Reviewerly/exHarmony huggingface dataset:
FAIRification of health-related data using semantic web technologies in the Swiss Personalized Health Network

The Swiss Personalized Health Network (SPHN) is a government-funded initiative developing federated infrastructures for a responsible and efficient secondary use of health data for research purposes in compliance with the FAIR principles (Findable, Accessible, Interoperable and Reusable). We built a common standard infrastructure with a fit-for-purpose strategy to bring together health-related data and ease the work of both data providers to supply data in a standard manner and researchers by enhancing the quality of the collected data. As a result, the SPHN Resource Description Framework (RDF) schema was implemented together with a data ecosystem that encompasses data integration, validation tools, analysis helpers, training and documentation for representing health metadata and data in a consistent manner and reaching nationwide data interoperability goals. Data providers can now efficiently deliver several types of health data in a standardised and interoperable way while a high degree of flexibility is granted for the various demands of individual research projects. Researchers in Switzerland have access to FAIR health data for further use in RDF triplestores.
Central bank digital currency, crypto assets, and cash demand: evidence from Japan

This study surveyed people’s knowledge and use of central bank digital currency (CBDC), crypto asset ownership, and cash demand in Japan. While 4% of the respondents understand CBDC, 10% are willing to use CBDC when available. The demographic characteristics of respondents who understand CBDC are similar to those of crypto asset owners. Average cash demand for day-to-day payment purposes by crypto asset owners is slightly lower than that by nonowners. If CBDC users had similar cash demand to that of crypto asset owners, the introduction of CBDC with attributes similar to crypto assets would not substantially reduce cash demand for day-to-day payment purposes.
Market trends and analysis of blockchain technology in supply chain

Technology is an important tool in the armory of state-of-the-art innovations, in terms of both digital expansions and disruptions. With blockchain technology gaining momentum, different industries are emphasizing experimentation with it. Nowadays, organizations are emphasizing towards agile and leaner supply chains with end-to-end prominence via the incorporation of this latest technology, thereby boosting services across the world. The revolutionary features of blockchain technology are paving the way for greater opportunities for supply chain businesses. The technology has the potential to become a supply chain data utility and repository that offers benefits to all its users, such as unique market information that would be otherwise unavailable from any other source. This paper contemplates the need for blockchain technology in the supply chain and its contribution to enhancing the overall efficiency and demand planning processes of businesses. It discusses the latest market trends and factors driving the need for the incorporation of blockchain in the supply chain sector and the future scenario. Different use cases of the technology, market challenges in the implementation of the technology, and the solutions offered by different companies to address such problems have also been discussed.
Emotion Analysis and Opinion Monitoring of Social Network Users Under Deep Convolutional Neural Network

With the development of the internet, the user behavior and emotional characteristics behind social networks have attracted scholars' attention. Meanwhile, identifying user emotion can promote the development of mobile communication technology and network intelligence industrialization. Based on this, this work explores the emotions of social network users and discusses the public comments on the speeches through the speeches of social network users. After 100 times of training, F1 of the BiLSTM algorithm can reach 97.32%, and after 100 times of training, its function loss can be reduced to 1.33%, which can reduce the impact of function loss on emotion recognition. The exploration is of great significance for analyzing the emotional behavior of social network users and provides a reference for the intelligent and systematic development of internet social model as well as the information management.
Modified Salp Swarm Algorithm With Deep Learning Based Gastrointestinal Tract Disease Classification on Endoscopic Images

Nowadays, the analysis of gastrointestinal (GI) tract disease utilzing endoscopic image classification becomes an active research activity from the biomedical sector.The latest technology in medical imaging is Wireless Capsule Endoscopy (WCE) for diagnosing gastrointestinal diseases namely bleeding, ulcer, polyp, and so on.Manual diagnoses will be time taking and tough for the medical practitioner; thus, the authors have designed computerized approaches for classifying and detecting such diseases.Many research groups presented various machine learning (ML) and image processing methods for classifying GI tract diseases in recent times.Conventional data augmentation and image processing methods are integrated with adjusted pre-trained deep convolutional neural networks (CNNs) for classifying diseases in the GI tract from WCI images.This study presents a Modified Salp Swarm Algorithm with Deep Learning based Gastrointestinal Tract Disease Classification (MSSADL-GITDC) on Endoscopic Images.The presented MSSADL-GITDC technique mainly focuses on the examination of WCE images for GIT classification.To accomplish this, the presented MSSADL-GITDC technique applies median filtering (MF) technique for image smoothening.The presented MSSADL-GITDC technique designs improved capsule network (CapsNet) model for feature extraction where the CapsNet model is modified by the class attention layer (CAL).Moreover, MSSA based hyperparameter tuning process is performed to improve the efficiency of the improved CapsNet model.For GIT classification, deep belief network with extreme learning machine (DBN-ELM) was used.Finally, backpropagation is applied for supervised fine tuning of the DBN-ELM model.The experimental validation of the MSSADL-GITDC technique takes place on Kvasir-V2 database reported the betterment of the MSSADL-GITDC technique on GIT classification with maximum accuracy of 98.03%.
Group Guided Data Association for Multiple Object Tracking

Multiple Object Tracking (MOT) usually adopts the Tracking-by-Detection paradigm, which transforms the problem into data association. However, these methods are restricted by detector performance, especially in dense scenes. In this paper, we propose a novel group-guided data association, which improves the robustness of MOT to error detections and increases tracking accuracy in occlusion areas. The tracklets are firstly clustered into groups of related motion patterns by a graph neural network. Using the idea of grouping, the data association is divided into two stages: intra-group and inter-group. For the intra-group, based on the structural relationship between objects, detections are recovered and associated by min-cost network flow. For inter-group, the tracklets are associated with the proposed hypotheses to solve long-term occlusion and reduce false positives. The experiments on the MOTChallenge benchmark prove our method’s effects, which achieves competitive results over state-of-the-art methods.
A Deep Learning-based Signal Classification Approach for Spectrum Sensing using Long Short-Term Memory (LSTM) Networks

Signal detection and identification plays an important role in cognitive radio (CR) and spectrum sensing applications. This paper proposes a deep learning (DL) technique for signal classification to identify the wireless signals in a certain radio frequency (RF) environment. The proposed approach is based on a long short-term memory (LSTM) neural network which can classify different radio signals based on their modulation type and pulse shape. We evaluate the classification accuracy of the LSTM network by using raw data and cyclostationary features as an input function. We show that the classification accuracy of the raw data-based LSTM classifier outperforms the one of the cyclostationary-based classifier even under low signal-to-noise ratio (SNR) conditions. The proposed raw data-based approach can classify 8 types of signals with an accuracy of more than 86% at an SNR ≥ 0dB.
The Best Problem Transformation Method in Multi-Label Classification Text for Thesis Abstract

The thesis data is increasing yearly; classification is one method that can be used to obtain added information from this case. The classification that suits this case is the multi-label classification. The multi-label classification with the Problem Transformation approach is a flexible approach. In this study, a comparison of the Problem Transformation methods, namely Binary Relevance, Label Powerset, and Classifier Chain, was carried out with the Multinomial Naïve Bayes algorithm as an estimator. As a result, the Classifier Chain method gives results that tend to be better, with the value of Hamming Loss being 0.0321 and an accuracy of 64.60%.
A Deep Learning Approach for Basic Human Activity Recognition with YOLOv4-tiny

The regression of the elderly body condition over time causes the elderly to be more susceptible to illness and other accidents. When the elderly experience illness or an accident such as a fall, it is necessary for the family to realize this and take prompt treatment immediately. However, with relatively many elderly people in Indonesia choosing to live independently, it will be difficult for families to find out and provide help instantly. Therefore, in this study, the authors form a model for recognizing basic human activities with deep learning-based computer vision that can be implemented in a supervisory system in a room. A deep-learning approach is needed because of the complexity and variance of body postures and forms of human activities. However, the deep learning approach requires extensive resources and computational capabilities. Therefore, the model is formed by the YOLOv4-tiny method, one of the tiny versions of YOLO. Model training using the author's laptop and model inference testing was carried out on the Jetson Nano and the author's laptop to compare the inference time between the two devices. We investigate the performance of the YOLOv4-tiny model application on the Jetson Nano and a laptop, as well as the accuracy of recognizing human activities. This study shows that this particular vision-based human activity recognition model formed using YOLOv4-tiny as a deep learning method can be applied using Jetson Nano as an embedded device in real-time, with a speed of about 20 frames per second, mAP@0.50 of 99.04%, and an average F1-Score of 94.18%.
Embedded FPGA-based Motion Planning and Control of a Dual-arm Car-like Robot

Dual-arm robotic applications require efficient tra-jectory control to establish a precise arm movement sequence to execute multiple tasks accurately. This paper presents a mul-tiprocessor implementation of the Lyapunov-based acceleration controllers using the field-programmable gate array (FPGA) on a General Purpose Processor (GPP) to solve the motion planning and control problem of a dual-arm car-like mobile robot. First, the dual-arm system's kinematic model and FPGA-based motion control are developed. Then, a dual-step algorithm is utilized to move the car-like base from an initial position to its pseudo-target and, secondly, maneuver the front and rear end-effectors to their designated targets, taking into account the restrictions and limitations of the revolute links and the steering control laws of the system. Finally, the computer simulations verify the effectiveness of the proposed method and the proposed control inputs. The innovative dual-arm technique presented in this study can potentially have real-world applications in smart city environments, particularly in the waste management and garbage collection sectors.
Acceleration Feedback Controller Processor Design of a Segway

Traffic congestions and carbon emissions are a few contributing factors to various problems in large cities across the globe. This paper provides the Single Purpose Processor (SPP) design for a nonholonomic segway robot's acceleration feedback control laws. The acceleration control rules are de-rived from the total potentials generated by the Lyapunov-based Control Scheme (LbCS), which were validated using Wolfram Mathematica simulation software. Due to the robot autonomously, the riders can have a safe ride while reaching their destination. Furthermore, the design could be implemented for rapid prototyping in hardware such as a VLSI chip, an ASIC chip, or an FPGA board. Hence, the segway robot could be used as a personal transport that could be ridden on narrow paths such as pavements while consuming less energy when compared to larger vehicles and help reduce carbon emissions; thus, the segway robot could be seen as an essential personal transport to contribute towards smart city goals.
A Model Decomposition Kalman Filter for Enhanced Localization of Land Vehicles

This article investigates a GPS-dead reckoning fusion localization problem for land-vehicles. We first give a nonlinear vehicle kinematics model, which is used as the transition model for the fusion filter. Then a decomposition rule is designed to resolve the transition model into a set of model families. Based on decomposed model series expansion, a fusion filter is derived to estimate the vehicle position based on Gaussian approximation. The fusion filter can guarantee higher real-time localization accuracy than other linearization-based approaches, since it is derived on the basis of the decomposed process model and the series expansion scheme involves a loss function to ensure minimum truncated orders. Numerical simulations and real-world experiments results demonstrate that our method is more accurate and reliable than the state-of-the-art methods for vehicle localization under various driving conditions.
Deep Saliency Smoothing Hashing for Drone Image Retrieval

Deep hashing algorithms are widely exploited in retrieval tasks due to its low storage and retrieval efficiency. Most of which focus on global feature learning, whilst neglecting local fine-grained features and saliency information for drone images. In this paper, we tackle these dilemmas with a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Saliency Smoothing Hashing</i> (DSSH) algorithm, which can leverage saliency capture mechanism, distribution smoothing term, global features and local fine-grained features to learn effective hash codes for drone image retrieval. The DSSH algorithm first designs information extraction module to capture global features and local fine-grained features for drone images. Meanwhile, a saliency capture module is proposed to perform information interaction attention and visual enhancement attention, which can capture the saliency area of drone images effectively. On top of the two paths, a novel objective function is designed to preserve the similarity of hash codes, smooth the distribution of drone image datasets and reduce the quantization errors between hash codes and hash-like codes concurrently. Extensive experiments on the Drone Action Dataset and ERA Drone Dataset demonstrate that the DSSH algorithm can further improve the retrieval performance compared to other deep hashing algorithms.
GMM-IL: Image Classification Using Incrementally Learnt, Independent Probabilistic Models for Small Sample Sizes

When deep-learning classifiers try to learn new classes through supervised learning, they exhibit catastrophic forgetting issues. In this paper we propose the Gaussian Mixture Model - Incremental Learner (GMM-IL), a novel two-stage architecture that couples unsupervised visual feature learning with supervised probabilistic models to represent each class. The key novelty of GMM-IL is that between images and labels there is a bijective connection. New classes can be incrementally learnt using a small set of annotated images with no requirement for any previous training data. This enables the incremental addition of classes to a database, that can be indexed by visual features and reasoned over based on perception. Using Gaussian Mixture Models to represent the independent classes, we outperform a benchmark of an equivalent network with a Softmax head, obtaining increased accuracy for sample sizes smaller than 12 and increased weighted F1 score for 3 imbalanced class profiles in that sample range. This novel method enables new classes to be added to a system with only access to a few annotated images of the new class.
A Region‐Based Analysis for the Feature Concatenation in Deep Forests

Deep forest is a tree-based deep model made up of non-differentiable modules that are trained without backpropagation. Despite the fact that deep forests have achieved considerable success in a variety of tasks, feature concatenation, as the ingredient for forest representation learning, still lacks theoretical understanding. In this paper, we aim to understand the influence of feature concatenation on predictive performance. To enable such theoretical studies, we present the first mathematical formula of feature concatenation based on the two-stage structure, which regards the splits along new features and raw features as a region selector and a region classifier respectively. Furthermore, we prove a region-based generalization bound for feature concatenation, which reveals the trade-off between Rademacher complexities of the two-stage structure and the fraction of instances that are correctly classified in the selected region. As a consequence, we show that compared with the prediction-based feature concatenation (PFC), the advantage of interaction-based feature concatenation (IFC) is that it obtains more abundant regions through distributed representation and alleviates the overfitting risk in local regions. Experiments confirm the correctness of our theoretical results.
A Declarative Approach to Contrast Pattern Mining

This paper proposes a declarative approach to the problem of contrast pattern mining. The approach is based on encodings of the data and the problem with Answer Set Programming (ASP), and evaluated in a novel AI application in the field of Digital Forensics.
BiLSTM Model With Attention Mechanism for Sentiment Classification on Chinese Mixed Text Comments

Sentiment classification has become a significant research topic in natural language processing. As the most popular research method for sentiment classification, deep learning has been applied to various experimental datasets by many scholars and has achieved good results. Aiming at the problems of poor effect and insufficient accuracy of sentiment classification in the current vertical field, and to solve sentiment classification on Chinese mixed text, including both long text and short text, this work proposes an improved BiLSTM-Attention model that can extract features more effectively. The problem of insufficient dependence on long text is resolved by the Bi-directional Long Short-Term Memory (BiLSTM) model, and important information in the text is obtained by the attention mechanism. This study uses online shopping comment datasets for experiments and applies a multiclassification evaluation index to evaluate the model. Experiments support the proposed approach, the accuracy of sentiment classification on mixed text, long text can achieve to 0.9280, 0.9358 respectively, and its practical effect are more advantageous than other sentiment classification methods in terms of classification performance. The experimental results show that this study makes an important contribution to business development and has good domain extensibility.
2Serial-2Parallel Optical DAC for High-Resolution Photonic-Efficient Energy-Efficient 4|16|64|256 QAM

Next upgrade to QAM256 poses formidable barriers in terms of attainable performance and energy-efficiency of DAC driver + MZM based Transmitters (Tx). A promising alternative, potentially changing the ‘rules of the game’ is optical DACs (oDAC). In previous work, we proposed a PAM4-driven optical Tx generating up to 256QAM. Here we introduce a novel oDAC parallelizing a pair of optimized 2-segment MZMs, remarkably fed by 4 uncoupled NRZ signals. Based on a pair of IQ-nested new oDACs, the resulting DAC-less QAM Tx is fed by 8 uncoupled NRZ drivers. The energy efficiency, photonic efficiency and resolution of the flexible oDAC and QAM Tx are at their utmost.
Adaptive Lightweight License Plate Image Recovery Using Deep Learning Based on Generative Adversarial Network

Many Convolutional Neural Networks (CNNs) methods have already surpassed traditional approaches to image restoration tasks. Those CNNs models were usually designed to enhance single tasks such as an image resolution (super-resolution) or image denoising, but we came up with unconventional goals, that is, multiple recovery tasks from a single network design. Although the Transformer design has recently gained attention in image recovery task but they are too slow. In order to work with license plate images from a traffic camera stream, the system has to be fast. So, we proposed a lightweight deep learning-based data recovery system using a Generative Adversarial Network (GAN) principle named License Plate Recovery GAN (LPRGAN). The design has a proposed encoder-decoder style inspired by an autoencoder aided by dual classification networks. This style is suitable for problem-characteristic learning because strong contextual information retrieves from the down-scaled representations. This proposed system has three main features, identifying a problem, data recovery, and a fail-safe mechanism. The data recovery unit (LPRGAN) is used to recover license plate images from multiple degraded input images. Most existing image restoration systems do not have self-awareness, leading to an inefficiency problem. Unlike existing works, this system has anomaly detection and will only process on a degraded input, reducing workload overhead and improving efficiency as well as a fail-safe feature to prevent an unexpected bad output. Hence, it is a light and efficient system so it could be possible to deploy on a low-power machine such as edge computing devices, opening up new possibilities in on-device computing. Our proposed research can recover several degraded problems up to 720p resolution at 15 frames per second on a single graphic card, 256x128 resolution at 17 frames per second on a CPU-only workstation machine, or 7 frames per second on an ultra-low-power tablet PC.
Deep learning-enabled invalid-point removal for spatial phase unwrapping of 3D measurement

The single-camera system projecting single-frequency patterns is the ideal option among all proposed Fringe Projection Profilometry (FPP) systems in terms of measurement speed and system cost. This system necessitates a robust spatial phase unwrapping algorithm. However, robust spatial phase unwrapping remains a challenge in complex scenes. Quality-guided spatial phase unwrapping needs more efficient ways to identify unreliable points in phase maps before unwrapping. End-to-end deep learning spatial phase unwrapping face generality and interpretability problems. This paper proposes a hybrid spatial phase unwrapping method combining deep learning-enabled invalid-point removal and traditional path-following. This hybrid method demonstrates better robustness than traditional quality-guided methods, better interpretability than end-to-end deep learning schemes, and generality on unseen data. Experiments on the real dataset of multiple illumination conditions and multiple FPP systems differing in image resolution, fringe number, fringe direction, and optics wavelength verify the effectiveness of the proposed method.