Reviewerly/exHarmony huggingface dataset:
Comparing the Quality of Human Pose Estimation with BlazePose or OpenPose

Human pose estimation is a computer vision task that predicts the position of person's body landmarks within a given image or video. This technology could help provide virtual motion assessments by analyzing videos captured when the patient is outside a clinical setting. In this study, a newer pose estimation model that can run on a smartphone (BlazePose) was compared to a well-accepted solution (OpenPose) to determine if these models can provide clinically viable body keypoints for virtual motion assessment. Using ten videos of clinically relevant movements (recorded by physicians), keypoint coordinates were generated from each model. Using OpenPose as a baseline, Pearson correlation and root mean square error were calculated between the BlazePose and OpenPose keypoint trajectories. BlazePose had more instances where keypoints deviated from anatomical joint centres, compared to OpenPose, indicating the BlazePose was not yet viable for clinically relevant assessments. However, BlazePose runtime was much faster than OpenPose and returned metrics that could be incorporated into a smartphone solution. Future designs of a smartphone-based system for conducting virtual motion assessments should utilize OpenPose for pose estimation; however, BlazePose could be used for other design aspects such as movement pre-screening or activity classification.
Genetic programming for feature extraction and construction in image classification

Genetic Programming (GP) has been successfully applied to image classification and achieved promising results. However, most existing methods either address binary image classification tasks only or need a predefined classifier to perform multi-class image classification while using GP for feature extraction. This limits their flexibility since it is unknown which combinations of classifiers and features are the most effective for an image classification task. Furthermore, high image variations increase the difficulty of feature extraction and image classification. This paper proposes a GP approach with a new program representation, new functions, and new terminals. The new approach can conduct feature extraction, feature construction, and classification, automatically and simultaneously. It can extract and construct informative image features, select a suitable classification algorithm instead of relying on a predefined classifier, and perform classification for binary and multi-class image classification tasks. In addition, this paper develops a new mutation operator based on fitness of population for dynamically adjusting the size of the evolved GP programs. The experimental results on eight datasets with different variations and difficulties show that the proposed approach achieves higher classification accuracy than most of the benchmark methods. Further analysis shows that the GP evolved programs have appropriate tree sizes and potentially high interpretability.
Autonomous and Adaptive Navigation for Terrestrial-Aerial Bimodal Vehicles

Terrestrial-aerial bimodal vehicles bloom in both academia and industry because they incorporate both the high mobility of aerial vehicles and the long endurance of ground vehicles. In this work, we present an autonomous and adaptive navigation framework to bring complete autonomy to this class of vehicles. The framework mainly includes 1) a hierarchical motion planner that generates safe and low-power terrestrial-aerial trajectories in unknown environments and 2) a unified motion controller which dynamically adjusts energy consumption in terrestrial locomotion. Extensive real-world experiments and benchmark comparisons are conducted on a customized robot platform to validate the proposed framework’s robustness and performance. During the tests, the robot safely traverses complex environments with terrestrial-aerial integrated mobility, and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$7\times$</tex-math></inline-formula> energy savings in terrestrial locomotion. Finally, we will release our code and hardware configuration for the reference of the community.
Global solar radiation predictions in Fiji Islands based on empirical models

Many difficulties associated with Global Solar Radiation (GSR)' variability in space and time and with the angular position in sky hemisphere hamper its accurate knowledge. Fiji Islands, with a tropical maritime climate without great extremes of heat or cold, have GSR measurement limited by a small number of stations due to the technical and financial restricts. Yet there are various options of available empirical models to select the most suitable one(s) for estimating GSR accurately using weather-related variables available for each location. This paper, therefore, focuses on evaluation of the performances of twenty (20) empirical models in predicting GSR between 1984 and 2018 in six (6) meteorological stations within Fiji Islands. Simulations were performed for 35-year period (1984–2018) and results were compared with the National Aeronautics and Space Administration (NASA)'s MERRA (Modern-Era Retrospective Analysis for Research and Applications) observational datasets after bias corrections. Model performance indices such as Mean Error (ME), Nash-Sutcliffe Efficiency coefficient (NSE), Percentage Mean Error (PME), Root Mean Square Error (RMSE) and correlation coefficient (r) were adopted. Obtained coefficients of determination from the regression analysis (0.415 ≤ R2 ≤ 0.988; at 95% confidential level) performed for the models' calibration between 1984 and 2018 suggest that the models' potentials to simulate GSR varies across the stations and among the models. About 33% of the models in group I, which relate GSR to the sunshine duration, monthly average day length and/or extra-terrestrial radiation performed favourably well in predicting GSR while 75% of models in group II (with air temperature as additional predictor) and 100% in group III (humidity) performed very well. It is concluded that inclusion of air temperature and humidity as two principal predictors alongside with sunshine duration, day length and extra-terrestrial radiation produce the best GSR prediction (RMSE ≤ 5 MJ m−2) over Fiji Islands.
Semantic Communications for Speech Recognition

The traditional communications transmit all the source date represented by bits, regardless of the content of source and the semantic information required by the receiver. However, in some applications, the receiver only needs part of the source data that represents critical semantic information, which prompts to transmit the application-related information, especially when bandwidth resources are limited. In this paper, we consider a semantic communication system for speech recognition by designing the transceiver as an end-to-end (E2E) system. Particularly, a deep learning (DL)-enabled semantic communication system, named DeepSC-SR, is developed to learn and extract text-related semantic features at the transmitter, which motivates the system to transmit much less than the source speech data without performance degradation. Moreover, in order to facilitate the proposed DeepSC-SR for dynamic channel environments, we investigate a robust model to cope with various channel environments without requiring retraining. The simulation results demonstrate that our proposed DeepSC-SR outperforms the traditional communication systems in terms of the speech recognition metrics, such as character-error-rate and word-error-rate, and is more robust to channel variations, especially in the low signal-to-noise (SNR) regime.
Distributed hop-constrained s-t simple path enumeration at billion scale

Hop-constrained s-t simple path (HC-s-t path) enumeration is a fundamental problem in graph analysis and has received considerable attention recently. Straightforward distributed solutions are inefficient and suffer from poor scalabiltiy when addressing this problem in billion-scale graphs due to the disability of pruning fruitless exploration or huge memory consumption. Motivated by this, in this paper, we aim to devise an efficient and scalable distributed algorithm to enumerate the HC-s-t paths in billion-scale graphs. We first propose a new hybrid search paradigm tailored for HC-s-t path enumeration. Based on the new search paradigm, we devise a distributed enumeration algorithm following the divide-and-conquer strategy. The algorithm can not only prune fruitless exploration, but also well bound the memory consumption with high parallelism. We also devise an effective workload balance mechanism that is automatically triggered by the idle machines to handle skewed workloads. Moreover, we explore the bidirectional search strategy to further improve enumeration efficiency. The experiment results demonstrate the efficiency of our proposed algorithm.
Blockchain as a driving force for federalism: A theory of cross-organizational task-technology fit

Digital technologies play an important role for the delivery of many public services. However, selecting and adopting the ‘right’ digital technologies is often challenging, especially for federally structured governments. Universal factors for successful adoption are hard to establish, and the particularities of federalism, such as the separation of competencies, complicate technology selection. Nevertheless, blockchain technology seems to flourish in these environments. Through a single-case study on the blockchain project of Germany’s Federal Office for Migration and Refugees, we unpack one essential factor for this success: the fit between (cross-)organizational task structure and technological properties. This fit earns the Federal Office’s project considerable credit and traction with stakeholders and partner authorities – not least because it supports the argument that the digitalization of federal systems is possible without ‘digital centralization’ and redistribution of competencies. Our task-technology fit analysis contributes to a better understanding of the adoption of blockchain in the public sector. It also provides the foundation for an extended task-technology fit theory for federally structured, cross-organizational contexts.
A Blockchain-Based Solution for COVID-19 Vaccine Distribution

The COVID-19 vaccine distribution chain faced multiple challenges associated with the lack of production capacity, security issues, and miscommunication between different actors. Blockchain technology has been shown to solve the security and miscommunication issues in other industries. We first identify distribution chain challenges via literature reviews and primary interviews. Case studies that solved these challenges in other industries also served as a source. This information allowed us to devise a blockchain framework for the vaccine distribution chain and evaluate its application feasibility. We present the framework using data flow diagrams. The proposed framework helps minimize the circulation of counterfeit vaccines and vaccination records, improves communication between stakeholders in the distribution chain, increases supply chain security, and simplifies vaccine inventorying and handling processes.
FeTaQA: Free-form Table Question Answering

Abstract Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and integration of information pieces retrieved from a structured knowledge source. To complement the existing datasets and to reveal the challenging nature of the table-based question answering task, we introduce FeTaQA, a new dataset with 10K Wikipedia-based {table, question, free-form answer, supporting table cells} pairs. FeTaQA is collected from noteworthy descriptions of Wikipedia tables that contain information people tend to seek; generation of these descriptions requires advanced processing that humans perform on a daily basis: Understand the question and table, retrieve, integrate, infer, and conduct text planning and surface realization to generate an answer. We provide two benchmark methods for the proposed task: a pipeline method based on semantic parsing-based QA systems and an end-to-end method based on large pretrained text generation models, and show that FeTaQA poses a challenge for both methods.
Exploring uncertainty in regression neural networks for construction of prediction intervals

Deep learning has achieved impressive performance on many tasks in recent years. However, it has been found that it is still not enough for deep neural networks to provide only point estimates. For high-risk tasks, we need to assess the reliability of the model predictions. This requires us to quantify the uncertainty of model prediction and construct prediction intervals. One of the significant advantages of the proposed method is that it simultaneously implements point estimation and uncertainty quantification. In this paper, we explore the uncertainty in regression neural networks to construct the prediction intervals. In general, we comprehensively consider two categories of uncertainties: aleatory uncertainty and epistemic uncertainty. We design a novel loss function, which enables us to learn uncertainty without uncertainty labels. We only need to supervise the learning of regression tasks. In the process of training, the model implicitly learns aleatory uncertainty under the guidance of loss function. And that epistemic uncertainty is accounted for in the ensembled form. Our method correlates the construction of prediction intervals with uncertainty estimation. Experimental results on some publicly available datasets show that the performance of our method is competitive with other state-of-the-art methods.
Study of various methods for reliable, efficient and Secured IoT using Artificial Intelligence

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The next stage of green electricity labeling

The labeling of electricity is considered an important mechanism to differentiate renewable power generation and, thus, to incentivize the expansion of green energy. However, today's systems for documenting and trading green energy certificates suffer from multiple challenges. These could be addressed by a digital solution that holistically collects and processes production and consumption data. Blockchain-based architectures have repeatedly been suggested for this purpose since they can provide transparency and can likely be accepted by a broad group of stakeholders. Yet, there are significant scalability and privacy issues of a blockchain-based approach for storing and processing fine-grained production and consumption data. In this paper, we propose and discuss a potential solution that levers succinct cryptographic zero-knowledge proofs to balance the required level of transparency and privacy while at the same time providing a high degree of scalability.
Movie Recommendation Algorithm Based on Sentiment Analysis and LDA

Traditional recommendation algorithms have problems such as data sparseness and not paying attention to the diversity of recommendation results. In this paper, we use LDA to extract topics of comments about movies, and identify the emotional tendencies related to topics. As a result, we enrich user interest model and product feature model based on emotional tendencies to improve content-based recommendation algorithms. Most of prior work on applying sentiment classification to recommendation systems only consider the use of sentiment dictionaries to judge polarity, and adopt pattern matching methods to identify features. This paper uses BERT to train sentiment classification models and uses LDA to extract topics. The algorithm is run on the movie review database crawled from Douban, and the experimental result showed that the diversity of recommendation lists had been significantly improved.
LTE Device Identification Based on RF Fingerprint with Multi-Channel Convolutional Neural Network

Radio frequency fingerprint (RFF) identification technique has drawn great attention to wireless terminal authentication. Long-Term Evolution (LTE) has been widely deployed all over the world. RFF-based LTE terminal identifications can prevent the potential impersonation or denial of service (DoS) attacks in the physical layer. This paper proposes a novel multi-channel convolutional neural network (MCCNN) for LTE terminal identification. Differential constellation trace figure (DCTF) is extracted from the random access preamble of the physical random access channel (PRACH). To the best knowledge of the authors, this is the first work dedicated to RFF-based LTE terminal identification. The proposed scheme is evaluated in the hardware experimental system consisting of the LTE eNodeB implemented on the software-defined radio (SDR) platform and six LTE mobile phones. Experimental results show that the classification accuracy can reach 98.96&#x0025; at the SNR level of 30 dB with the line-of-sight (LOS) scenarios. Furthermore, long-time evaluations show that the proposed DCTF-MCCNN scheme is robust over time.
Internet of Things with Deep Learning-Based Face Recognition Approach for Authentication in Control Medical Systems

Internet of Things (IoT) with deep learning (DL) is drastically growing and plays a significant role in many applications, including medical and healthcare systems. It can help users in this field get an advantage in terms of enhanced touchless authentication, especially in spreading infectious diseases like coronavirus disease 2019 (COVID-19). Even though there is a number of available security systems, they suffer from one or more of issues, such as identity fraud, loss of keys and passwords, or spreading diseases through touch authentication tools. To overcome these issues, IoT-based intelligent control medical authentication systems using DL models are proposed to enhance the security factor of medical and healthcare places effectively. This work applies IoT with DL models to recognize human faces for authentication in smart control medical systems. We use Raspberry Pi (RPi) because it has low cost and acts as the main controller in this system. The installation of a smart control system using general-purpose input/output (GPIO) pins of RPi also enhanced the antitheft for smart locks, and the RPi is connected to smart doors. For user authentication, a camera module is used to capture the face image and compare them with database images for getting access. The proposed approach performs face detection using the Haar cascade techniques, while for face recognition, the system comprises the following steps. The first step is the facial feature extraction step, which is done using the pretrained CNN models (ResNet-50 and VGG-16) along with linear binary pattern histogram (LBPH) algorithm. The second step is the classification step which can be done using a support vector machine (SVM) classifier. Only classified face as genuine leads to unlock the door; otherwise, the door is locked, and the system sends a notification email to the home/medical place with detected face images and stores the detected person name and time information on the SQL database. The comparative study of this work shows that the approach achieved 99.56% accuracy compared with some different related methods.
Human, I wrote a song for you: An experiment testing the influence of machines’ attributes on the AI-composed music evaluation

This study examines the evaluation of musical performances of artificial intelligence (AI) and the acceptance of AI music generators as musicians. Relying on theoretical frameworks of anthropomorphism and creative machine heuristics, a 2 x 2 experiment is designed, where both the perceived anthropomorphism of AI (high vs. low anthropomorphism) and its autonomy of creativity (independent vs. dependent creativity) are controlled. The study found that humanlike traits of an AI music generator led it to be accepted as a musician. However, whether it was autonomous when creating songs did not influence its perception as a genuine musician. Also, the evaluation of its songs was done independently from its attributes. Still, people who perceived the AI music generator as a musician appreciated its songs more than those who did not. The implications of the expected findings are discussed.
Understanding the challenges of the adoption of blockchain technology in the logistics sector: the TOE framework

The purpose of this study is to present a framework for characterising challenges to the adoption of blockchain technology (BCT) in the logistics sector. The TOE (Technical, Organisational, and Environmental) framework was developed based on a literature survey building upon existing work and a new empirical study consisting of fifteen interviews in five logistics companies. The study uses TOE as a theoretical framework and classifies the identified elements from literature and interviews into categories and subcategories to construct a decision framework. The paper identifies the elements for BCT adoption in Logistics Supply Chain (LSC). The framework can assess the challenges of BCT adoption and subsequently adapt the implementation phase to manage the process better. Practitioners may derive valuable insights about the elements that enable BCT adoption in LSCs and formulate the strategy for using BCT for creating a data driven real-time LSC.
2.5D lightweight RIU-Net for automatic liver and tumor segmentation from CT

One critical factor that restricts the clinical application of computer-aided liver and tumor segmentation is the method's high complexity and low accuracy. Overcoming this limitation is what we are concerned about in this study. This paper presented a new 2.5D lightweight network for fast and accurate liver and tumor segmentation from CT images. The method is grounded in the U-Net framework, which leverages the techniques from the residual and Inception theories. We first adopted the 2.5D training mode for CNN networks to improve the utilization of spatial information. Then, we designed an improved U-type architecture to substantially reduce the parameters by introducing residual block and InceptionV3, named RIU-Net. Finally, a hybrid loss function combined BCE and Dice is employed to speed up the convergence and improve accuracy. We evaluated the proposed method on two publicly available databases, LiTS17 and 3DIRCADb. The performance of our approach is compared with five closely related techniques. Our result outperforms the others on both accuracy and time cost. Specifically, the total number of parameters is reduced by 70% compared to U-Net. Both quantitative and qualitative results demonstrated the superior applicability of our method and thus proved to be a promising lightweight tool for computer-aided liver and tumor segmentation..
A Pedagogical Chatbot: A Supplemental Language Learning Tool

This innovations in practice article introduces a chatbot that was developed to support and motivate second language learners during the COVID-19 pandemic. The chatbot was designed to facilitate active, out-of-class language learning to supplement in-class input. It can adapt to learners’ abilities and pace by chatting with them, thus providing differentiated instruction. I describe the rationale for the development and implementation of the chatbot and its pedagogical implications. My aim is to motivate and assist chatbot developers and second language teachers in designing and implementing chatbots that will enhance motivation and independent learning in language students.