IoUformer: Pseudo-IoU prediction with transformer for visual tracking

Abstract: Siamese tracking has witnessed tremendous progress in tracking paradigm. However, its default box estimation pipeline still faces a crucial inconsistency issue, namely, the bounding box decided by its classification score is not always best overlapped with the ground truth, thus harming performance. To this end, we explore a novel simple tracking paradigm based on the intersection over union (IoU) value prediction. To first bypass this inconsistency issue, we propose a concise target state predictor termed IoUformer, which instead of default box estimation pipeline directly predicts the IoU values related to tracking performance metrics. In detail, it extends the long-range dependency modeling ability of transformer to jointly grasp target-aware interactions between target template and search region, and search sub-region interactions, thus neatly unifying global semantic interaction and target state prediction. Thanks to this joint strength, IoUformer can predict reliable IoU values near-linear with the ground truth, which paves a safe way for our new IoU-based siamese tracking paradigm. Since it is non-trivial to explore this paradigm with pleased efficacy and portability, we offer the respective network components and two alternative localization ways. Experimental results show that our IoUformer-based tracker achieves promising results with less training data. For its applicability, it still serves as a refinement module to consistently boost existing advanced trackers.


Inertial measurement and heart-rate sensor-based dataset for geriatric fall detection using custom built wrist-worn device

Abstract: This paper describes a dataset acquired from 41 volunteers performing 16 Activities of daily livings (ADLs) and 8 Falls repeated 5 times. This data was collected using a custom wrist-worn end device. The dataset has data collected from Inertial measurement unit (IMU) and heart-rate sensors. The end device is built using Qualcomm Snapdragon 820c System on Chip (SoC) interfaced to the sensors via Interconnect Integrated Circuit (I2C) protocol. The data was sampled for every activity at a rate of 20 Hz for the motion sensors and at a rate of 1 Hz for the heart-rate sensor. The motion sensor comprised of a triaxial accelerometer, triaxial gyroscope, triaxial magnetometer and a linear accelerometer. The heart-rate sensor was medical grade and all sensors were calibrated for the wrist -worn position. The dataset is available on this website https://shamanx86.github.io/fall_detection_data/ and https://doi.org/10.5281/zenodo.10013090.


Saccadic selection in visual working memory is robust across the visual field and linked to saccade metrics: Evidence from nine experiments and more than 100,000 trials.

Abstract: Visual working memory and actions are closely intertwined. Memory can guide our actions, but actions also impact what we remember. Even during memory maintenance, actions such as saccadic eye movements select content in visual working memory, resulting in better memory at locations that are congruent with the action goal as compared to incongruent locations. Here, we further substantiate the claim that saccadic eye movements are fundamentally linked to visual working memory by analyzing a large data set (> 100k trials) of nine experiments (eight of them previously published). Using Bayesian hierarchical models, we demonstrate robust saccadic selection across the full range of probed saccade directions, manifesting as better memory performance at the saccade goal irrespective of its location in the visual field. By inspecting individual differences in saccadic selection, we show that saccadic selection was highly prevalent in the population. Moreover, both saccade metrics and visual working memory performance varied considerably across the visual field. Crucially, however, both idiosyncratic and systematic visual field anisotropies were not correlated between visual working memory and the oculomotor system, suggesting that they resulted from different sources (e.g., rely on separate spatial maps). In stark contrast, trial-by-trial variations in saccade metrics were strongly associated with memory performance: At any given location, shorter saccade latencies and more accurate saccades were associated with better memory performance, undergirding a robust link between action selection and visual memory. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


Holevo bound independent of weight matrices for estimating two parameters of a qubit

Abstract: Holevo bound plays an important role in quantum metrology as it sets the ultimate limit for multi-parameter estimations, which can be asymptotically achieved. Except for some trivial cases, the Holevo bound is implicitly defined and formulated with the help of weight matrices. Here we report the first instance of an intrinsic Holevo bound, namely, without any reference to weight matrices, in a nontrivial case. Specifically, we prove that the Holevo bound for estimating two parameters of a qubit is equivalent to the joint constraint imposed by two quantum Cramér–Rao bounds corresponding to symmetric and right logarithmic derivatives. This weightless form of Holevo bound enables us to determine the precise range of independent entries of the mean-square error matrix, i.e., two variances and one covariance that quantify the precisions of the estimation, as illustrated by different estimation models. Our result sheds some new light on the relations between the Holevo bound and quantum Cramér–Rao bounds. Possible generalizations are discussed.


Applying Scrum to Knowledge Transfer Among Software Developers

Abstract: In the teaching-learning processes, research and continuous innovation are encouraged. Now, when talking about innovation, the concept of adapting methodologies that have been successfully applied to speed up and increase the quality of software projects begins to emerge, is the case of agile software development methodologies. Scrum is one of the most widely used methodologies in the software development process, it increases productivity and deliverable quality of software development teams, but there is not much evidence of how to use it to structure learning content and its use to transfer new knowledge in training or software development contexts. To fill this gap, this research analyzes the applicability of Scrum for transferring new knowledge among software developers is developed. In this paper, we describe the details of how to start transferring knowledge using Scrum, guiding the reader through its standards, processes, phases, and objectives. We also report an experiment for improving students’ performance to support the approach’s benefits in the academy context and a case study performed inside the company context.


Dataset of Arabic spam and ham tweets

Abstract: This data article provides a dataset of 132421 posts and their corresponding information collected from Twitter social media. The data has two classes, ham or spam, where ham indicates non-spam clean tweets. The main target of this dataset is to study a way to classify whether a post is a spam or not automatically. The data is in Arabic language only, which makes the data essential to the researchers in Arabic natural language processing (NLP) due to the lack of resources in this language. The data is made publicly available to allow researchers to use it as a benchmark for their research in Arabic NLP. The dataset was collected using the Twitter REST API between January 27, 2021, and March 10, 2021. An ad-hoc crawler was constructed using Python programming language to collect the data. Many scientists and researchers will benefit from this dataset in the domain of cybersecurity, NLP, data science and social networking analysis.


MM-Net: A Multi-Modal Approach Toward Automatic Modulation Classification

Abstract: Automatic Modulation Classification (AMC) has become an important component in communication systems for both civil and defense applications. The shortcomings of traditional approaches to AMC have led researchers to develop complex machine learning (ML)-based approaches. In this work, inspired by multi-modal approaches for general Computer Vision tasks like Semantic Segmentation, we propose MM-Net, a multimodal approach to AMC that uses domain-specific features in the form of Higher Order Cumulants (HOCs) to improve classification performance. Furthermore, we explore the usage of HOCs in existing Deep Learning (DL)-based applications for AMC. Simulation results show that for eight modulation classification, MM-Net achieves high classification accuracy even at low SNRs, demonstrating the robustness of the multimodal approach even under challenging channel conditions, while existing methods are improved by utilizing HOCs, especially at low SNR values.


RP-SG: Relation Prediction in 3D Scene Graphs for Unobserved Objects Localization

Abstract: The ability to search for objects is a fundamental prerequisite for mobile robots when addressing a wide range of automation tasks. However, how to effectively estimate the positions of unobserved objects in a continuously changing environment remains an open challenge. Previous works have utilized probabilistic models to estimate the co-occurrence property between the target object and the observed landmark objects in a scene. However, few approaches can predict the precise spatial relations between objects based on a specific scene configuration. In this letter, we propose a novel unobserved object localization framework that achieves context-specific relation prediction based on the particular configuration of a scene. First, we leverage a 3D scene graph as a compact representation of the environment and propose a relation prediction model based on graph neural networks. This model can effectively interpret the information provided by the 3D scene graph and make accurate relation predictions. Second, to address the challenge of a high number of non-existent links between objects in the scene graph, we introduce a novel loss function that can better address imbalanced training data. Additionally, we propose an evaluation framework to comprehensively assess whether the relation prediction model benefits object search tasks. Comprehensive evaluation results obtained on public datasets and real-world scenes reveal the superiority of our method over competing approaches.


A Novel Generalized Adaptive Domain Fusion Framework for Structured Light 3-D Measurement

Abstract: As a three-dimensional measurement technology that combines simplicity, accuracy, and noncontact measurement, structured light has attracted much attention in industrial metrology. Fringe projection profilometry (FPP) is one of the most promising structured light techniques based on phase analysis. In FPP, the phase-height model is a key element that significantly impacts the precision of three-dimensional measurements. However, the performance of such models is sensitive to the unwrapped phase and lacks the ability for adaptive adjustment. Despite extensive research on improving the mapping relationship of phase-height models, effectively using pixel phase information to establish a more accurate and reliable phase-height model remains a challenging issue, especially in cases with limited numbers of calibration planes. To address this issue, we propose a generalized adaptive domain fusion (GADF) framework for FPP. The core principle of the GADF framework is to use a specially designed dual indexing criterion to mine the adaptive domain for each pixel, and then use the domain model fusion technology to improve the accuracy of the height measurement. Experiment results demonstrate that the GADF framework outperforms traditional phase-height models.


International Human Rights Law

Abstract: Abstract Facebook’s Oversight Board upheld Donald Trump’s suspension not because he incited violence, which the First Amendment would not protect, but because he glorified violence, which the Board said is unprotected under international law, even though it is protected by the First Amendment. This chapter suggests that so-called content-moderation cases such as Facebook’s suspension of Trump should be government by the First Amendment, not international human rights law. The two are fundamentally different. International human rights law aims to protect individuals’ rights from interference by states, not from interference by other individuals or businesses. Adherence to it by private companies is voluntary and nonbinding. It is not a single, monolithic body of law. It is incomplete, fragmented, and inconsistent. Some of its elements allow speech suppression that is flatly incompatible with that permitted by the Constitution. The principal provisions of its seminal treaties are often vague and loophole-ridden. They have spawned state practice and customary international law that are confusing and contradictory. The net effect is a false universalism, a façade of consensus that masks indeterminacy and enables arbitrariness and cherry-picking.


A Dataset for Illuminant- and Device- invariant Colour Barcode Decoding with Cameras

Abstract: Barcodes are visual representations of data widely used in commerce and administration to compactly codify information about objects, services, and people [1]. Specifically, a barcode is an image composed of parallel lines, with different widths, spacing and sizes. Generally, the lines are dark (usually black) on a bright background (usually white) or vice-versa. Thanks to this representation, barcodes can be detected and decoded in a way robust to changes of light and noise (see, e.g, [2], [3], [4], [5]). However, using barcodes with several colours for the lines is quite intriguing because it enables boosting the barcode's data capacity. Colour barcodes still pose a challenge today, even though numerous studies on the topic were conducted between 1990 and 2022 [6]. The main issue that needs to be solved is the creation of an optical technology able to decode colour sequences regardless of the ambient light, the acquisition and printing or visualisation device, and the physical support on which the barcode is printed or displayed. To the best of our knowledge, the studies currently available in literature do not provide the experimental data on which they are based, nor are there online databases that can be used for further studies or for training data analysis procedures based on artificial intelligence techniques. To fill this gap and push further research in this technology, we built COCO-10, a public dataset of colour barcode images, that would like to become a testbench for the development and testing of colour barcode decoding algorithms, taking into account the colour variability due to the light, to the printer and camera gamuts and to the quality of the paper on which the barcode is printed. COCO-10 contains 5400 images of 150 colour barcodes, each of one printed on two white papers with different density and printers and acquired under six illuminations by three smartphones’ cameras. For each colour barcode image, a mask identifying the region occupied by the barcode is released too. The 150 colour barcodes have been generated by colouring the lines of black & white barcodes with colours randomly selected from a palette of ten colours including both warm and colour hues. The name COCO-10 just refers to the fact that the dataset contains COlor BarCOdes with 10 possible colours for each line. We also provide a set of 300 images created as follows. The 150 COCO-10 colour barcodes were synthetically superimposed on 150 cluttered backgrounds, resulting in 150 images. The first 75 (group 1) were printed on thick paper, the others (group 2) on plain paper. Each group was further subdivided into subsets of 25 images, resulting in 3 subgroups, each of which was captured by 2 smartphones’ cameras under one of the 6 illuminants mentioned above. We also provide masks for these images. These images would like to be a benchmark for testing the accuracy of barcode decoding algorithms, bearing in mind that the performance of these algorithms may be influenced by the accuracy of the previous detection of the barcodes themselves in the background. The total number of images in COCO-10 is 11700, including the 300 synthetic images of the colour barcodes displayed on white and cluttered background, the 5700 real-world images of the colour barcodes printed on white papers and with cluttered backgrounds and their corresponding 5700 masks. We finally highlight that COCO-10 can be also used for developing and testing algorithms for gamut and tone mapping, machine colour constancy, and colour correction.


2-D Compressive Sensing-Based Visually Secure Multilevel Image Encryption Scheme

Abstract: Aiming at the problem of how to effectively protect the sensitive information of multiple images and ensure the security of their transmission, we propose a visually meaningful multilevel image encryption solution that combines 3D chaotic mapping, DCT and 2D compressed sensing. Firstly, the association relationship between related grayscale images is established by using different reversible relationship matrices. We then undersample multiple grayscale images with established relationships via 2D compressive sensing. Secondly, chaotic scrambling and index scrambling are performed on the obtained multiple measurement results. Finally, the visually meaningful steganographic image is obtained by embedding different measurement results into the selected color image via the discrete cosine transform. Compared with the existing methods, this approach solves the problem of multi-privacy protection by realizing multi-level encryption of multiple images. More importantly, it improves the encryption ability and transmission efficiency by establishing the correlation relationship between different images. Realize the secure transmission of ciphertext data by information hiding and chaotic encryption mechanisms. Provide multi-level image recovery quality to authorized users by providing different authorization keys. In short, the proposed scheme has the advantages of high encryption capability, high security and controllable access. It is especially suitable for compressive sampling, privacy protection and secure transmission of images in sensor networks with limited computing power and transmission bandwidth.


AERMNet: Attention-enhanced relational memory network for medical image report generation

Abstract: The automatic generation of medical image diagnostic reports can assist doctors in reducing their workload and improving the efficiency and accuracy of diagnosis. However, among the most existing report generation models, there are problems that the weak correlation between generated words and the lack of contextual information in the report generation process. To address the above problems, we propose an Attention-Enhanced Relational Memory Network (AERMNet) model, where the relational memory module is continuously updated by the words generated in the previous time step to strengthen the correlation between words in generated medical image report. And the double LSTM with interaction module reduces the loss of context information and makes full use of feature information. Thus, more accurate disease information can be generated by AERMNet for medical image reports. Experimental results on four medical datasets Fetal heart (FH), Ultrasound, IU X-Ray and MIMIC-CXR, show that our proposed method outperforms some of the previous models with respect to language generation metrics (Cider improving by 2.4% on FH, Bleu1 improving by 2.4% on Ultrasound, Cider improving by 16.4% on IU X-Ray, Bleu2 improving by 9.7% on MIMIC-CXR). Conclusions: This work promotes the development of medical image report generation and expands the prospects of computer-aided diagnosis applications. Our code is released at https://github.com/llttxx/AERMNET.


SNLI Indo: A Recognizing Textual Entailment Dataset in Indonesian Derived from the Stanford Natural Language Inference Dataset

Abstract: Recognizing textual entailment (RTE) is an essential task in natural language processing (NLP). It is the task of determining the inference relationship between text fragments (premise and hypothesis), of which the inference relationship is either entailment (true), contradiction (false), or neutral (undetermined). The most popular approach for RTE is neural networks, which has resulted in the best RTE models. Neural network approaches, in particular deep learning, are data-driven and, consequently, the quantity and quality of the data significantly influences the performance of these approaches. Therefore, we introduce SNLI Indo, a large-scale RTE dataset in the Indonesian language, which was derived from the Stanford Natural Language Inference (SNLI) corpus by translating the original sentence pairs. SNLI is a large-scale dataset that contains premise-hypothesis pairs that were generated using a crowdsourcing framework. The SNLI dataset is comprised of a total of 569,027 sentence pairs with the distribution of sentence pairs as follows: 549,365 pairs for training, 9,840 pairs for model validation, and 9,822 pairs for testing. We translated the original sentence pairs of the SNLI dataset from English to Indonesian using the Google Cloud Translation API. The existence of SNLI Indo addresses the resource gap in the field of NLP for the Indonesian language. Even though large datasets are available in other languages, in particular English, the SNLI Indo dataset enables a more optimal development of deep learning models for RTE in the Indonesian language.


Machine-Learning-Assisted Soft Fiber Optic Glove System for Sign Language Recognition

Abstract: Sign language recognition devices are effective approaches to breaking the communication barrier between signers and non-signers and exploring human-machine interactions. Wearable gloves have been developed for gesture recognition and virtual reality applications by employing flexible sensors for motion detection and machine learning for data analysis. However, most existing wearable devices present limited sign language translating capacity due to the sensors' design and distribution. Here, we propose a cost-effective dual-hand soft fiber optic glove system consisting of multimode soft liquid-core fiber optic sensors, gyroscopes, wireless printed circuit boards, and batteries for sign language translation. In combination with different deep learning techniques and recognition strategies, the glove system can recognize static gestures and dynamic gestures of American Sign Language, and deduce the meaning of sentences by the sequence of gestures. The soft glove system exhibits a broad sign language range (10 numbers, 26 alphabets, 18 words, and 5 sentences meaning prediction), and high recognition accuracy (98.6% for static gestures, 95% for dynamic gestures). The results also present the recognizing capacity for high-correlated gestures (e.g., “M” and “N”). Finally, we demonstrate its application for controlling the motion of a virtual character through 7 discrete commands in the VR interface.


Pelican Whale Optimization Enabled Deep Learning Framework for Video Steganography Using Arnold Transform-Based Embedding

Abstract: Steganography refers to hiding a secret message from various sources, such as images, videos, audio and so on. The advantage of steganography is to avoid data hacking in transmission medium during the transmission of information sources. Video steganography is superior to image steganography since the videos can hide a substantial quantity of secret messages more than the image. Hence, this research introduced the video stereography technique, Arnold Transform with SqueezeNet-based Pelican Whale Optimization Algorithm (AT[Formula: see text]SqueezeNet_PWOA), for concealing the secret image on the video. To hide the secret image on the video, the proposed method follows three steps: key frame and feature extraction, pixel prediction and embedding. The extraction of the key frame process is carried out by the Structural Similarity Index Measure (SSIM), and then the neighborhood features and convolutional neural network (CNN) features are extracted from the frame to improve the robustness of the embedding process. Moreover, the pixel prediction is completed by the SqueezeNet model, wherein the learning factors are tuned by the PWOA. In addition, the embedding process is completed by applying the Arnold transform on the predicted pixel, and the transformed regions are combined with the secret image using the embedding function. Likewise, the extraction process extracts the secret image from the embedded video by substituting the predicted pixel and Arnold transform on the embedded video. The proposed method is used to hide chunks of secret data in the form of video sequences and it improves the performance. The Arnold transform used in this work provides security by encrypting the data. The use of SqueezeNet makes the proposed model a simple design and this reduces the computational time. Thus, the AT[Formula: see text]SqeezeNet_PWOA attained better correlation coefficient (CC), peak signal-to-noise ratio (PSNR) and mean square error (MSE) of 0.908, 48.66 and 0.001 dB with the Gaussian noise.


TaSPM: Targeted Sequential Pattern Mining

Abstract: Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated, but also increase the efficiency of users in performing related analysis. The current algorithms available for targeted sequence querying are based on specific scenarios and can not be extended to other applications. In this article, we formulate the problem of targeted sequential pattern mining and propose a generic algorithm, namely TaSPM. What is more, to improve the efficiency of TaSPM on large-scale datasets and multiple-item-based sequence datasets, we propose several pruning strategies to reduce meaningless operations in the mining process. Totally four pruning strategies are designed in TaSPM, and hence TaSPM can terminate unnecessary pattern extensions quickly and achieve better performance. Finally, we conducted extensive experiments on different datasets to compare the baseline SPM algorithm with TaSPM. Experiments show that the novel targeted mining algorithm TaSPM can achieve faster running time and less memory consumption.


Hardware Accelerator for High Accuracy Sign Language Recognition with Residual Network Based on FPGAs

Abstract: The ResNet series of networks has demonstrated powerful capabilities in the fields of object detection and image classification, garnering increasing attention from researchers. However, due to their deep network architectures, accelerator development based on FPGA faces challenges associated with limited on-chip resources and lengthy design cycles. This paper presents a versatile hardware acceleration system based on FPGA, achieving optimization through both hardware implementation and software inference architecture. The system reduces network complexity by employing techniques such as inter-layer fusion and dynamic quantization, while enhancing hardware resource utilization through channel parallelism and tightly-pipelined hardware design principles. By configuring and reusing computation units, the forward inference process of ResNet series networks can be rapidly deployed on FPGA, shortening the development and validation cycles. The proposed system is validated using the ResNet-18 model on a PYNQ-Z2 development board within a gesture recognition application scenario. The overall power consumption of the system is 2.136W, with hardware inference accuracy reaching 98.87%.


Hilbert’s and Takeda’s single-shot interferometry with a linear-carrier: a comparison

Abstract: Abstract The purpose of the present work is to compare single-shot phase-retrieval methods based on Hilbert’s transform with Takeda’s spatial linear-carrier method. In addition, we briefly discuss the so-called slightly-off-axis method that involves two interferograms as an efficient way to remove background illumination bias. After carefully analyzing the assumptions implicit in both approaches, we demonstrate that the Hilbert transform based method is completely identical to the 30 years old Takeda’s interferometry with a spatial linear-carrier. Validation experiments are presented.


Correlation-aware Graph Data Augmentation with Implicit and Explicit Neighbors

Abstract: In recent years, there has been a significant surge in commercial demand for citation graph-based tasks, such as patent analysis, social network analysis, and recommendation systems. Graph Neural Networks (GNNs) are widely used for these tasks due to their remarkable performance in capturing topological graph information. However, GNNs’ output results are highly dependent on the composition of local neighbors within the topological structure. To address this issue, we identify two types of neighbors in a citation graph: explicit neighbors based on the topological structure and implicit neighbors based on node features. Our primary motivation is to clearly define and visualize these neighbors, emphasizing their importance in enhancing graph neural network performance. We propose a Correlation-aware Network (CNet) to re-organize the citation graph and learn more valuable informative representations by leveraging these implicit and explicit neighbors. Our approach aims to improve graph data augmentation and classification performance, with the majority of our focus on stating the importance of using these neighbors, while also introducing a new graph data augmentation method. We compare CNet with state-of-the-art (SOTA) GNNs and other graph data augmentation approaches acting on GNNs. Extensive experiments demonstrate that CNet effectively extracts more valuable informative representations from the citation graph, significantly outperforming baselines. The code is available on public GitHub. 1


Attacking Click-through Rate Predictors via Generating Realistic Fake Samples

Abstract: How to construct imperceptible (realistic) fake samples is critical in adversarial attacks. Due to the sample feature diversity of a recommender system (containing both discrete and continuous features), traditional gradient-based adversarial attack methods may fail to construct realistic fake samples. Meanwhile, most recommendation models adopt click-through rate (CTR) predictors, which usually utilize black-box deep models with discrete features as input. Thus, how to efficiently construct realistic fake samples for black-box recommender systems is still full of challenges. In this article, we propose a hierarchical adversarial attack method against black-box CTR models via generating realistic fake samples, named CTRAttack. To better train the generation network, the weights of its embedding layer are shared with those of the substitute model, with both the similarity loss and classification loss used to update the generation network. To ensure that the discrete features of the generated fake samples are all real, we first adopt the similarity loss to ensure that the distribution of the generated perturbed samples is sufficiently close to the distribution of the real features, and then the nearest neighbor algorithm is used to retrieve the most appropriate features for non-existent discrete features from the candidate instance set. Extensive experiments demonstrate that CTRAttack can not only effectively attack the black-box recommender systems but also improve the robustness of these models while maintaining prediction accuracy.


Dynamic Attentive Convolution for Facial Beauty Prediction

Abstract: Facial Beauty Prediction (FBP) is a significant pattern recognition task that aims to achieve consistent facial attractiveness assessment with human perception. Currently, Convolutional Neural Networks (CNNs) have become the mainstream method for FBP. The training objective of most conventional CNNs is usually to learn static convolution kernels, which, however, makes the network quite difficult to capture global attentive information, and thus usually ignores the key facial regions, e.g., eyes, and nose. To tackle this problem, we devise a new convolution manner, Dynamic Attentive Convolution (DyAttenConv), which integrates the dynamic and attention mechanism into convolution in kernel-level, with the aim of enforcing the convolution kernels adapted to each face dynamically. DyAttenConv is a plug-and-play module that can be flexibly combined with existing CNN architectures, making the acquisition of the beauty-related features more globally and attentively. Extensive ablation studies show that our method is superior to other fusion and attention mechanisms, and the comparison with other state-of-the-arts also demonstrates the effectiveness of DyAttenConv on facial beauty prediction task.


Content-Adaptive Optimization Framework for Universal Deep Image Compression

Abstract: While deep image compression performs better than traditional codecs like JPEG on natural images, it faces a challenge as a learning-based approach: compression performance drastically decreases for out-of-domain images. To investigate this problem, we introduce a novel task that we call universal deep image compression, which involves compressing images in arbitrary domains, such as natural images, line drawings, and comics. Furthermore, we propose a content-adaptive optimization framework to tackle this task. This framework adapts a pre-trained compression model to each target image during testing for addressing the domain gap between pre-training and testing. For each input image, we insert adapters into the decoder of the model and optimize the latent representation extracted by the encoder and the adapter parameters in terms of rate-distortion, with the adapter parameters transmitted per image. To achieve the evaluation of the proposed universal deep compression, we constructed a benchmark dataset containing uncompressed images of four domains: natural images, line drawings, comics, and vector arts. We compare our proposed method with non-adaptive and existing adaptive compression methods, and the results show that our method outperforms them. Our code and dataset are publicly available at https://github.com/kktsubota/universal-dic.


Rotation-Invariant Convolution Networks with Hexagon-Based Kernels

Abstract: We introduce the Hexagonal Convolutional Neural Network (HCNN), a modified version of CNN that is robust against rotation. HCNN utilizes a hexagonal kernel and a multi-block structure that enjoys more degrees of rotation information sharing than standard convolution layers. Our structure is easy to use and does not affect the original tissue structure of the network. We achieve the complete rotational invariance on the recognition task of simple pattern images and demonstrate better performance on the recognition task of the rotated MNIST images, synthetic biomarker images and microscopic cell images than past methods, where the robustness to rotation matters.


High-Accuracy People Counting in Large Spaces Using Overhead Fisheye Cameras

Abstract: Quantifying the number of people in various spaces of a commercial building is important for saving energy, optimizing space usage, and assisting with public safety. To accomplish these goals requires obtaining accurate, fine-grained counts in real time. However, existing methodologies are ineffective for covering large areas with high occupancy. We propose an occupancy-sensing system that uses multiple overhead fisheye cameras and state-of-the-art deep-learning algorithms to cover large spaces with high counting accuracy. Tested in a 2,000 ft2 space, our system shows 54% to 83% reduction in commonly-used error metrics compared to recent people-counting methods proposed for large-space scenarios. Our system is scalable to arbitrarily-large spaces; additional cameras can be integrated with minimal commissioning. We also introduce two new performance metrics for assessing counting accuracy that, unlike common metrics used to date, are independent of occupancy level and can be easily compared across different occupancy scenarios.


A novel approach for detecting deep fake videos using graph neural network

Abstract: Abstract Deep fake technology has emerged as a double-edged sword in the digital world. While it holds potential for legitimate uses, it can also be exploited to manipulate video content, causing severe social and security concerns. The research gap lies in the fact that traditional deep fake detection methods, such as visual quality analysis or inconsistency detection, need help to keep up with the rapidly advancing technology used to create deep fakes. That means there's a need for more sophisticated detection techniques. This paper introduces an enhanced approach for detecting deep fake videos using graph neural network (GNN). The proposed method splits the detection process into two phases: a mini-batch graph convolution network stream four-block CNN stream comprising Convolution, Batch Normalization, and Activation function. The final step is a flattening operation, which is essential for connecting the convolutional layers to the dense layer. The fusion of these two phases is performed using three different fusion networks: FuNet-A (additive fusion), FuNet-M (element-wise multiplicative fusion), and FuNet-C (concatenation fusion). The paper further evaluates the proposed model on different datasets, where it achieved an impressive training and validation accuracy of 99.3% after 30 epochs.


What Happens Next? Resonance—Answer

Abstract: Abstract I demonstrate in the answer some of the resonances that occur when the strings of the piano are free to vibrate.


Stabilizing Error Correction Mechanism in the Presence of Explosiveness

Abstract: In the presence of explosiveness of the adjustment term in the error correction model, the adjustment of the dependent variable Y was too large and overshoots the equilibrium, creating a divergent pattern. The error correction model fails to capture the deviation from equilibrium appropriately, thereby resulting in overshooting of the model. In this paper, a new model to stabilize the explosiveness in an Error Correction model called the stabilizing Error Correction Mechanism was proposed. Mathematical methodology for obtaining the estimate of the model using the Ordinal Least Square method was derived. Error Correction model was used to model the relationship among the variables and the result was compared with the Stabilizing Error Correction Mechanism using root mean square error. A Monte-Carlo simulation was performed, and the stimulation results showed that the error correction model exhibited some explosiveness, and the damping coefficient of the stabilizing model exerted a stabilizing effect on the error correction mechanism, thereby reducing the overshooting in the error correction model. The proposed model contributed to a smoother and more stable response to deviations from the long-run equilibrium. The root mean square error of the stabilizing Error Correction model was observed to be 1.30663, 1.04533, 12.55786, 10.49876, 10.0034, and 19.41545 as compared to the adjustment model in the Error Correction model (60.6888, 35.5929, 315238, 24.31958, 10.1485 and 19.7687) when the persistence is high and . Therefore, the Stabilizing Error Correction model performs better than the Error Correction model.


Accurate Fourier-space statistics for line intensity mapping: Cartesian grid sampling without aliased power

Abstract: Abstract Estimators for n-point clustering statistics in Fourier-space demand that modern surveys of large-scale structure be transformed to Cartesian coordinates to perform Fast Fourier Transforms (FFTs). In this work, we explore this transformation in the context of pixelised line intensity maps (LIM), highlighting potential biasing effects on power spectrum measurements. Current analyses often avoid a complete resampling of the data by approximating survey geometry as rectangular in Cartesian space, an increasingly inaccurate assumption for modern wide-sky surveys. Our simulations of a 20 × 20 deg2 21cm LIM survey at 0.34 &lt; z &lt; 0.54 show this assumption biases power spectrum measurements by ${&gt;}\, 20~{{\%}}$ across all scales. We therefore present a more robust framework for regridding the voxel intensities onto a 3D FFT field by coordinate transforming large numbers of Monte-Carlo sampling particles. Whilst this unbiases power spectrum measurements on large scales, smaller-scale discrepancies remain, caused by structure smoothing and aliasing from separations unresolved by the grid. To correct these effects, we introduce modelling techniques, higher-order particle assignments, and interlaced FFT grids to suppress the aliased power. Using a Piecewise Cubic Spline (PCS) particle assignment and an interlaced FFT field, we achieve sub-percent accuracy up to 80% of the Nyquist frequency for our 21cm LIM simulations. We find a more subtle hierarchical improvement in results for higher-order assignment schemes, relative to the gains made for galaxy surveys, which we attribute to the extra complexity in LIM from additional discretising steps. Python code accompanying this paper is available at github.com/stevecunnington/gridimp.


Constructing new high-order polynomial chaotic maps and application in pseudorandom number generator

Abstract: Abstract The chaotic map have been widely applied in fields such as pseudorandom number generation(PRNG) and image encryption due to its excellent chaotic performance. In this paper, two classes of high-order polynomial chaotic maps of special form are proposed based on the Li-Yorke theorem. The dynamic behavior of the proposed maps is numerically analyzed, including bifurcation and Lyapunov exponent, and the analysis results prove the validity of the proposed conclusions. The proposed polynomial chaotic maps have a larger parameters and chaotic range, as well as a more stable Lyapunov exponent. Furthermore, based on the coupling chaotic systems, we design a pseudorandom number generator(PRNG), and the number of chaos parameters are expanded through the coupling control parameters in a PRNG. Then the performance of the pseudorandom sequence generated by the PRNG is tested and analysed. The test and analysis results show that the pseudorandom sequence has favorabble security, structural complexity, and randomness. . Especially, the information entropy of 7.9998 and the key space size of 2^ 208 exceed the recently reported pseudorandom number generators(PRNGs). In comparison with other pseudorandom number generators(PRNGs) based on chaotic maps in the recent literature, this paper provides comprehensive performance test and analysis of the proposed PRNG and demonstrates its potential for cryptographic applications.


Hamiltonian Switching Control of Noisy Bipartite Qubit Systems

Abstract: Abstract We develop a Hamiltonian switching ansatz for bipartite control that is inspired by the Quantum Approximate Optimization Algorithm (QAOA), to mitigate environmental noise on qubits. We demonstrate the control for a central spin coupled to bath spins via isotropic Heisenberg interactions, and then make physical applications to the protection of quantum gates performed on superconducting transmon qubits coupling to environmental two-level-systems (TLSs) through dipole-dipole interactions, as well as on such qubits coupled to both TLSs and a Lindblad bath. The control field is classical and acts only on the system qubits. We use reinforcement learning with policy gradient (PG) to optimize the Hamiltonian switching control protocols, using a fidelity objective for specific target quantum gates. &#xD; We use this approach to demonstrate effective suppression of both coherent and dissipative noise, with numerical studies achieving target gate implementations with fidelities over 0.9999 (four nines) in the majority of our test cases and showing improvement beyond this to values of 0.999999999 (nine nines) upon a subsequent optimization by Gradient Ascent Pulse Engineering (GRAPE). We analyze how the control depth, total evolution time, number of environmental TLS, and choice of optimization method affect the fidelity achieved by the optimal protocols and reveal some critical behaviors of bipartite control of quantum gates.


The Applications based on Video Motion Magnification Techniques

Abstract: This research study's major goal is to present an important overview of recent work on applications based on Video Motion magnification (VMM) approaches during the course of the last 10 years. Over the past few years, video motion magnification (VMM) technologies have attracted a lot of attention and research, particularly as applications based on video motion have become more and more necessary. With an increase in the number of recommended procedures, surveying and evaluation become necessary. In this study, we will highlight how the survey was focused on several articles that used motion video augmentation techniques in their applications. We contrast these applications as well.


A Method of Applying Virtual Reality Converged Remote Platform Based on Crawfish Optimization Algorithm to Improve ESN Network

Abstract: INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch.OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods.METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments.RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms.CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved.


The Impact of Rotation Model on Minimizing Inflectional Morphemes Errors in English Writing: A Comparative Study of Error Analysis

Abstract: The study aims to analyze 12th-grade students’ writing errors related to inflectional morphemes. In addition, the research measures efficacy of the Rotation Model (RM) and the Grammar Translation Method (GTM) in minimizing writing errors among learners of English. Comparative studies are rare to find the effectiveness of two teaching models in the context of error analysis. Eighty-two samples were taken into consideration for randomize sampling. The present research is divided into two groups; the experimental group, consisting of 41 participants, provided with instruction via RM, and the control group, also comprising 41 individuals, being taught through GTM. Pre and post-test treatment data were collected and analyzed with the help of SPSS 22. The results revealed common errors with inflectional morphemes among both groups. Additionally, the mean values for each type of error were significantly minimized due to the post-instruction of RM compared to the GTM. The study’s findings largely devoted to pedagogical improvement among ESL learners’ writing. The findings are reviewed concerning future research directions and instructional strategies.


FEIR: Quantifying and Reducing Envy and Inferiority for Fair Recommendation of Limited Resources

Abstract: Recommendation in settings such as e-recruitment and online dating involves distributing limited opportunities, which differs from recommending practically unlimited goods such as in e-commerce or music recommendation. This setting calls for novel approaches to quantify and enforce fairness. Indeed, typical recommender systems recommend each user their top relevant items, such that desirable items may be recommended simultaneously to more and to less qualified individuals. This is arguably unfair to the latter. Indeed, when they pursue such a desirable recommendation (e.g. by applying for a job), they are unlikely to be successful. To quantify fairness in such settings, we introduce inferiority : a novel (un)fairness measure that quantifies the competitive disadvantage of a user for their recommended items. Inferiority is complementary to envy : a previously-proposed fairness notion that quantifies the extent to which a user prefers other users’ recommendations over their own. We propose to use both inferiority and envy in combination with an accuracy-related measure called utility : the aggregated relevancy scores of the recommended items. Unfortunately, none of these three measures are differentiable, making it hard to optimize them, and restricting their immediate use to evaluation only. To remedy this, we reformulate them in the context of a probabilistic interpretation of recommender systems, resulting in differentiable versions. We show how these loss functions can be combined in a multi-objective optimization problem that we call FEIR (Fairness through Envy and Inferiority Reduction), used as a post-processing of the scores from any standard recommender system. Experiments on synthetic and real-world data show that the proposed approach effectively improves the trade-offs between inferiority, envy and utility, compared to the naive recommendation and the state of the art method for the related problem of congestion alleviation in job recommendation. We discuss and enhance the practical impact of our findings on a wide range of real-world recommendation scenarios, and we offer implementations of visualization tools to render the envy and inferiority metrics more accessible.


HA-DQS-Net: dynamic query design based on transformer with hollow attention

Abstract: A common problem in the field of object detection is that the image features could not be fully expressed. And another issue is that the static query selection in the detection transformer (DETR)-like models cannot adapt well to different datasets due to the fixed number of selected object queries. To solve these problems, hollow attention (HA) and dynamic query selection (DQS) modules were proposed, and a network HA-DQS-Net was further formed. HA integrates specially designed masks into self-attention to better combine channel and spatial directional feature information, thereby learning more complex and comprehensive target features. DQS improves the idea of static query selection in the current DETR-like model by dynamically selecting the number of object queries based on the actual number of targets in the image, which enhances the accuracy of the model. HA-DQS-Net, which combines the advantages of HA and DQS, has a competitive performance in the field of object detection. The excellent detection effectiveness of our viewpoint is validated based on PASVAL VOC and a homemade smoking dataset. It is worth noting that all APs have been improved when HA is applied to different DETR-like models, which improves the universality of the HA module.


Work In Progress: Implementing Team Projects in Online Courses - Balancing Individual Responsibility and Team Collaboration

Abstract: In this role, she focuses on


Augmented Reality for Sustainable Collaborative Design

Abstract: Her scholarship is motivated by the fundamental question of how


A project-based platform for students’ Robot Operation System (ROS) programming experience

Abstract: Abstract Robot Operating System (ROS) is an open-source software framework for robot automation. ROS includes a protocol of robot commanding and integrates with a collection of software libraries. ROS aims to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms. Currently, ROS is the major robotics software framework for researchers in graduate schools. One of the obstacles in exposing ROS to students is lacking appropriate projects for starters. ROS is not learner-friendly for its steep learning curve and comprehensive programming background needed. In this article, a robotics platform is designed by the author's institution, based on an iRobot Create robot, is introduced. The robotics platform that includes a mobile base, a webcam, and a Lidar, can fully support ROS-based programming for autonomous navigation. To help students start ROS programming practicing, a ROS-based student autonomous navigation project, including components of path-planning, image recognition, and simultaneous localization and mapping (SLAM) is implemented on this robotics platform. A pilot study shows that the platform is favored by students and inspiring for students to learn ROS. However, the curriculum needs improvements to ensure the students' success in completing the tasks.


Physically Contextualized Machining Instructions through Augmented Reality

Abstract: Abstract Hands-on fabrication skills require complementary capacity for spatial thinking, but teaching often relies on 2D drawings to render inherently three-dimensional concepts. Computer-aided Design (CAD) software provides robust engineering visualization, but 3D models are displayed on a 2D screen and separate from the learner's physical context. Augmented Reality (AR) is another 3D technology that has the potential to facilitate embodied learning and an introductory student's transfer of concepts to tasks, because of its ability to integrate three-dimensional information into the physical context of authentic tasks. We present the design, development, and initial course implementation of AR instructions to fabricate a metal flashlight using common fabrication tools and equipment in the machine shop. The app gives written instructions and engineering drawings, accompanied by an AR visualization of how the workpiece changes step-by-step and how all of the parts are eventually assembled. This app has now been released into an undergraduate-level introductory fabrication course ("Mechanical Engineering Tools") at the Massachusetts Institute of Technology (MIT). We developed pre- and post-assessments to measure cognitive and affective outcomes and compare outcomes between a pilot AR-based cohort (N=6) to the traditional cohort (N=20). A psychomotor assessment was also carried out for a subset of the cohorts. This work suggests AR-enhanced instruction may promote learning transfer in hands-on skills training.


Institutional Review Panel for Cybersecurity Research and Education

Abstract: Cybersecurity is an emerging field with significant implications as the use of interconnected devices increases.We are now at a point where the number of connected devices significantly exceeds the world population.[1] Each of these devices represents a potential entry point for individuals with malicious intentions.As such, many contend that cybersecurity is national security extending across multiple governmental, industry, and consumer sectors.To mitigate new and current threats as cybersecurity evolves into the future, considerable education and research is needed on both the operational technology and network sides of the industry.This research and education, by its nature, involves vulnerability testing, intentional network intrusion, virus testing and ethical hacking.The conduct of these activities has associated internal and external risk, as well as ethical considerations.Typically, these tasks require exceptions to the policies and controls in place to protect information.To manage the risk and ensure ethical conduct, a policy requiring review and approval of such activities is necessary.Such a policy, although different in focus, is similar in concept to policies for research and education involving humans and animals.Presented in this paper is the development of the Cybersecurity Institutional Review Policy and associated Cybersecurity Institutional Review Panel (CIRP) implemented in the RELLIS Academic Alliance on the RELLIS Campus of The Texas A&M University System.The review form is expected to be submitted and approved prior to any research and education activities involving cybersecurity.This includes class projects, independent research and study, and faculty research.The CIRP reviews all requests and evaluates the compensating controls to determine their acceptability.If predetermined risk levels are not exceeded, the policy includes provisions for an expedited or shortened review.The rationale for the provisions in the policy, the levels of review and the makeup of the review panel are discussed.The implemented policy and required review form are included as appendices.


Shifting to a Virtual Summer STEM Program for High School Students (Evaluation)

Abstract: Abstract The number of STEM jobs is growing so rapidly that there are projected shortages of up to 3.5 million STEM workers in the United States in the next five years. Additionally, STEM fields, particularly engineering, lack diversity with female students and students of color underrepresented in STEM majors and STEM careers. For example, while 25% of the U.S. population are people of color, this group only constitutes 11% of STEM professionals. Thus, the engineering and workforce development and outreach activities of our Engineering Research Center (ERC) are designed to promote STEM pathways that both increase and diversify the pool of K-12 students seeking STEM careers. 2021 was a year filled with new challenges and opportunities for learning and outreach. This paper reports on a high school outreach program designed to promote interest in STEM, with a focus on biomedical engineering, that is part of a large Engineering Research Center. Initially intended to be an on-campus program with authentic research experiences, the outreach program was forced to move online due to the COVID-19 pandemic. This shift was accompanied by both expected and unexpected obstacles, as well as opportunities. This paper reports on the evaluation of the online engineering outreach program and discusses the strengths, weaknesses, and opportunities of online STEM outreach for high school students. The outreach program was a partnership of an Engineering Research Center with a local high school in a diverse, urban school district. 26 students were selected as participants, and 24 students attended at least a single meeting on Google Meets. Of these students, 71% were female and 29% were male, from various racial and ethnic backgrounds. Students were invited to participate in the six-week outreach program. We provided STEM kits for students to explore biomedical engineering from their homes. STEM kits included a wheat germ DNA extraction activity, BioRad's pGLO plasmid investigation kit, brine shrimp cryopreservation simulation lab, and a MudWatt bio-energy kit. The program was facilitated virtually by undergraduate and graduate students, with different graduate engineering and science researchers joining as guest speakers and experts each week. Expected obstacles included restructuring the length of daily time allotted for the program, as well as a summer schedule; student recruitment incentives; homelife interference; and a shift in planned lessons and investigations. Unforeseen obstacles included managing and encouraging student participation; supply delivery mishaps; and perhaps most significantly - rates of success for at-home experiments. While these obstacles required program facilitators to adjust the way they originally envisioned and planned the program, the shift to online instruction also resulted in opportunities. For example, a larger number of affiliated STEM graduate and postdoctoral students were willing to lead video chats with high school participants about their post-secondary education journey, more flexible hours for participants in high school with unique homelife situations, and lessons focused on professionalism and networking within the STEM fields.


A VIRTUAL WALKTHROUGH OF AN ENERGY CONSCIOUS SINGLE-FAMILY HOME

Abstract: With the advances in information technology (IT) over the last decade, the traditional teaching format of having an individual lecture to an audience has been supplemented, and in some cases, replaced by the rapid development and implementation of new distance learning methods.It is becoming increasingly important to develop web based 3-D visualization and animation to explain the various environmental conscious concepts and elements.The objective of this research paper was to showcase the energy saving elements used in an energy conscious singlefamily home design using 3D animation and virtual walkthrough.The model and animation can act as an excellent teaching tool to explain the various concepts integral to an energy conscious design, as the instructor would present the entire scheme in a virtual world so that the students can understand the concepts with more clarity and ease.The model would also help generate awareness among common people about energy conscious design as the web interface is easily accessible and user friendly.


Research with an Undergraduate Student: Using Entropy to Assess the Training of a Neural Network

Abstract: This paper reports on enhancing undergraduate education by doing research with students.The work presented was done with a freshman mathematics


Cognitive Computing Emulating Human Intelligence in AI Systems

Abstract: Cognitive computing represents a groundbreaking paradigm in artificial intelligence (AI) systems, aiming to emulate and replicate the intricate processes of human intelligence. This article explores the fundamental principles, methodologies, and applications of cognitive computing, shedding light on how it transforms traditional AI approaches. By drawing inspiration from human cognition, cognitive computing systems leverage advanced algorithms, neural networks, and machine learning techniques to emulate complex cognitive functions such as perception, reasoning, and problem-solving.


Development of Social Engagement Activities to Increase Student Participation in a Makerspace

Abstract: Jill's teaching, service, and research activities focus on enhancing the first year student experience by providing the foundational technical skills, student engagement opportunities, and professional skill development necessary to improve success in the major, with emphasis on supporting non-dominant


Locally Driven Innovation Through Teacher Peer Mentoring in Times of COVID-19: A Professional Learning Community in Rural El Salvador

Abstract: The global trend in governments’ responses to school closures due to the COVID-19 pandemic was to transition to online learning and provision of materials, which was also El Salvador’s response despite many students lacking access to the internet and adequate devices. Schooling was reduced to filling out worksheets, leading to ineffective learning that lacked meaning. This chapter presents, through primary data collected from teachers, parents, and students, an experience in rural El Salvador where a group of teachers dedicated themselves to ensuring that their students continued learning throughout the pandemic. Contrary to global curricular trends that prioritize improving reading speed over meaning, these teachers facilitated learning based on a sociocultural and communicative approach to literacy development. When government support arrived through centralized training, these teachers already had their own professional support community. This organizational capacity likely stemmed from their history during the Salvadoran Civil War and popular education practices developed in that period. This experience encourages governments, donors, and local organizations to: (1) promote literacy development rooted in meaning and socioemotional learning over reading speed; (2) change traditional centralized teacher training and turn towards learning communities; and (3) leverage the power of local history and relevant cultural factors for local solutions.


Many-to-One: Wearable Interactions

Abstract: While wearable technology in the form of clothes has been introduced for over a thousand years, it has been evolving in the last decade, with computer wearables to e-textile (Berzowska, 2005; Birringer & Danjoux, 2009; Capineri, 2014; Hartman, 2014; Lymberis & Paradiso, 2008; Ryan, n.d.; Sparacino et al., 1997). The motivation of this project is to bring focus on a unique audience engagement in musical performances and explore the many-to-one network connection of tangible interactions. This paper focuses on the creation of a wearable wristband, the use of network connection and the interaction with a data visualization used to create a unique and engaging experience for every audience member. This research project proposes a new creative space that will explore an inverted interaction, where audience members set the stage of the live performance.


Collider VR: Using Virtual Reality for Immersive Theatre and the Performing Arts

Abstract: This MRP explores the future of immersive theatre in virtual reality by examining the production Collider VR as a case study, produced by Single Thread Theatre and presented at the 2021 Festival of Live Digital Art and the 2021 rEvolver Festival. Collider VR utilized concepts from traditional theatre-making and applied them in a virtual environment to investigate four specific research questions. First, the project explored the use of the avatar as mask and the possibilities of embodiment in virtual space with hand gesture and head control. Second, it examined the potential for an ensemble to move as a collective in virtual space, as a chorus, and the applicability of the concept of a coryphaeus in VR. Third, the project examines how the logic of vector math, necessary to the structure of a virtual setting, can become integrated into the rehearsal lexicon and what effects changes to the virtual world during a performance can have. Finally, Collider VR explores the audience experience of virtual space and how designers can incorporate realities of that usage into immersive design. In addition to documenting the process of producing Collider VR and the conclusions reached in each of these gyms, this paper will contextualize the use of virtual reality technology in theatrical performance over the technology’s history to better forecast the future of virtual theatre.


Many-to-One: Wearable Interactions


THEAT[AR]: Augmented Reality Based Pre-visualization Of Theatrical Design Elements

Abstract: Theatrical Design is often a very fluid medium, changing until the very last minute, or even second. Even then, with all the care and time put into a production, once constructed on stage the end result often looks different than one might have imagined. Augmented Reality pre-visualization allows us to see theatrical designs in real spaces, as well as work with other designers to collaborate and compare elements for a more cohesive design. Up until this point pre-visualization has been popular, but most programs have not been designed for more than one type of design, and none have made use of Augmented Reality. This technology allows designers to communicate visually with other members of the creative team in a more definite manner, and allows for more confidence when entering the theatre or performance space. In turn this will save productions time and money, and allow for a better creative process, resulting in better art.


Nonperturbative Zou-Wang-Mandel effect

Abstract: The Zou-Wang-Mandel (ZWM) effect is a remarkable consequence of photon indistinguishability and continuous-variable entanglement in which an optical phase shift is imprinted on photonic modes associated with optical paths that do not pass through the phase-shift source. By bringing the canonical formalism of continuous-variable Gaussian states to bear on the mode structure of the ZWM experiment, we show that the physical consequence of implementing optical path identity is a renormalization of quadrature squeezing which governs the entanglement of four effective optical modes. Nonperturbative expressions for the ZWM interference patterns and normalized first-order coherence function are derived. Generalizations to $\mathcal{H}$-graph states with more than four modes directly follow from the general method used to analyze the minimal example. We show that a ZWM interferometer with a laser-seeded signal mode, which estimates an idler phase shift by detecting photons that did not propagate through the phase shift, exhibits an optimal sensitivity comparable to that of a laser-seeded SU(1,1) interferometer if the path identity is implemented with high fidelity.


Balancing Innovation and Regulation: A Comprehensive Analysis and Neural Network Approach to AI Copyright Challenges

Abstract:
This article explores emerging issues surrounding artificial intelligence (AI) and copyright through a two-pronged approach. First, it provides an extensive literature review analyzing government and industry strategies for addressing AI copyright concerns and evaluates their rationality. Second, it details experiments conducted using neural networks to examine relevant information and investigate image copyright challenges, assessing mainstream large language models’ efficacy in handling copyright matters.
 The literature review explores AI copyright perspectives of the United Kingdom, China, the European Union, and the United States. It finds that countries emphasize balanced regulation and innovation (UK), ethical content creation (China), regulating high-risk applications (EU), or principles like non-discrimination and privacy (US). However, comprehensive governance frameworks are needed to navigate AI’s ethical, social, and legal intricacies.
 The experimental portion trains a convolutional neural network on a dataset of 41 infringing and non-infringing image sets to identify copyright infringement. While achieving over 80% accuracy, enhancements through expanded training data, segmentation, and multi-domain detection could improve generalization. The paper concludes with an analysis advocating copyright adaptation for AI creations, measured protections for standalone AI works, and constructive policies from interdisciplinary dialogue.


Multidisciplinary Authoring – A Critical Foundation for Augmented Reality Systems in Training and Education

Abstract: including AI, AR, and IoT Systems.His most recent work involves designing an AR framework that provides just-in-time informatics for training, education and maintenance activities.


Questioning the policy of improving the quality of students through the research program and publication of scientific work at the postgraduate UIN Prof. Kh Saifuddin Zuhri Purwokerto

Abstract: Universities have an important role in training students in orientation and assimilation of knowledge through research and publication of scientific papers, especially in the era of society 5.0. Understanding and knowledge seem to be the only things that guarantee the social and economic progress of society that is not eroded over time. The Policy for Improving the Quality of Students through the Research Program and Publication of Postgraduate Student Scientific Work is an important initiative in advancing higher education and strengthening the role of students in their contribution to knowledge and scientific development. This research aims to describe how this program has contributed to improving the quality of postgraduate students, as well as its impact on the development of research and publication of students' scientific work.This research method uses a descriptive qualitative approach with literature study research as direct data. Data collection techniques by collecting published scientific papers from postgraduate students. To optimize the results of data collection, an article search was carried out from Google Scholar using the search terms names of postgraduate students who were used as research objects. Qualitative data analysis consists of three series: data reduction, data presentation and conclusion drawing. The research results show that: First, the condition of the quality of research activities carried out by Postgraduate students at UIN Prof. K.H. Saifudin Zuhri Purwokerto is doing well. Second, Policy and Implementation of Increasing the Quality of Students Based on Research and Scientific Writing at the UIN Postgraduate Program, Prof. K.H. Saifuddin Zuhri Purwokerto is stated in Circular Letter Number: 354/Un.19/D.PPs/PP.009/4/2022 concerning Publication of Scientific Work for Postgraduate Students at the State Islamic University of Professor Kiai Haji Saifuddin Zuhri Purwokerto in 2022. In its implementation, postgraduates participated supports this policy by organizing various supporting activities.


Quantifying non-stabilizerness via information scrambling

Abstract: The advent of quantum technologies brought forward much attention to the theoretical characterization of the computational resources they provide. A method to quantify quantum resources is to use a class of functions called magic monotones and stabilizer entropies, which are, however, notoriously hard and impractical to evaluate for large system sizes. In recent studies, a fundamental connection between information scrambling, the magic monotone mana and 2-Renyi stabilizer entropy was established. This connection simplified magic monotone calculation, but this class of methods still suffers from exponential scaling with respect to the number of qubits. In this work, we establish a way to sample an out-of-time-order correlator that approximates magic monotones and 2-Renyi stabilizer entropy. We numerically show the relation of these sampled correlators to different non-stabilizerness measures for both qubit and qutrit systems and provide an analytical relation to 2-Renyi stabilizer entropy. Furthermore, we put forward and simulate a protocol to measure the monotonic behaviour of magic for the time evolution of local Hamiltonians.


THE EFFECTS OF HYBRID CARDIAC REHABILITATION USING SYNCHRONIZED TELEHEALTH ON PATIENT

Abstract: BACKGROUND Cardiac rehabilitation is effective for secondary prevention of cardiac events and is endorsed by consensus guidelines but is limited by low enrollment and completion rates. Non-traditional delivery models that facilitate participation in CR are needed. The improving ATTENDance (iATTEND) to CR trial is an open label, single-site trial that compared standard facility-based only CR (FBCR) to hybrid CR (HYCR = patient-individualized combination of virtual CR delivered via synchronized telehealth and up to 12 in-facility CR sessions). METHODS Qualifying patients enrolling into early outpatient CR were consented and randomized 1:1 to HYCR (n=142) or FBCR (n=140). Primary outcome was total number of CR sessions completed within 6 mo. Secondary outcomes were percent of patients completing all 36 prescribed CR sessions and changes (after CR – before CR) in exercise capacity (peak oxygen uptake, VO2; 6-minute walk distance, 6MWD). Both attendance outcomes (alpha set at p&lt;0.05) and both exercise capacity outcomes [examined using the TOST (two, one sided tests) equivalence test] were adjusted for relevant, pre-specified co-variates. RESULTS Among patients enrolled into iATTEND (54% Black race, 33%; women, 34% &gt; 65 yr), neither the total number of CR sessions completed per patient (28.7 + 11.8 vs 27.6 + 11.8 visits, adjusted p = 0.41) nor percent of patients completing 36 sessions (58.5 + 4.1 vs 50.7 + 4.2%, adjusted p=0.32) were different between HYCR and FBCR, respectively. After CR, changes in peak VO2 (mL.kg.-1min-1) were 2.3 + 2.8 vs 1.9 + 2.8 (HYCR and FBCR, respectively; adjusted p=0.78) and changes in 6MWD (m) were 46 + 46 vs 55 + 53 (HYCR and FBCR, respectively; adjusted p=0.18); TOST indicated equivalency (p=.001) between groups for both measures of change in exercise capacity. Across 7,735 total CR sessions, there was one major adverse event (non-fatal stroke within 3 hr after CR in HYCR) and no falls requiring medical attention. CONCLUSION In a diverse cohort of patients that included understudied groups, our data suggest that HYCR is an effective non-traditional model to deliver CR. When compared to standard FBCR, HYCR results in similar patient attendance patterns and equivalent improvements in exercise capacity. (Funded by: National Heart, Lung, and Blood Institute)


Time is -ending: Sublexical information activates the horizontal mental time line in word processing.

Abstract: The mental time line (MTL) is a spatial continuum on which earlier events are generally associated with the left space and later events with the right space. Accordingly, past- and future-related words receive faster responses with, respectively, the left and the right hand. Yet, it is currently unclear whether the MTL is activated by the whole word or whether it can be triggered by more subtle sublexical cues, such as verb-endings, and whether the activation of this spatial continuum is an automatic phenomenon. The aim of this study is to test whether verb-endings do bring conceptual information that is in turn capable to activate the MTL and whether this activation holds also when the temporal information is not explicitly processed. We designed three experiments. In Experiment 1, consisting of a temporal categorization task, and in Experiment 2, consisting of a lexical decision task, we tested Italian tensed verbs (trov-avo "I found," trov-erò "I will find") and pseudo-verbs (trop-avo, trop-erò). Results of Experiment 1 showed that both tensed verbs and pseudo-verbs were spatially coded on the MTL. Results from Experiment 2 showed that the MTL is activated by the verb-endings also when temporal information was task-irrelevant (i.e., lexical decision task). Experiment 3 further clarified that the spatial-temporal congruency effect does not emerge during the evaluation of an inhomogeneous set of stimuli (i.e., when adding to the stimuli time-unrelated fillers). Overall, the present findings indicate that sublexical strings carry specific semantic information that comes into play in the generation of spatial-temporal associations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


A record of Late Cretaceous to early Paleogene Insular terrane accretion within the northern Cordillera: Insights from monazite petrochronology across the Kluane Schist, southwest Yukon, Canada

Abstract: The Kluane Schist is a metamorphosed package of siliciclastic and lesser calcareous rocks that lies between the inboard pericratonic Intermontane terranes and outboard Insular terranes of the North American Cordillera within Yukon, Canada. The metamorphic sequence of the Kluane Schist preserves a record of the tectono-thermal evolution and timing of Insular terrane accretion. Here we document the timing of metamorphism and deformation across the Kluane Schist using in situ laser ablation−inductively coupled plasma−mass spectrometry U-Th-Pb monazite petrochronology. Monazite-bearing samples collected across an inverted metamorphic sequence preserved in the northern regions of the Kluane Schist yield dates ranging from ca. 70 Ma to 55 Ma. Complementary phase equilibria modeling and thin section analysis indicate monazite grew between ∼450 °C and 3.0−3.5 kbar to ∼700−715 °C and 4.0−4.5 kbar, coeval with the development of the Kluane Schist’s inverted metamorphic sequence. Dating the four chemical zones preserved by monazite demonstrates its protracted growth during three distinct periods of garnet crystallization and breakdown, as well as coeval with melt generation. Our data illustrate that peak metamorphic conditions were reached at progressively younger ages with decreasing structural level within the Kluane Schist. Our results are consistent with Buchan-style metamorphism associated with the terminal accretion of the outboard Insular terranes and southwest-directed overriding of the inboard Yukon-Tanana terrane from ca. 70 Ma to 55 Ma. These findings are further congruous with a Late Cretaceous timing for the terminal accretion of the Insular terranes within southwest Yukon, facilitated by east-dipping subduction beneath a westward migrating North American continent.


Knowledge Collaboration Mechanism and Validity Assessment Based on Design Awards: A Case Study of Design Intelligence Award

Abstract: The essence of design award lies in the curation of outstanding design cases. Through the organizational mechanisms established by award organizers, it can serve as an exceptional platform for knowledge exchange, dissemination, and innovation. This paper introduces the concept of Design Award Knowledge Network (DAKN), taking Design Intelligence Award (DIA) for example, elucidates the collaborative mechanisms of knowledge dissemination and innovation within the DAKN, notably facilitated through the D·will Lecture and DI Project Workshops. Additionally, surveys were conducted among faculty and students participating in the series of events initiated by DIA. The findings underscore the prominent advantage of a ‘stimulative policy environment’ within this network, while revealing the need for improvements in the ‘accessibility of knowledge resources.’ This paper proposes that enhancing the vitality of DAKN rely on the diversification of knowledge, interdisciplinary approaches, and organizational collaboration. This entails the aggregation of talent from various universities, institutions, and enterprises to establish collaborative innovation teams. Through collaborative governance, the integration of diverse foundational capabilities, data information, and tool skills from various stakeholders should be orchestrated to achieve effectiveness in knowledge exchange.


Requirement Analysis of Smart Kitchen Dietary and Social Service System Based on Older Adults Mental Models

Abstract: Using kitchen life as an entry point and the theory of mental modelling, we carried out a study on the needs analysis of a smart kitchen diet and social service system, aiming to meet the new needs of the elderly in terms of health and social interaction. Through questionnaires and user interviews, we collected and analyzed the mental data of the elderly and created a mental model of “diet and social interaction” as well as user requirements. The Analytic Hierarchy Process was used to identify the importance of dietary and social needs, covering the four demand directions of diet and nutrition, health management, social interaction, and health awareness. This study highlights the potential of user-centred research methodology in the health and social care of older people and provides useful guidance and insights for the design of future smart kitchens and related products.


“Ask Aloud Protocol”: Toward a Participant-Driven Question-Asking Design Research Method

Abstract: In the field of problem formulation for design research, the conventional Think Aloud Protocol (TAP) has faced challenges in addressing complex problems such as Wicked Problems (WPs) over its two-decade history. Instead, the utilization of questions as a verbal tool has emerged as a more effective approach, producing insightful outcomes for intricate design challenges. Moreover, the largely untapped potential of participant-generated questions in design research holds promise for reducing subjective biases in designer-driven problem formulations and strengthening the role of participants. This study presents the Participant-Driven Ask Aloud Protocol (AAP) and employs it in spatial planning experiments at both QILI South Urban Village and the China World Mall’s Transit-Oriented Development (TOD) in Beijing, China. The analysis highlights AAP’s effectiveness in broadening the scope of generative design possibilities and facilitating a more precise and efficient identification of WPs in the design process.


Security Challenges and Economic Development in Emerging Economics: The Nigeria Experience

Abstract: The study focused on security challenges and economic development in emerging economic in Nigeria and solutions. The continuous rise in insecurity and deterioration in the economic development in Nigeria call for a concern among researchers and policy makers over the years. Nigeria has witnessed an unprecedented security challenge occasioned by the activities of militants in the South-South region, kidnappers in the south east, violent armed robbery in almost parts of the country, political assassination, ritual killings and more recently activities of Boko Haram in some parts of the northern region especially north east. These social menaces, when put together impinge on the security of lives and property of both Nigerian citizens and foreigners living or even trying to invest in the country. To ensure economic development in Nigeria therefore, the study recommends various measures of curbing security challenges including preventive community policing, human capital development, equitable distribution of resources as well as channeling of resources to frontline sectors of the economy among others. This paper recommend that Federal government should engage peace and conflict resolution experts in dealing with issues concerning Fulani herdsmen and Farmers crises and that ranching method of cattle rearing location across the country while the herdsmen should be given needed training for effective and efficient management of the ranching. Also, effective leadership and good governance as a panacea to solving problems of insecurity, unemployment, poverty, hunger, disease, among other negative indices.


Fast grid search: A grid search-inspired algorithm for optimizing hyperparameters of support vector regression

Abstract: This study presents a fast hyperparameter optimization algorithm based on the benefits and shortcomings of the standard grid search (GS) algorithm for support vector regression (SVR). This presented GS-inspired algorithm, called fast grid search (FGS), was tested on benchmark datasets, and the impact of FGS on prediction accuracy was primarily compared with the GS algorithm on which it is based. To validate the efficacy of the proposed algorithm and conduct a comprehensive comparison, two additional hyperparameter optimization techniques, namely particle swarm optimization and Bayesian optimization, were also employed in the development of models on the given datasets. The evaluation of the models' predictive performance was conducted by assessing root mean square error, mean absolute error, and mean absolute percentage error. In addition to these metrics, the number of evaluated submodels and the time required for optimization were used as determinative performance measures of the presented models. Experimental results proved that the FGS-optimized SVR models yield precise performance, supporting the reliability, validity, and applicability of the FGS algorithm. As a result, the FGS algorithm can be offered as a faster alternative in optimizing the hyperparameters of SVR in terms of execution time.


Complexity of Shakespeare’s Social Networks

Abstract: A recommendation of: Christian M. Thurn; Simone Sebben; Zoran Kovacevic Using Shakespeare to Answer Psychological Questions: Complexity and Mental Representability of Character Networks https://osf.io/3zsvn


Bitcoin_ML: An Efficient Framework for Bitcoin Price Prediction Using Machine Learning

Abstract: Econometrics can be used to understand and forecast price movements, assess market efficiency, and explore the factors influencing Bitcoin's value and adaptation. Econometrics is related to bitcoin in seven categories: price analysis and prediction, market efficiency, determination of Bitcoin prices, risk analysis, adaptation and network effects, causality tests, and simulation and stress. Testing these analyses can be invaluable for policymakers, investors, and financial institutions interested in the economics of digital currencies. Bitcoin price prediction in machine learning has many challenges that have deep roots in 2 main properties: cryptocurrencies and complexities in the Machine Learning models. Many problems are associated with machine learning for bitcoin price prediction, such as overfitting, data quality and availability, latent variables, model interpretability, computational complexity, dynamic adaptation, market manipulation, anomalies, data snooping bias risk, and time horizon mismatch. In the paper, we proposed an efficient framework for the prediction of bitcoin using nine different machine learning algorithms (linear Regression, random forest, adaboost, tree, KNN, gradient boosting, constant, neural network, SVM) on five different datasets. The results revealed that linear Regression emerged as the optimal model for the first data set. In the second data set, the random forest model demonstrated superior performance. The third data set exhibited the highest efficacy when the Adaboost model was employed. The fourth data set yielded the best outcomes with the random forest model, while linear Regression was the most effective choice for the final data set.


Octonion-based transform moments for innovative stereo image classification with deep learning

Abstract: Abstract Recent advances in imaging technologies have led to a significant increase in the adoption of stereoscopic images. However, despite this proliferation, in-depth research into the complex analysis of the visual content of these stereoscopic images is still relatively rare. The advent of stereoscopic imaging has brought a new dimension to visual content. These images offer a higher level of visual detail, making them increasingly common in a variety of fields, including medicine and industrial applications. However, exploiting the full potential of stereoscopic images requires a deeper understanding. By exploiting the capabilities of octonion moments and the power of artificial intelligence, we aim to break new ground by introducing a novel method for classifying stereoscopic images. The proposed method is divided into two key stages: The first stage involves data preprocessing, during which we strive to construct a balanced database divided into three distinct categories. In addition, we extract the stable Octonion Krawtchouk moments (SOKM) for each image, leading to a database of moment images with dimensions of 128 × 128 × 1. In the second step, we train a convolutional neural network (CNN) model using this database, with the aim of discriminating between different categories. Standard measures such as precision, accuracy, recall, F1 score, and ROC curves are used to assess the effectiveness of our method. These measures provide a quantitative assessment of the performance of our object classification approach for stereoscopic images.


Implementation Importance-Performance Analysis Method to Increase Customer Satisfaction of Honda Motorcycle Dealer using Text Mining

Abstract: Transportation is an essential aspect of people’s lives that plays an essential role in supporting economic, social, and cultural activities. Private transportation has become a basic need for Indonesian people, increasing yearly, especially for motorcycles. One of the biggest motorcycle brands in Indonesia is Honda, and West Java is one of the most significant contributors to motorcycle usage. However, their market share is decreasing through the years. It is essential to review customer satisfaction in purchasing Honda products both in terms of product and service through their customer journey in purchasing Honda products. Therefore, this research will discover the aspects customers need from online reviews, especially Google reviews. This research conducted case studies on Dealer Daya Adicipta Motora Bandung, Dealer Bintang Niaga Jaya Bogor, and Dealer Murni Motor II Bogor. Using Importance Performance Analysis (IPA) will recommend aspects that must be prioritized to improve. The first step is to identify the customer’s needs from Google reviews using non-negative matrix factorization (NMF) that produces matrix H and matrix W as the keywords for every aspect. Then, this research also calculates the sentiment for each review using a dependency tree and lexicon SenticNet5. With the output from NMF and sentiment, we will calculate the performance and importance levels. Ultimately, the Performance Level and Importance Level will be plotted into the graph and divided into four quadrants, with Quandrant A as the top priority for improvement. Eight aspects identified in Dealer Daya Adicipta Motora Bandung, the waiting room facility become the priority of improvement. Dealer Bintang Niaga Jaya Bogor has 7 aspects identified, and the services and buying experience become the top priority of improvement; meanwhile, there are 4 aspects identified in Dealer Murni Motor II Bogor, with the waiting time and waiting room facility become the top priority for improvement.


Speech Recognition for Smart AI Devices

Abstract: This article looks at the progress, challenges and future directions in the field of speech recognition for intelligent AI devices. Speech recognition technology has made significant progress over the years, enabling seamless interactions between humans and artificial intelligence. This article provides an in-depth analysis of modern speech recognition techniques used in smart AI devices and the potential applications and limitations of the technology. It also discusses the current challenges faced by developers and researchers and suggests possible solutions to overcome them. Keywords-Natural language processing Neat vs. scruff, Soft vs. hard computing, Narrow vs general AI, Rule-based approaches, Statistical and machine learning based methods, Deep Neural Networks, Deep learning models for speech recognition, Hybrid and ensemble approaches, Voice biometrics and authentication, Speech translation and language learning, Natural language processing improvement.


Associated Factors with Premenstrual Dysphoric Disorder among Undergraduate Medical Students of a Teaching Hospital

Abstract: Introduction: Premenstrual Dysphoric Disorder (PMDD) is a severe form of premenstrual syndrome in women which adversely affects social, academic, or work performance. The mechanisms underlying PMDD are still insufficiently known, the findings indicate a multifactorial genesis. Prevalence data of PMDD and its potential associated risk factors is sparse in Nepal. This study aimed to determine the associated factors with PMDD among undergraduate medical students of a teaching hospital in Nepal. Methods: This was a cross sectional study conducted among unmarried female medical students at KIST medical college and teaching hospital from 1st June to 30th July, 2023. Students were assigned from each of the four MBBS years (First, Second, Third, and Final) through random sampling till the desired sample size was achieved. Questionnaires about socio-demographic, menstrual, lifestyle variables and questionnaire for diagnosis of PMDD were administered. PMDD was diagnosed from the Diagnostic and Statistical Manual – fifth edition (DSM-5) of the American Psychiatric Association (APA). Data Analysis was performed using SPSS version 16. Data calculated as frequencies, mean, standard deviation and chi square test, independent t test with p-value of < 0.05 was considered as statistically significant. Results: The prevalence of PMDD according to DSM-V criteria was 41 (36.28%). PMDD was significantly associated with dysmenorrhea (P=0.030) and with intake of caffeinated beverages (P=0.034). Conclusion: PMDD is prevalent among medical students in Nepal. The potential associated risk factors are dysmenorrhea and intake of caffeinated beverages.


Economic Security and Gender Budgeting in Nigeria

Abstract: Gender budgeting has emerged as a crucial tool in addressing gender disparities and invariably promoting economic security. This article examines the intersection of economic security and gender budgeting in the Nigerian context. The study highlights the significant impact of gender- responsive budgeting on narrowing gender gaps in economic security. The article explores the concept of economic security within the framework of gender equality and sustainable development, emphasizing the importance of addressing gender-based discrimination as barriers to women's economic empowerment. Furthermore, the research examines the potential benefits of gender budgeting in achieving broader national development goals, including poverty reduction, increased productivity, and social cohesion. It underscores the need for a holistic approach that integrates gender considerations into all aspects of budget formulation, implementation, and monitoring. Ultimately, the work underscores that gender budgeting is not merely a fiscal exercise but a transformative strategy for building a more equitable and prosperous society in Nigeria.