Reviewerly/exHarmony huggingface dataset:
Gesture Recognition Method Using Acoustic Sensing on Usual Garment

In this study, we show a new gesture recognition method for clothing-based gesture input methods using active and passive acoustic sensing. Our system consists of a piezoelectric speaker and a microphone. The speaker transmits ultrasonic swept sine signals, and the microphone simultaneously records the ultrasonic signals that propagate through the garment and the rubbing sounds generated by the gestures on the garment. Our method recognizes a variety of gestures, such as pinch, twist, touch, and swipe, by incorporating active and passive acoustic sensing. An important feature of our method is that it does not require a dedicated garment or embroidery embedded since our system only requires a pair of piezoelectric elements to be attached to the usual garment with a magnet. We performed recognition experiments of 11 gestures on the forearm with four types of garments made from different materials and recognition experiments of five one-handed gestures on the button of a shirt and the pocket of pants. The results of a per-user classifier confirmed that the f-scores were 83.9% and 95.9% for 11 gestures with four different types of garments and 5 gestures that were selected assuming actual use, respectively. In addition, we confirmed that the system recognizes five gestures, which can be performed with one hand, with 89.2% and 92.6% accuracy in the button and pocket sites, respectively.
Blockchain technology prospects in transforming Ghana’s economy: a phenomenon-based approach

A phenomenon-based approach is used to learn more about how blockchain technology could improve Ghana's economic sectors in terms of cost savings, efficiency, and reliability with reduced risks. With our proposed blockchain-enabled frameworks, we describe how blockchains' Internet-of-Things (IoTs) and Distributed Ledger Technologies (DLTs) might reduce transaction, contract, and monitoring-related costs in the Agriculture & Agro-processing sector's supply chains. We also demonstrate how Smart Contracts (SMCs) and Distributed Ledger Technologies (DLTs) can improve time and cost-based efficiencies in local procurements, logistic contract execution, and supply chains across the Mining & Minerals processing sector. With the help of IoTs, DLTs, and SMCs, information asymmetries in the finance sector can be reduced to improve the financing for Small and Medium-sized Enterprises (SMEs). The Technological-Organizational-Environmental (TOE) elements remain crucial in the adoption of blockchain technology. As a result, it's critical to provide adequate frameworks for blockchain adoption.
Auritus

Smart ear-worn devices (called earables) are being equipped with various onboard sensors and algorithms, transforming earphones from simple audio transducers to multi-modal interfaces making rich inferences about human motion and vital signals. However, developing sensory applications using earables is currently quite cumbersome with several barriers in the way. First, time-series data from earable sensors incorporate information about physical phenomena in complex settings, requiring machine-learning (ML) models learned from large-scale labeled data. This is challenging in the context of earables because large-scale open-source datasets are missing. Secondly, the small size and compute constraints of earable devices make on-device integration of many existing algorithms for tasks such as human activity and head-pose estimation difficult. To address these challenges, we introduce Auritus, an extendable and open-source optimization toolkit designed to enhance and replicate earable applications. Auritus serves two primary functions. Firstly, Auritus handles data collection, pre-processing, and labeling tasks for creating customized earable datasets using graphical tools. The system includes an open-source dataset with 2.43 million inertial samples related to head and full-body movements, consisting of 34 head poses and 9 activities from 45 volunteers. Secondly, Auritus provides a tightly-integrated hardware-in-the-loop (HIL) optimizer and TinyML interface to develop lightweight and real-time machine-learning (ML) models for activity detection and filters for head-pose tracking. To validate the utlity of Auritus, we showcase three sample applications, namely fall detection, spatial audio rendering, and augmented reality (AR) interfacing. Auritus recognizes activities with 91% leave 1-out test accuracy (98% test accuracy) using real-time models as small as 6-13 kB. Our models are 98-740x smaller and 3-6% more accurate over the state-of-the-art. We also estimate head pose with absolute errors as low as 5 degrees using 20kB filters, achieving up to 1.6x precision improvement over existing techniques. We make the entire system open-source so that researchers and developers can contribute to any layer of the system or rapidly prototype their applications using our dataset and algorithms.
IF-ConvTransformer

Recent advances in sensor based human activity recognition (HAR) have exploited deep hybrid networks to improve the performance. These hybrid models combine Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to leverage their complementary advantages, and achieve impressive results. However, the roles and associations of different sensors in HAR are not fully considered by these models, leading to insufficient multi-modal fusion. Besides, the commonly used RNNs in HAR suffer from the 'forgetting' defect, which raises difficulties in capturing long-term information. To tackle these problems, an HAR framework composed of an Inertial Measurement Unit (IMU) fusion block and an applied ConvTransformer subnet is proposed in this paper. Inspired by the complementary filter, our IMU fusion block performs multi-modal fusion of commonly used sensors according to their physical relationships. Consequently, the features of different modalities can be aggregated more effectively. Then, the extracted features are fed into the applied ConvTransformer subnet for classification. Thanks to its convolutional subnet and self-attention layers, ConvTransformer can better capture local features and construct long-term dependencies. Extensive experiments on eight benchmark datasets demonstrate the superior performance of our framework. The source code will be published soon.
Long-term ENSO prediction with echo-state networks

Abstract The El Niño-Southern Oscillation (ENSO) is a climate phenomenon that profoundly impacts weather patterns and extreme events worldwide. Here we develop a method based on a recurrent neural network, called echo state network (ESN), which can be trained efficiently to predict different ENSO indices despite their relatively high noise levels. To achieve this, we train the ESN model on the low-frequency variability of ENSO indices and estimate the potential future high-frequency variability from specific samples of its past history. Our method reveals the importance of cross-scale interactions in the mechanisms underlying ENSO and skilfully predicts its variability and especially El Niño events at lead times up to 21 months. This study considers forecasts skillful if the correlation coefficients are above 0.5. Our results show that the low-frequency component of ENSO carries substantial predictive power, which can be exploited by training our model on single scalar time series. The proposed machine learning method for data-driven modeling can be readily applied to other time series, e.g. finance and physiology. However, it should be noted that our approach cannot straightforwardly be turned into a real-time operational forecast because of the decomposition of the original time series into the slow and fast components using low-pass filter techniques.
Opportunities for using eye tracking technology in manufacturing and logistics: Systematic literature review and research agenda

Workers play essential roles in manufacturing and logistics. Releasing workers from routine tasks and enabling them to focus on creative, value-adding activities can enhance their performance and wellbeing, and it is also key to the successful implementation of Industry 4.0. One technology that can help identify patterns of worker-system interaction is Eye Tracking (ET), which is a non-intrusive technology for measuring human eye movements. ET can provide moment-by-moment insights into the cognitive state of the subject during task execution, which can improve our understanding of how humans behave and make decisions within complex systems. It also enables explorations of the subject's interaction mode with the working environment. Earlier research has investigated the use of ET in manufacturing and logistics, but the literature is fragmented and has not yet been discussed in a literature review yet. This article therefore conducts a systematic literature review to explore the applications of ET, summarise its benefits, and outline future research opportunities of using ET in manufacturing and logistics. We first propose a conceptual framework to guide our study and then conduct a systematic literature search in scholarly databases, obtaining 71 relevant papers. Building on the proposed framework, we systematically review the use of ET and categorize the identified papers according to their application in manufacturing (product development, production, quality inspection) and logistics. Our results reveal that ET has several use cases in the manufacturing sector, but that its application in logistics has not been studied extensively so far. We summarize the benefits of using ET in terms of process performance, human performance, and work environment and safety, and also discuss the methodological characteristics of the ET literature as well as typical ET measures used. We conclude by illustrating future avenues for ET research in manufacturing and logistics.
Towards Quality Digital Learning: Lessons Learned during COVID-19 and Recommended Actions—The Teachers’ Perspective

The research focuses on identifying challenges and lessons from distance learning experiences in Italy during COVID-19, on opportunities emerging from a more digitalized school in the post-COVID-19 crisis, and on preparedness for potential future emergencies. The study aims to analyze different teachers’ experiences, perceptions, and perspectives on distance learning and provide suggestions for addressing crises and emergencies. An online survey based on a semi-structured questionnaire was conducted targeting primary and secondary school teachers and including schools whose students are only from urban contexts and schools where students are also from rural areas. The main results indicated that teachers were resilient and proactive and found strategies and good practices to deal with distance learning, although several open technological and pedagogical challenges remain unsolved. Finally, some recommended priority actions for stakeholders and policymakers have been provided to face these challenges and ensure inclusive and equitable quality education.
Enhancing classification of cells procured from bone marrow aspirate smears using generative adversarial networks and sequential convolutional neural network

Background and Objective: Leukemia represents 30% of all pediatric cancers and is considered the most common malignancy affecting adults and children. Cell differential count obtained from bone marrow aspirate smears is crucial for diagnosing hematologic diseases. Classification of these cell types is an essential task towards analyzing the disease, but it is time-consuming and requires intensive manual intervention. While machine learning has shown excellent outcomes in automating medical diagnosis, it needs ample data to build an efficient model for real-world tasks. This paper aims to generate synthetic data to enhance the classification accuracy of cells obtained from bone marrow aspirate smears. Methods: A three-stage architecture has been proposed. We first collaborate with experts from the medical domain to prepare a dataset that consolidates microscopic cell images obtained from bone marrow aspirate smears from three different sources. The second stage involves a generative adversarial networks (GAN) model to generate synthetic microscopic cell images. We propose a GAN model consisting of three networks; generator discriminator and classifier. We train the GAN model with the loss function of Wasserstein GAN with gradient penalty (WGAN-GP). Since our GAN has an additional classifier and was trained using WGAN-GP, we named our model C-WGAN-GP. In the third stage, we propose a sequential convolutional neural network (CNN) to classify cells in the original and synthetic dataset to demonstrate how generating synthetic data and utilizing a simple sequential CNN model can enhance the accuracy of cell classification. Results: We validated the proposed C-WGAN-GP and sequential CNN model with various evaluation metrics and achieved a classification accuracy of 96.98% using the synthetic dataset. We have presented each cell type's accuracy, specificity, and sensitivity results. The sequential CNN model achieves the highest accuracy for neutrophils with an accuracy rate of 97.5%. The highest value for sensitivity and specificity are 97.1% and 97%. Our proposed GAN model achieved an inception score of 14.52 ± 0.10, significantly better than the existing GAN models. Conclusions: Using three network GAN architecture produced more realistic synthetic data than existing models. Sequential CNN model with the synthetic data achieved higher classification accuracy than the original data.
A Novel Generative Model for Face Privacy Protection in Video Surveillance with Utility Maintenance

In recent years, the security and privacy issues of face data in video surveillance have become one of the hotspots. How to protect privacy while maintaining the utility of monitored faces is a challenging problem. At present, most of the mainstream methods are suitable for maintaining data utility with respect to pre-defined criteria such as the structure similarity or shape of the face, which bears the criticism of poor versatility and adaptability. This paper proposes a novel generative framework called Quality Maintenance-Variational AutoEncoder (QM-VAE), which takes full advantage of existing privacy protection technologies. We innovatively add the loss of service quality to the loss function to ensure the generation of de-identified face images with guided quality preservation. The proposed model automatically adjusts the generated image according to the different service quality evaluators, so it is generic and efficient in different service scenarios, even some that have nothing to do with simple visual effects. We take facial expression recognition as an example to present experiments on the dataset CelebA to demonstrate the utility-preservation capabilities of QM-VAE. The experimental data show that QM-VAE has the highest quality retention rate of 86%. Compared with the existing method, QM-VAE generates de-identified face images with significantly improved utility and increases the effect by 6.7%.
Named-entity recognition in Turkish legal texts

Abstract Natural language processing (NLP) technologies and applications in legal text processing are gaining momentum. Being one of the most prominent tasks in NLP, named-entity recognition (NER) can substantiate a great convenience for NLP in law due to the variety of named entities in the legal domain and their accentuated importance in legal documents. However, domain-specific NER models in the legal domain are not well studied. We present a NER model for Turkish legal texts with a custom-made corpus as well as several NER architectures based on conditional random fields and bidirectional long-short-term memories (BiLSTMs) to address the task. We also study several combinations of different word embeddings consisting of GloVe, Morph2Vec, and neural network-based character feature extraction techniques either with BiLSTM or convolutional neural networks. We report 92.27% F1 score with a hybrid word representation of GloVe and Morph2Vec with character-level features extracted with BiLSTM. Being an agglutinative language, the morphological structure of Turkish is also considered. To the best of our knowledge, our work is the first legal domain-specific NER study in Turkish and also the first study for an agglutinative language in the legal domain. Thus, our work can also have implications beyond the Turkish language.
Effective Biometric Technology Used with Big Data

The research has proposed a detailed study into the perceptive of government employees about the entry of the concept of biometric authentication like voice recognition at the respective workplaces in the State of Kuwait. Studies were carried out on employees regarding the factors that influence the employees to adopt any new technology, which helps in improved reception of biometric technology in the Big data applications. To derive the required data for this study, a mix of interviews and questionnaires was done; managers had to give the interviews, and employees had to complete questionnaires which were provided by four particular government organizations in the State of Kuwait to understand the perception of the employees using voice recognition biometrics. It was seen from the study that there was a notable cultural and digital space between the required authentication solutions issued by the management and the technological knowledge of the employees. Due to the situation of misuse, and mainly a lack of trust in the newly introduced technology and different management intentions, managers feel more responsible for minimizing the prevalent gaps. In the context of voice recognition implementation, it was more essential to tackle the employees' resistance. It is highly recommended to have proper orientation and knowledge in voice biometrics much before introducing the technology into the organization.
Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs

Human Activity Recognition (HAR) has become an increasingly popular task for embedded devices such as smartwatches. Most HAR systems for ultra-low power devices are based on classic Machine Learning (ML) models, whereas Deep Learning (DL), although reaching state-of-the-art accuracy, is less popular due to its high energy consumption, which poses a significant challenge for battery-operated and resource-constrained devices. In this work, we bridge the gap between on-device HAR and DL thanks to a hierarchical architecture composed of a decision tree (DT) and a one dimensional Convolutional Neural Network (ID CNN). The two classifiers operate in a cascaded fashion on two different sub-tasks: the DT classifies only the easiest activities, while the CNN deals with more complex ones. With experiments on a state-of-the-art dataset and targeting a single-core RISC-V MCU, we show that this approach allows to save up to 67.7% energy w.r.t. a “stand-alone” DL architecture at iso-accuracy. Additionally, the two-stage system either introduces a negligible memory overhead (up to 200 B) or on the contrary, reduces the total memory occupation.
Lightweight Fall Detection Algorithm Based on AlphaPose Optimization Model and ST-GCN

Falls cause great harm to people, and the current, more mature fall detection algorithms cannot be well-migrated to the embedded platform because of the huge amount of calculation. Hence, they do not have a good application. A lightweight fall detection algorithm based on the AlphaPose optimization model and ST-GCN was proposed. Firstly, based on YOLOv4, the structure of GhostNet is used to replace the DSPDarknet53 backbone network of the YOLOv4 network structure, the path convergence network is converted into BiFPN (bidirectional feature pyramid network), and DSC (deep separable convolution) is used to replace the standard volume of spatial pyramid pool, BiFPN, and YOLO head network product. Then, the TensorRt acceleration engine is used to accelerate the improved and optimized YOLO algorithm. In addition, a new type of Mosaic data enhancement algorithm is used to enhance the pedestrian detection algorithm, improving the effect of training. Secondly, use the TensorRt acceleration engine to optimize attitude estimation AlphaPose model, speeding up the inference speed of the attitude joint points. Finally, the spatiotemporal graph convolution (ST-GCN) is applied to detect and recognize actions such as falls, which meets the effective fall in different scenarios. The experimental results show that, on the embedded platform Jeston nano, when the image resolution is 416 × 416, the detection frame rate of this method is stable at about 8.33. At the same time, the accuracy of the algorithm in this paper on the UR dataset and the Le2i dataset has reached 97.28% and 96.86%, respectively. The proposed method has good real-time performance and reliable accuracy. It can be applied in the embedded platform to detect the fall state of people in real time.
A Study on Deep Learning-Based Fault Diagnosis and Classification for Marine Engine System Auxiliary Equipment

Maritime autonomous surface ships (MASS) are proposed as a future technology of the maritime industry. One of the key technologies for the development of MASS is condition-based maintenance (CBM) based on prognostics and health management (PHM). The CBM technology can be used for early detection of abnormalities based on the database and for a prediction of the fault occurring in the future. However, this technology has a problem that requires a high-quality database that reproduces the operation state of the actual ships and quantitatively and systematically indicates the characteristics for the various fault state of the device. To solve this problem, this paper presents a study on the development method of the fault database based on the reliability. Firstly, the reliability analysis of the target device was performed to select five types of the core fault modes. After that, a fault simulation scenario that defined the fault simulation test methodology was drawn. A land-based testbed was built for the fault simulation test. The fault simulation database was developed with a total of 109 sets through the fault simulation test. Additionally, a fault classification algorithm based on deep learning is proposed. The classification performance was evaluated with a confusion matrix. The developed database will be expected to serve as the basis for the development CBM technology of MASS in the future.
Interactive Robotic Grasping with Attribute-Guided Disambiguation

Interactive robotic grasping using natural language is one of the most fundamental tasks in human-robot interaction. However, language can be a source of ambiguity, particularly when there are ambiguous visual or linguistic contents. This paper investigates the use of object attributes in disambiguation and develops an interactive grasping system capable of effectively resolving ambiguities via dialogues. Our approach first predicts target scores and attribute scores through vision-and-language grounding. To handle ambiguous objects and commands, we propose an attribute-guided formulation of the partially observable Markov decision process (Attr-POMDP) for disambiguation. The Attr-POMDP utilizes target and attribute scores as the observation model to calculate the expected return of an attribute-based (e.g., “what is the color of the target, red or green?”) or a pointing-based (e.g., “do you mean this one?”) question. Our disambiguation module runs in real time on a real robot, and the interactive grasping system achieves a 91.43 % selection accuracy in the real-robot experiments, outperforming several baselines by large margins. Supplementary material is available at https://sites.google.com/umn.eduJattr-disam.