Reviewerly/exHarmony huggingface dataset:
ProxiFit

Although many works bring exercise monitoring to smartphone and smartwatch, inertial sensors used in such systems require device to be in motion to detect exercises. We introduce ProxiFit, a highly practical on-device exercise monitoring system capable of classifying and counting exercises even if the device stays still. Utilizing novel proximity sensing of natural magnetism in exercise equipment, ProxiFit brings (1) a new category of exercise not involving device motion such as lower-body machine exercise, and (2) a new off-body exercise monitoring mode where a smartphone can be conveniently viewed in front of the user during workouts. ProxiFit addresses common issues of faint magnetic sensing by choosing appropriate preprocessing, negating adversarial motion artifacts, and designing a lightweight yet noise-tolerant classifier. Also, application-specific challenges such as a wide variety of equipment and the impracticality of obtaining large datasets are overcome by devising a unique yet challenging training policy. We evaluate ProxiFit on up to 10 weight machines (5 lower- and 5 upper-body) and 4 free-weight exercises, on both wearable and signage mode, with 19 users, at 3 gyms, over 14 months, and verify robustness against user and weather variations, spatial and rotational device location deviations, and neighboring machine interference.
ImpersonatAR: Using Embodied Authoring and Evaluation to Prototype Multi-Scenario Use Cases for Augmented Reality Applications

Abstract Prototyping use cases for augmented reality (AR) applications can be beneficial to elicit the functional requirements of the features early-on, to drive the subsequent development in a goal-oriented manner. Doing so would require designers to identify the goal-oriented interactions and map the associations between those interactions in a spatio-temporal context. Pertaining to the multiple scenarios that may result from the mapping, and the embodied nature of the interaction components, recent AR prototyping methods lack the support to adequately capture and communicate the intent of designers and stakeholders during this process. We present ImpersonatAR, a mobile-device-based prototyping tool that utilizes embodied demonstrations in the augmented environment to support prototyping and evaluation of multi-scenario AR use cases. The approach uses: (1) capturing events or steps in the form of embodied demonstrations using avatars and 3D animations, (2) organizing events and steps to compose multi-scenario experience, and finally (3) allowing stakeholders to explore the scenarios through interactive role-play with the prototypes. We conducted a user study with ten participants to prototype use cases using ImpersonatAR from two different AR application features. Results validated that ImpersonatAR promotes exploration and evaluation of diverse design possibilities of multi-scenario AR use cases through embodied representations of the different scenarios.
Comparison Among Double-Stream CNN and Local Feature Fusion of CNN in the Facial Expression Recognition Field

In recent years, with the development of deep learning, facial expression recognition has become a hot research direction. In order to improve the accuracy of facial expression recognition, this paper focuses on comparing two improved algorithms based on convolution neural network (CNN), namely, double-stream CNN and CNN local fusion feature facial expression recognition. The dual-stream CNN algorithm uses a variety of advanced technologies for preprocessing and feature extraction; Local feature fusion of CNN is to perform complex processing on local features and finally optimize feature fusion weights. In this paper, the accuracy of face recognition of the two algorithms on CK+ and JAFFE data sets is 98.81% and 96.05%; 94.56%, and 97.08%, respectively. The recognition rate of these two algorithms shows that the optimization effect is very good. By comparing the similarities and differences of the two algorithms, it provides ideas and directions for further improving the accuracy and algorithm.
Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

In this paper, we present a comprehensive framework for assessing the explainability of various XAI methods, such as LIME and SHAP, across multiple datasets and machine learning models, with the ultimate goal of creating a unified multidimensional explainability score. Our methodology focuses on three key aspects of explainability: fidelity, simplicity, and stability. We leverage benchmarking experiments to systematically evaluate these aspects and use the insights gained to construct an offline knowledge base. This knowledge base captures the explainability scores for each registered model and serves as a valuable resource for context-dependent evaluation of explainability. By analyzing the complementary characteristics and metadata of AI models, datasets, and XAI methods, the knowledge base will enable the estimation of explainability scores for previously unseen datasets and models. Properties like fidelity, simplicity, and stability may vary significantly based on the dataset, underlying model, and domain expertise of the end user. We demonstrate our framework by applying it to three open-source datasets, discussing the impli-cations of the obtained results in relation to the characteristics of the datasets. Our work contributes to the growing field of XAI by providing a robust and versatile tool for evaluating and comparing the explainability of various XAI methods, ultimately supporting the development of more transparent and trustworthy AI systems.
Research on Path Planning Based on NAO Robot

With the great progress made in the study of robots, the path planning of robots in research has become an important problem. The goal is for NAO to walk smoothly and plan suitable routes to complete the tasks given to it by people. The ant colony algorithm and genetic algorithm were used, and the environment was constructed by the raster method, and the feasibility of the two methods was analyzed by simulation experiment using Matlab software and the optimal results were compared. The path planning experiment using the NAO bipedal robot first makes a simple arrangement of the surrounding environment where the robot is walking, and uses choregraphe software to connect to the NAO robot and control NAO to complete the return path planning experiment.
Directional characteristics of two gamelan gongs

The distinctive geometry and structural characteristics of Balinese gamelan gongs lead to the instrument's unique sound and musical style. This work presents high-resolution directivity measurements of two types of gamelan gongs to quantify and better understand their acoustic behavior. The measured instruments' structural modes clearly impact their far-field directivity patterns, with the number of directional lobes corresponding to the associated structural mode shapes. Many of the lowest modes produce dipole-like radiation, with the dipole moment determined by the positions of the nodal and antinodal regions. Higher modes exhibit more complex patterns with multiple lobes often correlated with the location and number of antinodal regions on the gong's edge. Directivity indices correspond to dipole radiation at low frequencies and quadrupole radiation at intermediate and higher frequencies. Symmetry analysis confirms that the gong's rim significantly impacts the resultant directivity.
Explainable Artificial Intelligence to Enhance Data Trustworthiness in Crowd-Sensing Systems

Around the world there has been an advancement of IoT edge devices, that in turn have enabled the collection of rich datasets as part of the Mobile Crowd Sensing (MCS) paradigm, which in practice is implemented in a variety of safety critical applications. In spite of the advantages of such datasets, there exists an inherent data trustworthiness challenge due to the interference of malevolent actors. In this context, there has been a great body of proposed solutions which capitalize on conventional machine algorithms for sifting through faulty data without any assumptions on the trustworthiness of the source. However, there is still a number of open issues, such as how to cope with strong colluding adversaries, while in parallel managing efficiently the sizable influx of user data. In this work we suggest that the usage of explainable artificial intelligence (XAI) can lead to even more efficient performance as we tackle the limitation of conventional black box models, by enabling the understanding and interpretation of a model's operation. Our approach enables the reasoning of the model's accuracy in the presence of adversaries and has the ability to shun out faulty or malicious data, thus, enhancing the model's adaptation process. To this end, we provide a prototype implementation coupled with a detailed performance evaluation under different scenarios of attacks, employing both real and synthetic datasets. Our results suggest that the use of XAI leads to improved performance compared to other existing schemes.
SignRing

Sign language is a natural language widely used by Deaf and hard of hearing (DHH) individuals. Advanced wearables are developed to recognize sign language automatically. However, they are limited by the lack of labeled data, which leads to a small vocabulary and unsatisfactory performance even though laborious efforts are put into data collection. Here we propose SignRing, an IMU-based system that breaks through the traditional data augmentation method, makes use of online videos to generate the virtual IMU (v-IMU) data, and pushes the boundary of wearable-based systems by reaching the vocabulary size of 934 with sentences up to 16 glosses. The v-IMU data is generated by reconstructing 3D hand movements from two-view videos and calculating 3-axis acceleration data, by which we are able to achieve a word error rate (WER) of 6.3% with a mix of half v-IMU and half IMU training data (2339 samples for each), and a WER of 14.7% with 100% v-IMU training data (6048 samples), compared with the baseline performance of the 8.3% WER (trained with 2339 samples of IMU data). We have conducted comparisons between v-IMU and IMU data to demonstrate the reliability and generalizability of the v-IMU data. This interdisciplinary work covers various areas such as wearable sensor development, computer vision techniques, deep learning, and linguistics, which can provide valuable insights to researchers with similar research objectives.
VidAdapter

Video lectures are increasingly being used by learners in a ubiquitous manner. However, existing video designs are not optimised for ubiquitous use, creating the need to adapt the style of these videos to meet the constraints of the learning platform and context of use. Our formative study with experienced video editing users, however, found that performing these adaptations using traditional video editors can be a challenging and time-consuming task. We developed VidAdapter, a tool that facilitates lecture video adaptation by allowing direct manipulation of the video content. For this, VidAdapter automatically extracts meaningful elements from the video, enables spatial and temporal reorganisation of the elements, and streamlines the modification of an element's visual appearance. We demonstrate the capabilities and specific use cases of VidAdapter within the domain of adapting existing blackboard lecture videos for on-the-go learning on Optical Head-Mounted Displays. Our evaluation of the tool with experienced video editing users revealed that VidAdapter was strongly preferred over traditional approaches and can improve the efficiency of the adaptation process by over 53% on average.
AttFL

This work presents AttFL, a federated learning framework designed to continuously improve a personalized deep neural network for efficiently analyzing time-series data generated from mobile and embedded sensing applications. To better characterize time-series data features and efficiently abstract model parameters, AttFL appends a set of attention modules to the baseline deep learning model and exchanges their feature map information to gather collective knowledge across distributed local devices at the server. The server groups devices with similar contextual goals using cosine similarity, and redistributes updated model parameters for improved inference performance at each local device. Specifically, unlike previously proposed federated learning frameworks, AttFL is designed specifically to perform well for various recurrent neural network (RNN) baseline models, making it suitable for many mobile and embedded sensing applications producing time-series sensing data. We evaluate the performance of AttFL and compare with five state-of-the-art federated learning frameworks using three popular mobile/embedded sensing applications (e.g., physiological signal analysis, human activity recognition, and audio processing). Our results obtained from CPU core-based emulations and a 12-node embedded platform testbed shows that AttFL outperforms all alternative approaches in terms of model accuracy and communication/computational overhead, and is flexible enough to be applied in various application scenarios exploiting different baseline deep learning model architectures.
HMGAN

Wearable Human Activity Recognition (WHAR) is an important research field of ubiquitous and mobile computing. Deep WHAR models suffer from the overfitting problem caused by the lack of a large amount and variety of labeled data, which is usually addressed by generating data to enlarge the training set, i.e., Data Augmentation (DA). Generative Adversarial Networks (GANs) have shown their excellent data generation ability, and the generalization ability of a classification model can be improved by GAN-based DA. However, existing GANs cannot make full use of the important modality information and fail to balance modality details and global consistency, which cannot meet the requirements of deep multi-modal WHAR. In this paper, a hierarchical multi-modal GAN model (HMGAN) is proposed for WHAR. HMGAN consists of multiple modal generators, one hierarchical discriminator, and one auxiliary classifier. Multiple modal generators can learn the complex multi-modal data distributions of sensor data. Hierarchical discriminator can provide discrimination outputs for both low-level modal discrimination losses and high-level overall discrimination loss to draw a balance between modality details and global consistency. Experiments on five public WHAR datasets demonstrate that HMGAN achieves the state-of-the-art performance for WHAR, outperforming the best baseline by an average of 3.4%, 3.8%, and 3.5% in accuracy, macro F1 score, and weighted F1 score, respectively.
Local Mean k-Nearest Neighbor Classifier with Gaussian-Kernel Distance Function

KNN is a simple but efficient machine learning classification algorithm that has an irreplaceable place in industry. In this paper, we propose a kernel-based local mean k-nearest neighbor classifier algorithm (KLMKNN) that not only considers the k nearest samples from each class of the samples to be classified, but also introduces a kernel mapping approach to compute data points in a high-dimensional space as a way to extract better location information. To verify the feasibility and efficiency of our proposed algorithm, we compared it with related algorithms on several datasets, and the results showed that the accuracy of our proposed algorithm is 0.7538 in all datasets, which is an improvement compared to KNN (0.7249), KKNN (0.7215) and LMKNN (0.7445), which is a good proof that our proposed algorithm is effective.
Improved RM-YOLO Model Based on Transformer for Intelligent Monitoring Meters

Pointer meters are still used in large numbers in power engineering because of their reliability in complex electromagnetic environments, but the complex electromagnetic and machinery environment of substations causes huge problems for intelligent identification to identify the meter. In order to solve the current problem of automatic recognition of pointer meters in substations, this paper proposes an improved RM-YOLO model based on Transformer. By introducing the Transformer module in the backbone, it enhanced the model's ability to integrate shallow and deep feature information. The fusion of multi-scale feature enhances the dynamic adjustment accuracy of meter edge feature at different scales. To solve the increased data volume and reduced computational speed caused by the introduction of the Transformer, the RMNet module is then introduced to replace the RepVGG module, significantly reducing the complexity of the model, effectively improving its recognition speed and further enhancing its ability to mine deep data. The method has been validated to achieve an average target recognition accuracy of 94.7%, enabling effective and accurate identification of pointer meters by inspection robots.
Designing Effective Mathematics Learning: Augmented Reality Framework Development via the Fuzzy Delphi Method

In the current era where Augmented Reality (AR) is making significant impact on various aspects of education, its specific role in mathematics education is of crucial significance. A quantitative research design was employed, utilizing the Fuzzy Delphi technique to collate opinions from a diverse group of 17 experts drawn from fields such as education, User Interface/User Experience (UI/UX), mobile app development, AR/VR, mathematics, computer science, and educational technology. The data analysis indicated a consensus among the experts on the key elements necessary for creating effective AR applications in mathematics education, as evidenced by values exceeding 75% agreement, with a maximum acceptable dispersion of opinions (d) ≤ 0.2, and a minimum consensus measure (fuzzy score, ≥ α-cut value larger than 0.5). The findings underscore the importance of the identified components in the design and development of AR applications for mathematics education and signal significant potential for promoting technology-enhanced active learning in the educational field.
Impacts of Remedial Action Schemes on Power System Resilience After Large-Scale Earthquakes

Damage from an earthquake can devastate a region and have a broad impact on the interconnected system. Significant loss of load and generation following an earthquake has the potential to cause failure of an entire interconnected system. This paper builds off previous Quasi-Steady-State (QSS) simulation results with analysis of potential Remedial Action Schemes (RAS) based on their ability to enhance grid resilience following a major earthquake. Using a synthetic model of the North America western power grid, a Monte Carlo QSS simulation is used to estimate the damage, recovery and the impact of RAS on load served following a major earthquake off the west coast of the United States. Three relatively simple RAS are investigated: load shedding, intentional islanding, and a combination of both are evaluated against a base case with no preventative actions taken. Results show that in heavy damage scenarios (95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentile), all RAS outperform the base case. The combination of islanding and load shedding improves the resilience of load served by 92%. In low-to-moderate damage scenarios, 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> and 50 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> percentiles, RAS shows similar performance to the base case.
A Multi-Task Learning Framework for Drone State Identification and Trajectory Prediction

The rise of unmanned aerial vehicle (UAV) operations, as well as the vulnerability of the UAVs' sensors, has led to the need for proper monitoring systems for detecting any abnormal behavior of the UAV. This work addresses this problem by proposing an innovative multi-task learning framework (MLF-ST) for UAV state identification and trajectory prediction, that aims to optimize the performance of both tasks simultaneously. A deep neural network with shared layers to extract features from the input data is employed, utilizing drone sensor measurements and historical trajectory information. Moreover, a novel loss function is proposed that combines the two objectives, encouraging the network to jointly learn the features that are most useful for both tasks. The proposed MLF-ST framework is evaluated on a large dataset of UAV flights, illustrating that it is able to outperform various state-of-the-art baseline techniques in terms of both state identification and trajectory prediction. The evaluation of the proposed framework, using real-world data, demonstrates that it can enable applications such as UAV-based surveillance and monitoring, while also improving the safety and efficiency of UAV operations.
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model

In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of the robot. Also, the dynamics of the robot may change due to, e.g., different payloads, changing the system's mass, or wear and tear, changing actuator gains or joint friction. An autonomous agent should thus be able to adapt to such variations. In this paper, we develop a novel probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN, which is able to adapt to the above-mentioned variations. It builds on recent advances in meta-learning forward dynamics models based on Neural Processes. We evaluate our method in a simulated 2D navigation setting with a unicycle-like robot and different terrain layouts with spatially varying friction coefficients. In our experiments, the proposed model exhibits lower prediction error for the task of long-horizon trajectory prediction, compared to non-adaptive ablation models. We also evaluate our model on the downstream task of navigation planning, which demonstrates improved performance in planning control-efficient paths by taking robot and terrain properties into account.
A New Key Negotiation Mechanism Based on Digital Certificate

This paper analyzes the shortcomings of the traditional mechanism of cryptographic key negotiation between gateway and terminal, and proposes a new key negotiation mechanism based on digital certificate. This method presets the root certificate and authenticates the encryption key and decryption key of the protection key in the gateway and terminal in advance. It utilizes root certificates to verify the validity of terminal and gateway certificates, confirming the legitimacy of certificates and effectively preventing the possibility of counterfeit certificates. The dispersion technology is used to disperse the preset key first, and then the distributed sub-key is used to encrypt the random number, preventing the security of the whole system from being threatened after the master key is cracked. Finally, an effective combination of asymmetric algorithms and symmetric algorithms is used for session key negotiation and confirmation between the gateway and the terminal. Thus, after mutual authentication between the gateway and the terminal, the session key is obtained.