures and permissioned blockchains driving adoption across industrial and more mature application scenarios.
A Reinduction-Based Approach for Efficient High Utility Itemset Mining from Incremental Datasets

Abstract High utility itemset mining is a crucial research area that focuses on identifying combinations of itemsets from databases that possess a utility value higher than a user-specified threshold. However, most existing algorithms assume that the databases are static, which is not realistic for real-life datasets that are continuously growing with new data. Furthermore, existing algorithms only rely on the utility value to identify relevant itemsets, leading to even the earliest occurring combinations being produced as output. Although some mining algorithms adopt a support-based approach to account for itemset frequency, they do not consider the temporal nature of itemsets. To address these challenges, this paper proposes the Scented Utility Miner (SUM) algorithm that uses a reinduction strategy to track the recency of itemset occurrence and mine itemsets from incremental databases. The paper provides a novel approach for mining high utility itemsets from dynamic databases and presents several experiments that demonstrate the effectiveness of the proposed approach.
An Instance- and Label-Based Feature Selection Method in Classification Tasks

Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we calculate the linear relationship between each feature and the target variable, resulting in correlation coefficients. Features with high correlation coefficients are selected. Compared to traditional methods, our approach offers two advantages. Firstly, it effectively selects features highly correlated with the target variable from a large feature set, reducing data dimensionality and improving analysis and modeling efficiency. Secondly, our method considers label correlation between features, enhancing the accuracy of selected features and subsequent model performance. Experimental results on three datasets demonstrate the effectiveness of our method in selecting features with high correlation coefficients, leading to superior model performance. Notably, our approach achieves a minimum accuracy improvement of 3.2% for the advanced classifier, lightGBM, surpassing other feature selection methods. In summary, our proposed method, based on instance and label correlation, presents a suitable solution for classification problems.
Secure CAPTCHA by Genetic Algorithm (GA) and Multi-Layer Perceptron (MLP)

To achieve an acceptable level of security on the web, the Completely Automatic Public Turing test to tell Computer and Human Apart (CAPTCHA) was introduced as a tool to prevent bots from doing destructive actions such as downloading or signing up. Smartphones have small screens, and, therefore, using the common CAPTCHA methods (e.g., text CAPTCHAs) in these devices raises usability issues. To introduce a reliable, secure, and usable CAPTCHA that is suitable for smartphones, this paper introduces a hand gesture recognition CAPTCHA based on applying genetic algorithm (GA) principles on Multi-Layer Perceptron (MLP). The proposed method improves the performance of MLP-based hand gesture recognition. It has been trained and evaluated on 2201 videos of the IPN Hand dataset, and MSE and RMSE benchmarks report index values of 0.0018 and 0.0424, respectively. A comparison with the related works shows a minimum of 1.79% fewer errors, and experiments produced a sensitivity of 93.42% and accuracy of 92.27–10.25% and 6.65% improvement compared to the MLP implementation. The range of the supported hand gestures can be a limit for the application of this research as a limited range may result in a vulnerable CAPTCHA. Also, the processes of training and testing require significant computational resources. In the future, we will optimize the method to run with high reliability in various illumination conditions and skin color and tone. The next development plan is to use augmented reality and create unpredictable random patterns to enhance the security of the method.
FPQNet: Fully Pipelined and Quantized CNN for Ultra-Low Latency Image Classification on FPGAs Using OpenCAPI

Convolutional neural networks (CNNs) are to be effective in many application domains, especially in the computer vision area. In order to achieve lower latency CNN processing, and reduce power consumption, developers are experimenting with using FPGAs to accelerate CNN processing in several applications. Current FPGA CNN accelerators usually use the same acceleration approaches as GPUs, where operations from different network layers are mapped to the same hardware units working in a multiplexed manner. This will result in high flexibility in implementing different types of CNNs; however, this will degrade the latency that accelerators can achieve. Alternatively, we can reduce the latency of the accelerator by pipelining the processing of consecutive layers, at the expense of more FPGA resources. The continued increase in hardware resources available in FPGAs makes such implementations feasible for latency-critical application domains. In this paper, we present FPQNet, a fully pipelined and quantized CNN FPGA implementation that is channel-parallel, layer-pipelined, and network-parallel, to decrease latency and increase throughput, combined with quantization methods to optimize hardware utilization. In addition, we optimize this hardware architecture for the HDMI timing standard to avoid extra hardware utilization. This makes it possible for the accelerator to handle video datasets. We present prototypes of the FPQNet CNN network implementations on an Alpha Data 9H7 FPGA, connected with an OpenCAPI interface, to demonstrate architecture capabilities. Results show that with a 250 MHz clock frequency, an optimized LeNet-5 design is able to achieve latencies as low as 9.32 µs with an accuracy of 98.8% on the MNIST dataset, making it feasible for utilization in high frame rate video processing applications. With 10 hardware kernels working concurrently, the throughput is as high as 1108 GOPs. The methods in this paper are suitable for many other CNNs. Our analysis shows that the latency of AlexNet, ZFNet, OverFeat-Fast, and OverFeat-Accurate can be as low as 69.27, 66.95, 182.98, and 132.6 µs, using the architecture introduced in this paper, respectively.
TWITTER’DA KADIN POLİTİKA GAZETECİLERİNE YÖNELİK ÇEVRİM İÇİ TACİZ

Journalistic practices are negatively affected by digitalization and economic developments. In particular, digitalization both extends the working hours of journalists and makes them more accessible than ever before. The accessibility of journalists online exposes them not only to positive feedback but also to negative feedback. In this sense, one of the most important problems of journalists is online harassment. Therefore, this study analyzes the comments on political information, opinions or news shared by women journalists in Turkey on Twitter. In this context, the online harassment that political journalists in Turkey are exposed to on Twitter is analyzed. The study mainly aims to reveal the types of online harassment Turkish women political journalists face on Twitter. According to the results of the study, the majority (60.97%) of the comments made to women journalists consist of negative discourse.The most common negative discourse against women journalists (n=31,489) is swearing.The data obtained allows for the comparison of the types of online harassment faced by journalists in different countries and the identification of similarities and differences.
Hybrid quantum ResNet for car classification and its hyperparameter optimization

Abstract Image recognition is one of the primary applications of machine learning algorithms. Nevertheless, machine learning models used in modern image recognition systems consist of millions of parameters that usually require significant computational time to be adjusted. Moreover, adjustment of model hyperparameters leads to additional overhead. Because of this, new developments in machine learning models and hyperparameter optimization techniques are required. This paper presents a quantum-inspired hyperparameter optimization technique and a hybrid quantum-classical machine learning model for supervised learning. We benchmark our hyperparameter optimization method over standard black-box objective functions and observe performance improvements in the form of reduced expected run times and fitness in response to the growth in the size of the search space. We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization. Our tests show a qualitative and quantitative advantage over the corresponding standard classical tabular grid search approach used with a deep neural network ResNet34. A classification accuracy of 0.97 was obtained by the hybrid model after 18 iterations, whereas the classical model achieved an accuracy of 0.92 after 75 iterations.
Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.
Task-based Meta Focal Loss for Multilingual Low-resource Speech Recognition

Low-resource automatic speech recognition is a challenging task due to a lack of labeled training data. To resolve this issue, multilingual meta-learning learns a better model initialization from many source language tasks for fast adaptation to unseen target languages. However, for diverse source languages, the quantity and difficulty vary greatly because of their different data scales and phonological systems. These differences lead to task-quantity and task-difficulty imbalance issues and thus a failure of multilingual meta-learning ASR. In this work, we propose a task-based meta focal loss (TMFL) approach to address this tough challenge. Specifically, we introduce a hard-task moderator and update the meta-parameters using gradients from both the support set and query set. Our proposed approach focuses more on hard tasks and makes full use of the data from hard tasks. Moreover, we analyze the significance of the hard task moderator and interpret its significance at the sample level. Experiment results show that the proposed method, TMFL, significantly outperforms the state-of-the-art multilingual meta-learning on all target languages for the IARPA BABEL and OpenSLR datasets, especially under very-low-resource conditions. In particular, it can reduce character error rate from 72% to 60% by fine-tuning the pre-trained model with about 22 hours of Vietnamese data.
Adaptive active contour model based on local bias field estimation and saliency

Some active contour models proposed based on intensity inhomogeneity are sensitive to initialization and cannot achieve ideal segmentation results for real images. An adaptive active contour model based on local bias field estimation and saliency is proposed in this paper. First of all, this model proposes an adaptive multi-local search algorithm, which avoids the initialization sensitivity by adaptively setting of the initial contour; Secondly, the local bias field is estimated by fusing the saliency map and fuzzy c-means clustering; Finally, the new bias field and the corrected energy fitting constant are used to define the new energy functional. The desired target object is obtained by minimizing the energy functional. The experimental results show that the segmentation accuracy of the model proposed in this paper is higher than that of the models participating in the comparison. The proposed model can not only avoid the interference of initialization and redundant information, but also segment images with intensity inhomogeneity effectively.
Optical and atomic decoherence in quantum nondemolition measurement induced atomic ensemble entanglement

We study the effects of optical and atomic decoherence in entangled atomic ensembles produced via quantum nondemolition (QND) measurements. We examine potentially experimentally detrimental effects, such as optical phase diffusion, photon loss and gain, and atomic dephasing. For the optical decoherence channels, we use the technique of integration within ordered operators to obtain the associated Kraus operators. We analyze the effect of different decoherence channels on various quantities, such as the variances of the spin operators, entanglement and correlation criteria, logarithmic negativity, and the Bell–CHSH inequality. We generally find a smooth decay of correlations and entanglement in the presence of decoherence. In the short interaction time range, we find that various quantities show signals consistent with, and showing that entanglement exists under all three types of decoherence. Our results show that QND measurements are one of the most promising methods for entanglement generation between two Bose–Einstein condensates.
HGR-ResNet: Hand Gesture Recognition with Enhanced Residual Neural Network

Hand Gesture Recognition (HGR) has garnered increasing attention in recent years due to its potential to enhance human-computer interaction (HCI) and facilitate communication between individuals who are mute or deaf and the wider public. HGR can facilitate non-contact interaction between humans and machines, offering an effective interface for recognizing sign language used in everyday communication. This paper proposes a novel approach for static HGR using transfer learning of ResNet152 with early stopping, adaptive learning rate, and class weightage techniques, referred to as HGR-ResNet. Transfer learning enables the model to utilize previously acquired knowledge from pre-training on a large dataset, allowing it to learn from pre-extracted image features. Early stopping serves as a regularization technique, halting the training process before overfitting occurs. Adaptive learning rate adjusts the learning rate dynamically based on the model's error rate during training, promoting faster convergence and improved accuracy. Additionally, the class weightage technique is employed to address the issue of class imbalance in the data, ensuring fair representation and mitigating biases during the training process. To assess the effectiveness of the proposed model, we conduct a comparative analysis with multiple existing methods using three distinct datasets: the American Sign Language (ASL) dataset, ASL with digits dataset, and the National University of Singapore (NUS) hand gesture dataset. HGR-ResNet achieves remarkable results, with an average accuracy of 99.20% across all three datasets, and individual accuracies of 99.88% for the ASL dataset, 98.93% for the ASL with digits dataset, and 98.80% for the NUS hand gesture dataset.