Reviewerly/exHarmony huggingface dataset:
KD-Former: Kinematic and dynamic coupled transformer network for 3D human motion prediction

Recent studies have made remarkable progress on 3D human motion prediction by describing motion with kinematic knowledge. However, kinematics only considers the 3D positions or rotations of human skeletons, failing to reveal the physical characteristics of human motion. Motion dynamics reflects the forces between joints, explicitly encoding the skeleton topology, whereas rarely exploited in motion prediction. In this paper, we propose the Kinematic and Dynamic coupled transFormer (KD-Former), which incorporates dynamics with kinematics, to learn powerful features for high-fidelity motion prediction. Specifically, We first formulate a reduced-order dynamic model of human body to calculate the forces of all joints. Then we construct a non-autoregressive encoder-decoder framework based on the transformer structure. The encoder involves a kinematic encoder and a dynamic encoder, which are respectively responsible for extracting the kinematic and dynamic features for given history sequences via a spatial transformer and a temporal transformer. Future query sequences are decoded in parallel in the decoder by leveraging the encoded kinematic and dynamic information of history sequences. Experiments on Human3.6M and CMU MoCap benchmarks verify the effectiveness and superiority of our method. Code will be available at: https://github.com/wslh852/KD-Former.git.
The Next Frontier in Software Development: AI-Augmented Software Development Processes

Excitement around advances in generative artificial intelligence (AI) has taken the world by storm. This article reviews how software development processes evolved successfully only when automation was appropriately targeted to remove roadblocks and recommends that the software development community needs to replicate the same mindset when embracing generative AI based tools.
Reduced rank photonic computing accelerator

Use of artificial intelligence for tasks such as image classification and speech recognition has started to form an integral part of our lives. Facilitation of such tasks requires processing a huge amount of data, at times in real time, which has resulted in a computation bottleneck. Photonic cores promise ultra-fast convolutional processing by employing broadband optical links to perform parallelized matrix–vector multiplications (MVMs). Yet the scalability of photonic MVMs is limited by the footprint of the system and energy required for programming the weights, which scale with the matrix dimensionality ( M × N ). One approach is to reduce the number of hardware matrix weights required, which would allow for less aggressive scaling of the hardware. In this paper, we propose and experimentally demonstrate precisely such a hardware photonic architecture with reduced rank of operation, significantly improving on scalability and decreasing the system complexity. We employ the reduced photonic matrix with reconfigurable optical weights in image processing tasks where we demonstrate the ability to achieve edge detection and classification with 33% reduction in the conventional 3×3 kernel matrix and with no detectable loss of accuracy. While our demonstration is in photonics, this architecture can be universally adapted to MVM engines, and offers the potential for fast, scalable computations at a lower programming cost.
Cryptocurrency Price Analysis using Deep Learning

Crypto currency is an immerging field for investments and trading which attracts many businessmen, investors, and most importantly a generation of aspiring youth which understands the future of money transactions and its security. Cryptocurrency price prediction problem solutions can provide extremely useful information which will prevent investors from losing money invested on cryptocurrency. The ability to forecast the price of an asset such as crypto offers the opportunity for profit by trading it. The goal of this study is to use LSTM (Long-Short Term Memory) and RNN (Recurrent Neural Network) with Sentiment Analysis to create an algorithm model that can accurately predict the price of cryptocurrency the following day.
Path Optimization and Obstacle Avoidance using Gradient Method with Potential Fields for Mobile Robot

This research study presents a novel path-planning algorithm for mobile robots to overcome the limitations of existing techniques in terms of obstacle avoidance in mobile robots. Recent advances in the field have introduced a variety of genetic algorithm-based techniques but these systems face unique challenges. These difficulties are accurately accounting for attractive and repulsive forces in the environment, building safe and efficient paths while minimizing path length, and ensuring smooth and collision-free navigation in complex scenarios. Given these constraints, the proposed algorithm combines the Gradient approach and potential fields to address them. The algorithm intelligently guides the robot around obstacles by leveraging attractive and repulsive forces, ensuring a safe and efficient trajectory. The proposed algorithm aims to provide a comprehensive solution that effectively addresses the challenges existing mobile robot navigation systems face. To evaluate the algorithm’s performance, simulations are run on a mobile robot with static obstacles using the MATLAB platform. The results of these simulations show that the algorithm can generate smooth, collision-free paths even in difficult situations. By combining the strengths of potential field and gradient-based methods, the proposed algorithm represents a promising approach for mobile robot navigation in complex environments. This study identifies the difficulties that recent mobile robot path planning techniques have encountered, such as accurately considering forces and ensuring collision-free navigation.
A novel violence detection for drone surveillance system

Violence has always been a massive problem for all countries. One of the most effective ways to prevent Violence is to use surveillance systems that detect Violence. However, human-supervised Violence detection systems are ineffective; while traditional automated Violence detection technologies have many limitations, deep learning has emerged as the leading solution for automatically detecting Violence. Although several deep learning research papers give good experimental results, only a few are capable of real-time implementation, and fewer have been implemented in real-time on monitoring systems. In this paper, a solution is proposed to deploy a real-time violence detection system by deep learning on UAVs. The resolution will meet many standards simultaneously, such as calculation speed, model capacity, accuracy, high coverage, rapid deployment, and object tracking technology; especially the calculation speed, model capacity, and accuracy completely meet the requirements of real-time deployment. Experimental results can confirm that the concluded performance with 93.69% mAP(0.5), 0.114 FLOPS(B), weights size just 564.3 KB, and could achieve 21-22 FPS.
Decoupled Feature Pyramid Learning for Multi-Scale Object Detection in Low-Altitude Remote Sensing Images

Recently, low-altitude remote sensing platforms are widely used for various practical applications. Object detection is a basic and significant technology, serving them. The scale imbalance problem is predominant in low-altitude remote sensing images which brings a great challenge to detect objects from these imageries. Consequently, in this paper, we boost performance from the perspective of mitigating scale imbalance issue. Firstly, we choose a one-stage object detector with decoupled heads as baseline because of its comparatively high efficiency and accuracy. Current decoupled heads ignore inter-layer relationship and the information contained. On the other hand, all existing feature pyramid structures generate one feature map for two branches at every layer. Inspired by them, we propose a novel feature pyramid network paradigm—Decoupled Feature Pyramid Network (DFPN) with consideration of different preferences for classification and localization. Meanwhile, introduction of feature pyramid architecture will cause performance deterioration of larger objects because upper layers receive insufficient supervision in the training phase. Therefore, we adopt a distinct supervision strategy—Level Supervision, which pays more attention to upper layers. We demonstrate extensive experiments on two popular benchmarks of object detection in low-altitude remote sensing images to validate the effectiveness of our proposed method. Whats more, we introduce a Scale Imbalance metric to quantify the degree of size change of objects to better illustrate the ability of relieving scale imbalance problem. Finally, our proposed approach achieves state-of-the-arts performance on both datasets.
Audio Splicing Detection and Localization Based on Acquisition Device Traces

In recent years, the multimedia forensic community has put a great effort in developing solutions to assess the integrity and authenticity of multimedia objects, focusing especially on manipulations applied by means of advanced deep learning techniques. However, in addition to complex forgeries as the deepfakes, very simple yet effective manipulation techniques not involving any use of state-of-the-art editing tools still exist and prove dangerous. This is the case of audio splicing for speech signals, i.e., to concatenate and combine multiple speech segments obtained from different recordings of a person in order to cast a new fake speech. Indeed, by simply adding a few words to an existing speech we can completely alter its meaning. In this work, we address the overlooked problem of detection and localization of audio splicing from different models of acquisition devices. Our goal is to determine whether an audio track under analysis is pristine, or it has been manipulated by splicing one or multiple segments obtained from different device models. Moreover, if a recording is detected as spliced, we identify where the modification has been introduced in the temporal dimension. The proposed method is based on a Convolutional Neural Network (CNN) that extracts model-specific features from the audio recording. After extracting the features, we determine whether there has been a manipulation through a clustering algorithm. Finally, we identify the point where the modification has been introduced through a distance-measuring technique. The proposed method allows to detect and localize multiple splicing points within a recording.
PyMAiVAR: An open-source Python suite for audio-image representation in human action recognition

We present PyMAiVAR, a versatile toolbox that encompasses the generation of image representations for audio data including Wave plots, Spectral Centroids, Spectral Roll Offs, Mel Frequency Cepstral Coefficients (MFCC), MFCC Feature Scaling, and Chromagrams.This wide-ranging toolkit generates rich audio-image representations, playing a pivotal role in reshaping human action recognition.By fully exploiting audio data's latent potential, PyMAiVAR stands as a significant advancement in the field.The package is implemented in Python and can be used across different operating systems.
Applying Deep Learning Model to Predict Diagnosis Code of Medical Records

The International Classification of Diseases (ICD) code is a diagnostic classification standard that is frequently used as a referencing system in healthcare and insurance. However, it takes time and effort to find and use the right diagnosis code based on a patient's medical records. In response, deep learning (DL) methods have been developed to assist physicians in the ICD coding process. Our findings propose a deep learning model that utilized clinical notes from medical records to predict ICD-10 codes. Our research used text-based medical data from the outpatient department (OPD) of a university hospital from January to December 2016. The dataset used clinical notes from five departments, and a total of 21,953 medical records were collected. Clinical notes consisted of a subjective component, objective component, assessment, plan (SOAP) notes, diagnosis code, and a drug list. The dataset was divided into two groups: 90% for training and 10% for test cases. We applied natural language processing (NLP) technique (word embedding, Word2Vector) to process the data. A deep learning-based convolutional neural network (CNN) model was created based on the information presented above. Three metrics (precision, recall, and F-score) were used to calculate the achievement of the deep learning CNN model. Clinically acceptable results were achieved through the deep learning model for five departments (precision: 0.53-0.96; recall: 0.85-0.99; and F-score: 0.65-0.98). With a precision of 0.95, a recall of 0.99, and an F-score of 0.98, the deep learning model performed the best in the department of cardiology. Our proposed CNN model significantly improved the prediction performance for an automated ICD-10 code prediction system based on prior clinical information. This CNN model could reduce the laborious task of manual coding and could assist physicians in making a better diagnosis.
Mapping the intellectual and conceptual structure of research on CoDa in the ‘Social Sciences’ scientific domain. A bibliometric overview

This article presents a bibliometric analysis and a mapping of the intellectual and conceptual structure of the research conducted on 'Compositional Data' (CoDa) in Social Sciences. Since the publication of John Aitchison's seminal documents on compositional data in 1982 and 1986, the scientific production of papers using compositional data methods in Social Sciences has increased exponentially in several fields. The purpose of this paper focuses on multiple categories of Social Sciences and offers a comprehensive approach through bibliometrics methods, of the application of CoDa since 1988. Using various bibliometric techniques of productivity and graphic mapping, this document determines the evolution that research in compositional data has experienced, the most relevant journals and the research topics that have been addressed in different fields of research, among other issues. The findings reveal that the publication of papers applying compositional data has increased exponentially over the last 30 years, highlighting the contributions that have been made by the fields of 'geosciences', 'geography' and 'geology', 'health' and 'environmental science' to this development. The Journal of Geochemical Exploration, the Journal of Archaeological Science, Archaeometry, the Journal of Archaeological Science-Reports, the International Journal of Environmental Research and Public Health are the main journals that have contributed to this development. Among other contributions of this work, we can highlight that it offers the intellectual, social, and conceptual structure of the research over a long period of time, gathering the growing literature of research progress on CoDa in Social Sciences.
State of the art and future trends in obstacle-surmounting unmanned ground vehicle configuration and dynamics

Abstract This article presents a review of the platform configuration and dynamic of obstacle-surmounting unmanned ground vehicles (UGVs). For now, unmanned systems have emerged as a result of the rapid advancement of artificial intelligence and modern manufacturing techniques both domestically and internationally. The research on unmanned systems has been improved a lot. The UGV platform can execute transportation, recurring, and military tasks independently. For the high-level self-control, adaption, and maneuverability abilities, the UGV platform has been applied in the military, industry, and other special fields widely. The UGV platform usually performs tasks in an unstructured environment, so the all-terrain performance becomes a key factor restricting their operating efficiency and reliability. A brief literature review of the UGV platform is carried out in this article.
Machine Learning Techniques for Predicting Malaria: Unpacking Emerging Challenges and Opportunities for Tackling Malaria in Sub-saharan Africa

Malaria resurgence significantly threatens progress made towards malaria elimination in the past years and consequently increases socioeconomic and public health burden, especially in developing countries. This is exacerbated by the lack of intelligent models for predicting, mapping, diagnosing, and detecting malaria to strengthen malaria prevention and control measures. Predicting malaria and understanding risk factors leading to malaria outbreaks can assist policymakers in re-strategizing and re-aligning malaria elimination strategies and optimizing resource allocation by prioritizing malaria-endemic areas. Therefore, this study provides a comprehensive review of machine learning techniques applied to predict malaria using various risk factors. The study revealed that despite the distribution of mosquito nets, indoor spraying of insecticides, community engagement programmes and awareness strategies, socioeconomic factors, climate and environmental conditions significantly contribute towards malaria outbreaks and remain underexploited and poorly understood. Socioeconomic factors such as lower income, living conditions with house type, distance to health facilities, availability, and use of mosquito nets influence malaria outbreaks. Climatic and environmental risk factors including land surface temperature, rainfall, humidity, enhanced vegetation index, normalized difference vegetation index, and normalized difference water index significantly influence malaria incidences. The study further revealed that machine learning models such as support vector machines, decision trees, random forests, Extreme Gradient Boosting, logistic regression, K-Nearest Neighbors, Naïve Bayes, and multilayer perceptron have been greatly used to predict malaria using socioeconomic, climatic and environmental data. Predicting malaria can assist to develop early malaria warning systems, redesign interventions, make informed decision-making and subsequently strengthening malaria prevention and control measures.
Hepatic vessel segmentation based on 3D swin-transformer with inductive biased multi-head self-attention

Segmentation of liver vessels from CT images is indispensable prior to surgical planning and aroused a broad range of interest in the medical image analysis community. Due to the complex structure and low-contrast background, automatic liver vessel segmentation remains particularly challenging. Most of the related researches adopt FCN, U-net, and V-net variants as a backbone. However, these methods mainly focus on capturing multi-scale local features which may produce misclassified voxels due to the convolutional operator's limited locality reception field.We propose a robust end-to-end vessel segmentation network called Inductive BIased Multi-Head Attention Vessel Net(IBIMHAV-Net) by expanding swin transformer to 3D and employing an effective combination of convolution and self-attention. In practice, we introduce voxel-wise embedding rather than patch-wise embedding to locate precise liver vessel voxels and adopt multi-scale convolutional operators to gain local spatial information. On the other hand, we propose the inductive biased multi-head self-attention which learns inductively biased relative positional embedding from initialized absolute position embedding. Based on this, we can gain more reliable queries and key matrices.We conducted experiments on the 3DIRCADb dataset. The average dice and sensitivity of the four tested cases were 74.8[Formula: see text] and 77.5[Formula: see text], which exceed the results of existing deep learning methods and improved graph cuts method. The Branches Detected(BD)/Tree-length Detected(TD) indexes also proved the global/local feature capture ability better than other methods.The proposed model IBIMHAV-Net provides an automatic, accurate 3D liver vessel segmentation with an interleaved architecture that better utilizes both global and local spatial features in CT volumes. It can be further extended for other clinical data.
Metaverse and Blockchain in Education for collaborative Product-Service System (PSS) Design towards University 5.0

Following the technological advances, new personalized, and immersive services, based on recent developments in virtual co-existence, i.e., Metaverse, new opportunities for teaching methodologies and practices for reskilling educators and students emerge. However, the Metaverse infrastructure is still in its infancy. Thus, researchers, educators, and digital designers have an opportunity to lead the way. To realize the Metaverse potential as a three-dimensional, global, interconnected, immersive and real-time online world, new ways of connecting the physical world with Extended Reality (XR) experiences are required. Online learning has become mainstream, and Education is mainly focusing on the integration of immersive technologies into academic curricula to make learning more engaging and conceivable. Thus, Metaverse is one of those focal points for educators to create 3D virtual classrooms. Additionally, heading towards Industry 5.0, huge amount of personal data is continuously uploaded to the Cloud. As such, Blockchain is required to improve the quality of online education by enhancing accreditation processes, including security issues and scalability. Therefore, in this research work the design and development of collaborative platform for the design of Product-Service Systems (PSS), encapsulating a plethora of virtual tools is presented. Ultimately, with the integration of Metaverse, Blockchain and their accompanying digital technologies, young engineers can coexist with their educators, industrial partners, and their colleagues, in an attempt to achieve greater engagement, learn by doing, and get familiar with foreign and multi-cultural educational systems. Consequently, the presented framework promotes design education based on the collaboration provided by Metaverse, and academic credits are granted for young engineers through the Blockchain framework.
Characterizing and Optimizing Differentially-Private Techniques for High-Utility, Privacy-Preserving Internet-of-Vehicles

Recent developments of advanced Human-Vehicle Interactions rely on Internet-of-Vehicles (IoV), to achieve large-scale communications and synchronizations of data in practice. IoV is highly similar to a distributed system, where each vehicle is considered as a node and all nodes are grouped with a centralized server. In this manner, concerns of data privacy are rising, since privacy leak possibly occurs when all vehicles collect, process and share personal statistics (e.g. driver's heart rate, skin conductance and etc.). Therefore, it's important to understand how to efficiently apply modern privacy-preserving techniques on IoV. In this work, we first present a comprehensive study to characterize modern privacy-preserving techniques for IoV, and then propose a Differential Privacy(DP) privacy-protection framework specialized for unique characteristics of IoV. Our characterization focuses on DP, a representative set of mathematically-guaranteed mechanisms for both privacy-preserving processing and sharing of sensitive data. It demystifies the tradeoffs of deploying DP techniques, in terms of service quality and privacy-preserving effects. The lessons learned from characterization reveal the importance of data utility in DP-protected IoV and motivate us to examine new opportunities. To better balance tradeoffs and improve service quality, we introduce HUT, for high-utility, batched queries under DP-protection on IoV. We quantitatively examine the benefits of HUT, and experimentally show that, in an IoV context, HUT can reduce information loss by 95.69% while enabling strong mathematically-guaranteed protection over sensitive data. Based on our characterization and optimizations, we identify key challenges and opportunities for future studies, to enable privacy-preserving IoV with low service quality degradation.