Reviewerly/exHarmony huggingface dataset:
LoLTV: A Low Light Two-Wheeler Violation Dataset With Anomaly Detection Technique

Detecting traffic violations is essential for improving road safety, ensuring rule compliance, and maintaining smooth traffic flow. It also aids in holding violators accountable and supports data-driven decision-making for infrastructure enhancements. To address these challenges, the integration of AI-based methods for automated violation detection is increasingly vital, reducing the need for manual oversight. Low-light conditions pose additional difficulties, as violations become harder to detect. In this study, we created a novel dataset containing 1032 images with 1475 two-wheeler violations under low-light conditions. We propose a real-time deep learning system using YOLO-v8 for two-wheeler violation detection. Our system addresses the challenge of low-light conditions by incorporating a real-time low-light video enhancement module. Through comprehensive evaluations, our system has achieved an average precision of 98.2%, recall of 97.5%, and an accuracy of 97.05% when tested on our custom dataset. Notably, it successfully detected 172 out of 188 violations in the test dataset and exhibited 60% faster processing compared to other state-of-the-art methods. This suggests that our system not only outperforms existing methods on public datasets but also excels in terms of performance and accuracy when applied to the specifically constructed low-light traffic dataset. Furthermore, our system’s practical scalability is evident through its integration with multiple devices and CCTV systems.
ACTIVA: Innovation in Quality of Care for Nursing Homes Through Activity Recognition

There are an increasing number of elderly people with various pathologies and care needs, making nursing homes one of the most suitable places for them to live. The demand for nursing homes is expected to increase further in the coming years due to the growing life expectancy. Human activity recognition (HAR) approaches are an excellent resource that can be deployed in nursing homes to detect an inhabitant’s activity through sensors in the environment. Despite the many theoretical approaches to HAR proposed in the literature, there is a significant lack of implementation in real environments. In this paper, we present an activity recognition approach deployed to monitor a floor of a nursing home using a mobile app called ACTIVA, which allows caregivers to track inhabitants and be notified of anomalies in real-time. The ACTIVA system relies on successful tools proposed for HAR, such as the use of fuzzy logic techniques, linguistic protoforms, and temporal windows to infer the location and activity of each inhabitant in a multi-occupancy environment. These tools have proven effective in inferring inhabitant activity and location, allowing for more precise and detailed monitoring. ACTIVA provides multiple benefits in terms of providing more information on the activities performed by residents, reducing stress for caregivers, and increasing safety for the residents being monitored. To evaluate the impact of ACTIVA app, we gathered feedback from different stakeholders and found overall positive satisfaction levels. Our approach represents a significant step towards the practical implementation of HAR approaches in real nursing home environments.
Hyperparameter Optimization of Long Short Term Memory Models for Interpretable Electrical Fault Classification

The reliability of the model significantly affects early detection and accurate classification of electrical faults. In this study, a Long Short Term Memory based fault classification model was developed for the Power System Machine Learning benchmark dataset, focusing on improving reliability by increasing interpretability. First, novel metrics are introduced to measure model interpretability. These interpretability metrics are uniquely defined based on the disentanglement of the fault classification factors. Subsequently, hyperparameter optimization was performed using multi-objective Bayesian Optimization to determine the optimal model architecture. The objective of optimization is to maximize interpretability and classification accuracy. The Pareto-optimal solution presents different model architectures with varying accuracy and interpretability trade-offs. Finally, the manifestation of interpretability in terms of subsequences is studied using Shapley Additive Explanations. The impact of class representation and architectural parameters on interpretability was also analyzed. Furthermore, the most accurate model in the Pareto front achieved highly competitive accuracy for the benchmark data.
ViolinTalk: Violin Robots as Internet of Things Devices

In recent years, the application of intelligent robots in the field of music has gradually emerged. Among string instruments, the violin stands as an unparalleled musical marvel, representing the soprano voice. Countless composers have penned masterpieces for this cherished instrument. This paper proposes ViolinTalk, a system for controlling XY-type violin robots as Internet of Things (IoT) devices. ViolinTalk enhances the performance of robots through calibration and testing. ViolinTalk has improved the software of violin robots with an IoT-based modular design, resulting in a better development environment. In this environment, we have made key contributions, including: (1) The development of two control modes for the bowing servo motors, comprising displacement-based control and time-based control; (2) Utilizing bowing speed control to introduce variations in volume and tone, enriching the performance effects; (3) Analyzing errors in playing speed and establishing correction parameters for future use; (4) Analyzing the robot’s mechanism to enhance its stability and durability. ViolinTalk also leverages IoT technology to facilitate seamless connections between different music instruments for ensemble performances. Multiple robots have been successfully integrated into a string orchestra, featuring two violins, one viola, and one cello, capable of performing standard chamber music. This development has resulted in the acquisition of a total of 8 patents from Taiwan.
Transformer-Based Feature Aggregation and Stitching Network for Crowd Counting

With the rapid development of society, crowded scenes can be seen almost everywhere. Therefore, it is important to accurately predict the number and density distribution of people in those crowded regions by utilizing image analysis. In recent years, most of those studies based on various deep learning technologies for pedestrian image analysis have been based on mature convolution neural networks (CNN). Nowadays, vision transformer has demonstrated its competitive performance compared with CNNs in many computer vision domains, and provides a novel idea for density distribution in image. In this paper, we modify the Swin-Transformer and integrate CNN to propose a feature aggregation and stitching network (FASNet), which effectively improves the counting accuracy. The hierarchical vision transformer backbone captures the global multi-scale features of the image, and encode the interaction information among different pedestrians in the deep network. Feature Aggregation Module (FAM) is used to fuse the deep and shallow features, and then Density Regression Module (DRM) upsamples the output of FAM and finally produces the predicted crowd density map and the final count. In addition, we propose the Feature Stitching Mechanism (FSM) to cope with the feature damage or loss caused by image cropping during the model testing. The experimental results on three benchmark datasets (UCF_CC_50, UCF_QNRF, Shanghaitech) demonstrate the effectiveness of our proposed scheme.
Fairness-Aware Regression Robust to Adversarial Attacks

In this paper, we take a first step towards answering the question of how to design fair machine learning algorithms that are robust to adversarial attacks. Using a minimax framework, we aim to design an adversarially robust fair regression model that achieves optimal performance in the presence of an attacker who is able to add a carefully designed adversarial data point to the dataset or perform a rank-one attack on the dataset. By solving the proposed nonsmooth nonconvexnonconcave minimax problem, the optimal adversary as well as the robust fairness-aware regression model are obtained. For both synthetic data and real-world datasets, numerical results illustrate that the proposed adversarially robust fair models have better performance on poisoned datasets than other fair machine learning models in both prediction accuracy and group-based fairness measure.
Whole-Body Ergodic Exploration With a Manipulator Using Diffusion

This letter presents a whole-body robot control method for exploring and probing a given region of interest. The ergodic control formalism behind such an exploration behavior consists of matching the time-averaged statistics of a robot trajectory with the spatial statistics of the target distribution. Most existing ergodic control approaches assume the robots/sensors as individual point agents moving in space. We introduce an approach that decomposes the whole-body of a robotic manipulator into multiple kinematically constrained agents. Then, we generate control actions by calculating a consensus among the agents. To do so, we use an ergodic control formulation called heat equation-driven area coverage (HEDAC) and slow the diffusion using the non-stationary heat equation. Our approach extends HEDAC to applications where robots have multiple sensors on the whole-body (such as tactile skin) and use all sensors to optimally explore the given region. We show that our approach increases the exploration performance in terms of ergodicity and scales well to real-world problems. We compare our method in kinematic simulations with the state-of-the-art and demonstrate the applicability of an online exploration task with a 7-axis Franka Emika robot.
Multi-Manifold Attention for Vision Transformers

Vision Transformers are very popular nowadays due to their state-of-the-art performance in several computer vision tasks, such as image classification and action recognition. Although their performance has been greatly enhanced through highly descriptive patch embeddings and hierarchical structures, there is still limited research on utilizing additional data representations so as to refine the self-attention map of a Transformer. To address this problem, a novel attention mechanism, called multi-manifold multi-head attention, is proposed in this work to substitute the vanilla self-attention of a Transformer. The proposed mechanism models the input space in three distinct manifolds, namely Euclidean, Symmetric Positive Definite and Grassmann, thus leveraging different statistical and geometrical properties of the input for the computation of a highly descriptive attention map. In this way, the proposed attention mechanism can guide a Vision Transformer to become more attentive towards important appearance, color and texture features of an image, leading to improved classification and segmentation results, as shown by the experimental results on well-known datasets.
Multiuser Entanglement Distribution in Quantum Networks Using Multipath Routing

Quantum networks facilitate numerous applications including secure communication and distributed quantum computation by performing entanglement distribution. For some multi-user quantum applications access to a shared multipartite state is required. We consider the problem of designing protocols for distributing such states, at an increased rate. For this, we propose three protocols that leverage multipath routing to increase the distribution rate for multi-user applications. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation. Simulation results show that the developed protocols achieve an exponential increase in the distribution rate of multipartite states compared to single path routing techniques, with a maximum increase of four orders of magnitude for the cases studied. Further, the relative increase in distribution rate was also found to improve for larger sets of users. When the protocols were tested in scaled-down real-world topologies, it was found that topology had a significant effect on the multipartite state distribution rates achieved by the protocols. Finally, we found that the benefits of multipath routing are maximum for short quantum memory decoherence times and intermediate values of entanglement generation probability. Hence, the protocols developed can benefit NISQ quantum network control and design.
Not All Patches Are Equal: Hierarchical Dataset Condensation for Single Image Super-Resolution

Although the performance of single image super-resolution (SR) has been significantly improved with deep neural networks, existing methods commonly require millions of iterations for training, which not only limits their training efficiency, but also causes considerable energy consumption. In this paper, we comprehensively study the redundancy of existing training datasets and reveal that not all patches are equal for SR network training. We observe that a large percentage of patches with low textures or similar textures lead to high computation costs but make low contributions to SR performance. Then, we propose a dataset condensation method to remove these redundant patches hierarchically. Extensive experiments demonstrate that our dataset condensation method can effectively reduce the redundancy of SR datasets with a 90% condensation rate on DIV2K. With our condensed dataset, baseline networks can achieve significant improvement in terms of training efficiency while maintaining competitive accuracy. Codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/QingtangDing/DCSR</uri> .
A CA-Based Weighted Clustering Adversarial Network for Unsupervised Domain Adaptation PolSAR Image Classification

With the development of science and technology, although more and more polarimetric synthetic aperture radar (PolSAR) data are collected, marking PolSAR data still requires a lot of costs. Moreover, the datasets between different domains have the class distribution shift problem, which reduces the reusability of labeled samples between cross-domain images. To address this issue, this article proposed an unsupervised domain adaptation (UDA) network based on coordinate attention (CA) and weighted clustering. Firstly, an adversarial UDA network with a bi-classifier is introduced to eliminate the problem of class distribution shift and achieve alignment of data distribution between different domains. Secondly, the CA mechanism is introduced to select important features to enhance the utilization of spatial information among pixels. Finally, to improve the utilization of semantic and classification information of the target domain, and to align same class samples, a weighted clustering algorithm is introduced. Experimental results show that compared with the existing UDA method, the proposed method can achieve the better PolSAR image classification.
Legislating Against Misinformation: Lessons from Australia’s Misinformation Bill

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Strategies for Optimizing Policy Outcomes through Machine Learning: A Case Study on Korean R&amp;amp;D Project Assessment

When employed in artificial intelligence (AI) applications, machine learning (ML) allows AI to recognize patterns in data and predict future outcomes based on these patterns, supporting decision-making. This additionally allows ML to be utilized in the formulation of industrial policies (IPs). However, overreliance on AI for all pol-icies presents several challenges. To harness AI effectively, it is essential to ensure logical clarity and measurability that can be digitally transformed into data, along with the availability of a sufficient amount of data to ensure accuracy and reliability. On the other hand, it is more difficult to use AI in IP design when policies must take into normative as well as economic considerations, or when it becomes necessary to define new norms. These are typically cases in which simple pattern recognition fails to grasp the complexity of various issues at play, making the immediate application of AI application impossible.For instance, situations in which numerous stakeholders hold diverse perspectives can make it challenging to establish clear policy objectives. Additionally, any given problem may include some issues that are fundamentally subjective or normative, and thus incapably of being quantified or measured. This also presents challenges to the effective use of AI.This paper explores the ways in which machine learning (ML) techniques in the field of object classification can contribute to formulating industrial policies. Thank you for reading this abstract of a report from the Korea Institute for Industrial Economics and Trade!Visit us on YouTube: https://www.youtube.com/watch?v=Q36v30l5CV0Visit us on Instagram: https://www.instagram.com/worldkiet/Visit our website: http://www.kiet.re.kr/en
Feasibility of designing a cloud-based platform for entrepreneurship training of college students

Abstract This paper aims to improve college students’ entrepreneurial abilities as its main goal. A platform for college students’ entrepreneurship development is constructed using cloud technology, and the entrepreneurship data on the platform is classified and divided by combining it with the KNN algorithm. The algorithm is optimized for distance calculation and distance sorting stages to reduce inter-program dependencies and shorten the program execution time. The distance between matrices is calculated by calling the functions in the cublas function library. The experimental data are used to analyze the feasibility of entrepreneurship training for college students. The results show that the KNN algorithm can be applied to data processing in cloud computing. When the data dimension is 128 and the number of sample points is 215, the algorithm can get a maximum speedup of 1.52 times. The server configuration of the platform needs to be in a 4-way dual-core or 4-core to ensure the platform’s normal operation. This study is beneficial in promoting the improvement and development of entrepreneurial skills for college students.
Characteristics of teaching English and American literature based on multimodality under language acquisition theory

Abstract First, This paper constructs a multimodal teaching model based on teaching contents, activities, learning environment and learning means and examines measures to illustrate teaching English and American literature under language acquisition theory. Secondly, it uses Fourier transform and discrete cosine transform to preprocess audio signals, extracts emotional features from MFCC according to the bidirectional long and short-term memory network algorithm of attention, proposes to use representational learning and attention mechanism to focus on the detected emotional regions, and analyzes the multimodalized teaching of English and American literature course and the emotion of teaching speech reading in multimodal English and American literature. The results showed that on the multimodalized English and American literature course, the experimental class had mean scores of 21.81, 8.04, 6.71, and 7.08 on each test, respectively, and the experimental class outperformed the control class on all tests of English and American literature instruction. On the analysis of speech reading aloud emotion evaluation, the model assessment score and teacher assessment error were 2.3, the correlation coefficient was 0.6982, and the overall performance of the model of speech reading aloud emotion analysis of English and American literature was relatively reliable. Students' literary emotional literacy is developed through this study, and a multidimensional interpretation of English and American literature is achieved.
The Causes, Connotation and Contemporary Value of Yan’an Spirit under the Construction of Digital Cognitive Mapping

Abstract This paper develops a cognitive map of the Yan’an spirit that is based on the performance characteristics of the Yan’an spirit. An entity boundary perception model based on multi-task learning is proposed, and the traditional named entity recognition task is decomposed into entity boundary perception and entity classification, which completes the entity recognition based on MTBA-NER. The entity-oriented relationship extraction is based on the Bi-LSTM network to classify the expression of features, and the softmax function is used to extract the classification results. The political value has always been the value of Yan’an spirit, which has a value of 0.274 today, while the economic value, cultural value and educational value have a value of 0.322, 0.367 and 0.245, respectively. The analysis of the connotation of the Yan’an spirit based on cognitive mapping can help reflect and promote its value in contemporary times.
ML-DSTnet: A Novel Hybrid Model for Breast Cancer Diagnosis Improvement Based on Image Processing Using Machine Learning and Dempster–Shafer Theory

Medical intelligence detection systems have changed with the help of artificial intelligence and have also faced challenges. Breast cancer diagnosis and classification are part of this medical intelligence system. Early detection can lead to an increase in treatment options. On the other hand, uncertainty is a case that has always been with the decision-maker. The system's parameters cannot be accurately estimated, and the wrong decision is made. To solve this problem, we have proposed a method in this article that reduces the ignorance of the problem with the help of Dempster-Shafer theory so that we can make a better decision. This research on the MIAS dataset, based on image processing machine learning and Dempster-Shafer mathematical theory, tries to improve the diagnosis and classification of benign, malignant masses. We first determine the results of the diagnosis of mass type with MLP by using the texture feature and CNN. We combine the results of the two classifications with Dempster-Shafer theory and improve its accuracy. The obtained results show that the proposed approach has better performance than others based on evaluation criteria such as accuracy of 99.10%, sensitivity of 98.4%, and specificity of 100%.
Transfer learning across datasets with different input dimensions: An algorithm and analysis for the linear regression case

With the development of new sensors and monitoring devices, more sources of data become available to be used as inputs for machine learning models. These can on the one hand help to improve the accuracy of a model. On the other hand, combining these new inputs with historical data remains a challenge that has not yet been studied in enough detail. In this work, we propose a transfer learning algorithm that combines new and historical data with different input dimensions. This approach is easy to implement, efficient, with computational complexity equivalent to the ordinary least-squares method, and requires no hyperparameter tuning, making it straightforward to apply when the new data is limited. Different from other approaches, we provide a rigorous theoretical study of its robustness, showing that it cannot be outperformed by a baseline that utilizes only the new data. Our approach achieves state-of-the-art performance on 9 real-life datasets, outperforming the linear DSFT, another linear transfer learning algorithm, and performing comparably to non-linear DSFT.1