Reviewerly/exHarmony huggingface dataset:
Hybrid Visual-Ranging Servoing for Positioning Based on Image and Measurement Features

In this article, a hybrid visual-ranging servoing method is proposed to realize high-precision positioning tasks with a 6-degree of freedom (DOF) manipulator. This method utilizes the image and measurement features directly in the control loop. Without the need of complex image feature design and attitude estimation, this method realizes the 6-DOF control of a robot. A vital challenge in traditional vision-based systems is avoiding local minima and singularity problems. To tackle this issue, a full-rank interaction matrix hybrid visual servo (FRHVS) design criterion is proposed, which guarantees that the hybrid interaction matrix and its pseudoinverse matrix are both full rank. Moreover, the interaction matrix for these hybrid strategies, which combines image features with other sensors features, is derived in an analytical form. Experiments on a 6-DOF manipulator show that the proposed method is effective and has global asymptotic stability and high precision.
Classroom Concordancing and Second Language Motivational Self-System: A Data-Driven Learning Approach

Research shows that exploring language corpora through data-driven learning (DDL) plays a significant role in language learning. Nevertheless, it is not clear if using concordancing as an application of DDL affects the learners’ second language motivation. To address this gap, the current study adopted a triangulation design, validating quantitative data model, and a quasi-experimental (comparison group, pretest-posttest) design. Ninety English-major university students with an intermediate level of English language proficiency, divided into control and experimental groups, took part in the study. Drawing on a second language motivational self-system questionnaire, the findings of the study did not result in any statistically significant differences between the students in their second language motivational self-system. Altogether, the students found learning English through a DDL approach with concordancing less motivating than receiving explicit instruction. The study has implications for language teaching and learning.
Is there any financial kuznets curve in Jordan? a structural time series analysis

This paper investigates the notion of the financial Kuznets curve in an emerging country—Jordan. Both variants of the financial Kuznets curve (growth financial Kuznets curve and inequality financial Kuznets curve) have been examined using different time series methodologies applying to a sample period from 1993 to 2017. The unobserved components model results provide evidence for both variants of the financial Kuznets curve when using private credit to GDP as a proxy for financial-sector development. Moreover, non-nested model tests suggest that financial intermediaries are relatively more important than stock markets for income inequality. Overall, this paper provides evidence for the financial Kuznets curve in emerging countries. Moreover, it provides new insights for policymakers in Jordan in their challenge to boost economic growth and decelerate income inequality, by reversing the trend towards the concentration of power in the financial sector and creating public-financial institutions that provide affordable credit to small businesses and households.
The Korean 3T Practice: New Biosurveillance Model Utilizing New Information Technology and Digital Tools

In South Korea, COVID-19 pandemic responses, namely the 3T (testing, tracing, and treating) strategy, emerged as a new biosurveillance regime actively using new information technology (IT) and digital tools. The foundation of the Korean 3T system is epidemiological investigation efforts and clinical practices exploiting the use of new digital and IT tools. Due to these unique features, the Korean 3T system can be referred to as a “contact-based biosurveillance system,” which is an advanced version of the traditional biosurveillance models (indicator-based or event-based models). This article illustrates how the contact-based biosurveillance system originated from the experience with the 2015 Middle East Respiratory Syndrome (MERS) outbreak. The post-MERS Korean biosurveillance regime actively adopted the utility of new digital and IT tools to strengthen not only the ex-ante epidemic intelligence capabilities (by traditional models) but also the ex-post response and recovery capabilities (digital contact tracing and digital health intervention). However, critics claim that the Korean 3T system may violate individuals’ privacy and human rights by addressing the fact that the Korean biosurveillance system would strengthen social surveillance and population control by the government as a “digital big brother” in the cyber age. Nevertheless, 3T biosurveillance promises a positive future direction for digital health practice in the current biosurveillance regimes.
Application Prospects of Blockchain Technology to Support the Development of Interport Communities

A key aspect for the efficiency and security of maritime transport is linked to the associated information flows. The optimal management of maritime transport requires the sharing of data in real-time between the various participating organizations. Moreover, as supply chains become increasingly integrated, the connectivity of stakeholders must be ensured not only within the single port but also between ports. Blockchain could offer interesting opportunities in this regard and is believed to have a huge impact on the future of the digitization of the port and maritime industry. This document analyzes the state of art and practice of blockchain applications in the maritime industry and explores the application prospects and practical implications of blockchain for building an interport community. The paper uses SWOT analysis to address several research questions concerning the practical impacts and barriers related to the implementation of blockchain technology in port communities and develops a Proof of Concept (PoC) to concretely show how blockchain technology can be applied to roll-on roll-off transport and interport communities in real environments. In this regard, this study intends to contribute to the sector literature by providing a detailed framework that describes how to proceed to choose the correct blockchain scheme and implement the various management and operational aspects of an interport community by benefiting from the blockchain.
Understanding the Capabilities of the HoloLens 1 and 2 in a Mixed Reality Environment for Direct Volume Rendering with a Ray-casting Algorithm

Direct volume rendering (DVR) is a standard technique for visualizing scientific volumetric data in three-dimension (3D). Utilizing current mixed reality head-mounted displays (MR-HMDs), the DVR can be displayed as a 3D hologram that can be superimposed on the original &#x2018;physical&#x2019; object, offering supplementary x-ray visions showing its interior features. These MR-MHDs are stimulating innovations in a range of scientific application fields, yet their capabilities on DVR have yet to be thoroughly investigated. In this study, we explore a key requirement of rendering latency capability for MR - HMDs by proposing a benchmark application with 5 volumes and 30 rendering parameter variations.
Data stream clustering for low-cost machines

Nowadays, the operations performed by the Internet of Things (IoT) systems are no more trivial since they rely on more sophisticated devices than in the past. The IoT system is physically composed of connected computing, digital, mechanical devices such as sensors or actuators. Most of the time, each of them incorporates a logical arithmetic unit that can pre-compute or compute on the device. To extract value from the data produced at the edge, processing power offered by cloud computing is still utilized. However, streaming data to the cloud exposes some limitations related to the increased communication and data transfer, which introduces delays and consumes network bandwidth. Clustering data is one example of a treatment that can be executed in the cloud. In this paper, we propose a methodology for solving the data stream clustering problem at the edge. Data Stream clustering is defined as the clustering of data that arrive continuously, such as telephone records, multimedia data, sensors data, financial transactions, etc. Since we use low-cost and low-capacity devices, the objective is, given a sequence of points, to construct a good clustering of the stream using a small amount of memory and time. We propose a ‘windowing’ scheme, coupled with a sampling scheme to respect the objective. Under the experimental conditions, experiments show that the clustering solutions can be controlled, with difficulties for time-stamped data but not for random data or data with well-delimited clusters. The main advantage of our schema is that we are clustering data “on the fly” with no knowledge or assumption regarding the available data. We do not assume that all the data are known before a treatment batch by batch. Our schema also has the potential to be adapted to other classes of machine learning algorithms.
Cross-Project Defect Prediction Based on Two-Phase Feature Importance Amplification

As the typical application of computational intelligence in software engineering, cross-project defect prediction (CPDP) uses labeled data from other projects (source projects) for building models to predict the defects in the current projects (target projects), helping testers quickly locate the defective modules. But class imbalance and different data distribution among projects make CPDP a challenging topic. To address the above two problems, we propose a two-phase feature importance amplification (TFIA) CPDP model in this paper which can solve these two problems from domain adaptation phase and classification phase. In the domain adaptation phase, the differences in data distribution among projects are reduced by filtering both source and target projects, and the correlation-based feature selection with greedy best-first search amplifies the importance of features with strong feature-class correlation. In the classification phase, Random Forest works as the classifier to further amplify the importance of highly correlated features and establish a model which is sensitive to highly correlated features. We conducted both ablation experiments and comparison experiments on the widely used AEEEM database. Experimental results show that TFIA can yield significant improvement on CPDP. And the performance of TFIA CPDP model in all experiments is stable and efficient, which lays a solid foundation for its further application in practical engineering.
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis

A growing number of people are seeking healthcare advice online. Usually, they diagnose their medical conditions based on the symptoms they are experiencing, which is also known as self-diagnosis. From the machine learning perspective, online disease diagnosis is a sequential feature (symptom) selection and classification problem. Reinforcement learning (RL) methods are the standard approaches to this type of tasks. Generally, they perform well when the feature space is small, but frequently become inefficient in tasks with a large number of features, such as the self-diagnosis. To address the challenge, we propose a non-RL Bipartite Scalable framework for Online Disease diAgnosis, called BSODA. BSODA is composed of two cooperative branches that handle symptom-inquiry and disease-diagnosis, respectively. The inquiry branch determines which symptom to collect next by an information-theoretic reward. We employ a Product-of-Experts encoder to significantly improve the handling of partial observations of a large number of features. Besides, we propose several approximation methods to substantially reduce the computational cost of the reward to a level that is acceptable for online services. Additionally, we leverage the diagnosis model to estimate the reward more precisely. For the diagnosis branch, we use a knowledge-guided self-attention model to perform predictions. In particular, BSODA determines when to stop inquiry and output predictions using both the inquiry and diagnosis models. We demonstrate that BSODA outperforms the state-of-the-art methods on several public datasets. Moreover, we propose a novel evaluation method to test the transferability of symptom checking methods from synthetic to real-world tasks. Compared to existing RL baselines, BSODA is more effectively scalable to large search spaces.
Analysis of the Effect of Branched Chain Amino Acids on Muscle Health Information of Swimmers Based on Multisensor Fusion and Deep Learning

Swimmers must fully mobilize the muscles of the whole body during exercise, and it is necessary to study the protection of swimmers from muscle damage. Now, muscle damage is increasing year by year, and more athletes are affected. Therefore, studying the causes of muscle injuries and exploring more effective treatments have become important research topics in the field of sports medicine. This study is mainly based on deep learning to analyze the protective effect of branched-chain amino acids on swimming athletes’ muscle injury. Due to the complex and changeable environment and the interference of unknown factors, a single sensor cannot meet the needs of obtaining information. Therefore, people have developed the technology of multisensor information fusion to obtain enough information. Multisensor data fusion technology can synthesize the information of each sensor and then obtain more comprehensive and accurate decision-making information. This study is mainly based on multisensor fusion and deep learning to analyze the impact of branched chain amino acids on Swimmers’ muscle health information. Finally, two experiments were designed in this article. The first experimental result showed that the pain level of the experimental group who took BCAA supplements was 19% lower than that of the control group that did not take the BCAA supplement within three days after exercise. The results of the second experiment show the following: after exercise, the creatine kinase activity value of the experimental group taking BCAA supplement was <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mn>4.38</mn> <mo>±</mo> <mn>1.45</mn> </math> , and the creatine kinase activity value of the control group taking placebo was <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mn>5.42</mn> <mo>±</mo> <mn>2.12</mn> </math> . It proves that BBCA can protect muscle damage by reducing the activity of creatine kinase.
Exploring Renewable Energy Futures through Household Energy Resilience

A transition to renewable energy increases the risks of disruptions when electricity supply does not meet demand. HCI has explored how digital technologies can mitigate such problems in households through support for reducing or shifting electricity use. However, faster transitions may be possible if some disturbances can be acceptable and households are supported in adapting to them. In this paper, we present a study of 21 Swedish households and their experiences of and ideas on how to manage disruptions in electricity supply. We call this perspective household energy resilience and identify three strategies for resilience: (1) response diversity, i.e., diversity in ways of carrying out normally electricity-dependent practices, (2) creating opportunities to develop resilience, and (3) building community energy resilience. Furthermore, we suggest how HCI can support these strategies, both by providing tools to increase resilience and by carefully designing technology and services to be more resilient in themselves.
MFFE: Multi-scale Feature Fusion Enhanced Net for image dehazing

It is very challenging to perform dehazing operations on single haze images taken due to information degradation, for which we propose a novel single-image dehazing network based on U-Net. Most existing image dehazing algorithms only focus on whether they can remove the haze, but ignore the quality of the final dehazed image, which can lead to problems such as loss of image detail information and texture blurring, affecting the final recovery. We use the enhancement and error feedback mechanisms inside the super-resolution algorithm to gradually recover the haze-free images by introducing the Strengthen-Operate-subtract strategy in the decoder and demonstrate their effectiveness for the dehazing problem. Meanwhile, to solve the problem of missing information and utilization in the traditional U-Net structure, we design a multiscale feature fusion module that can effectively compensate for the missing contextual information and make full use of the disjoint features. In addition, an improved local binary pattern and SE attention mechanism are used to help the network obtain clearer details and texture images, and the accuracy of the enhanced dehazing network is improved using residual learning. We analyze the effectiveness of the proposed algorithm and show through extensive evaluation that our proposed model can be effectively used for single image dehazing and also has good performance compared to state-of-the-art methods on the test dataset.
Interrogating Human-centered Data Science: Taking Stock of Opportunities and Limitations

Data science has become an important topic for the CHI conference and community, as shown by many papers and a series of workshops. Previous workshops have taken a critical view of data science from an HCI perspective, working toward a more human–centered treatment of the work of data science and the people who perform the many activities of data science. However, those approaches have not thoroughly examined their own grounds of criticism. In this workshop, we deepen that critical view by turning a reflective lens on the HCI work itself that addresses data science. We invite new perspectives from the diverse research and practice traditions in the broader CHI community, and we hope to co-create a new research agenda that addresses both data science and human-centered approaches to data science.
Object Detection-Based Video Compression

Video compression is designed to provide good subjective image quality, even at a high-compression ratio. In addition, video quality metrics have been used to show the results can maintain a high Peak Signal-to-Noise Ratio (PSNR), even at high compression. However, there are many difficulties in object recognition on the decoder side due to the low image quality caused by high compression. Accordingly, providing good image quality for the detected objects is necessary for the given total bitrate for utilizing object detection in a video decoder. In this paper, object detection-based video compression by the encoder and decoder is proposed that allocates lower quantization parameters to the detected-object regions and higher quantization parameters to the background. Therefore, better image quality is obtained for the detected objects on the decoder side. Object detection-based video compression consists of two types: Versatile Video Coding (VVC) and object detection. In this paper, the decoder performs the decompression process by receiving the bitstreams in the object-detection decoder and the VVC decoder. In the proposed method, the VVC encoder and decoder are processed based on the information obtained from object detection. In a random access (RA) configuration, the average Bjøntegaard Delta (BD)-rates of Y, Cb, and Cr increased by 2.33%, 2.67%, and 2.78%, respectively. In an All Intra (AI) configuration, the average BD-rates of Y, Cb, and Cr increased by 0.59%, 1.66%, and 1.42%, respectively. In an RA configuration, the averages of ΔY-PSNR, ΔCb-PSNR, and ΔCr-PSNR for the object-detected areas improved to 0.17%, 0.23%, and 0.04%, respectively. In an AI configuration, the averages of ΔY-PSNR, ΔCb-PSNR, and ΔCr-PSNR for the object-detected areas improved to 0.71%, 0.30%, and 0.30%, respectively. Subjective image quality was also improved in the object-detected areas.
A saliency guided remote sensing image dehazing network model

This article presents a saliency guided remote sensing image dehazing network model. It consists of the following three blocks: A dense residual based backbone network, a saliency map generator, and a deformed atmospheric scattering model (ASM) based haze removal model, of which the dense residual based backbone network is used to capture the texture detail information of a remote sensing image, the saliency map generator is used to generate the saliency map of the related remote sensing image, and the generated saliency map is used to guide the network to capture more texture details through the guided fusion module. Finally, the deformed atmospheric scattering model (ASM) is used to remove haze from remote sensing images. The model here is compared with several state-of-art dehazing methods on synthetic data sets and real remote sensing images. Experimental results show that on the synthetic data set, the PSNR value of this model is increased by 4.47 db and the SSIM value is increased by 0.045 compared with the best model. On real remote sensing hazy images, the visual effect of our model is also better than that of existing methods. The authors also perform experiments to demonstrate that remote sensing image dehazing is helpful for remote sensing image detection automatically.
Intelligent Decision Algorithm of Target Compound Interception Based on A2C-PPO

In order to improve the timeliness of composite interception decision-making, and provide a variety of interception equipment task allocation schemes within a limited time, a proximal policy optimization (PPO) target prevention and control decision algorithm based on advantage actor critical (A2C) is proposed. Based on the in-depth analysis of A2C, temporal difference algorithm, and PPO algorithm, the architecture of A2C-PPO neural network is designed by using bidirectional long short-term memory (LSTM) network, and the training models of actor and critical network are established respectively. The situation information and entity information generated by target and intercepting equipment are taken as the input of neural network to realize the cooperative operation of intercepting equipment. Simulation results show the effectiveness of the proposed method.
SimSCL: A Simple Fully-Supervised Contrastive Learning Framework for Text Representation

During the last few years, deep supervised learning models have been shown to achieve state-of-the-art results for Natural Language Processing tasks. Most of these models are trained by minimizing the commonly used cross-entropy loss. However, the latter may suffer from several shortcomings such as sub-optimal generalization and unstable fine-tuning. Inspired by the recent works on self-supervised contrastive representation learning, we present SimSCL, a framework for binary text classification task that relies on two simple concepts: (i) Sampling positive and negative examples given an anchor by considering that sentences belonging to the same class as the anchor as positive examples and samples belonging to a different class as negative examples and (ii) Using a novel fully-supervised contrastive loss that enforces more compact clustering by leveraging label information more effectively. The experimental results show that our framework outperforms the standard cross-entropy loss in several benchmark datasets. Further experiments on Moroccan and Algerian dialects demonstrate that our framework also works well for under-resource languages .