Reviewerly/exHarmony huggingface dataset:
Separation and recovery Markov boundary discovery and its application in EEG-based emotion recognition

In a Bayesian network (BN), the Markov boundary (MB) presents the local causal structure around a target. Due to the interpretability and robustness, it has been widely applied to feature selection and BN structure learning. However, existing MB discovery algorithms might fail to identify some true positives, leading to poor performance in real-world applications. To tackle this issue, we introduce a two-phase-discovery strategy to search more true positives. Based on this strategy, we propose a more accurate and data-efficient algorithm, separation and recovery MB discovery algorithm (SRMB). SRMB first discovers an incomplete parent–child set and spouse set via an MB separation process, and then retrieves the ignored true positives via an MB recovery process, which further exploits a symmetry test to improve accuracy in unfaithful cases. Experiments on standard BN and real-world data sets demonstrate the effectiveness and superiority of SRMB in terms of MB discovery, BN structure learning, and feature selection. To demonstrate the superiority of SRMB in data with distribution shift, we further apply SRMB to EEG-based emotion recognition tasks, where distribution shift exists in multiple unstable sessions. We prove that the most predictive features are from Gamma/Beta frequency bands and are distributed at the lateral temporal area.
Existing Path Planning Techniques in Unmanned Aerial Vehicles (UAVs): A Systematic Review

In recent times Unmanned Aerial Vehicle developed promptly and broadly used in many areas like reconstruction rescue and monitoring. In all the areas one important key challenge is path planning which tells UAVs that where to fly and how to find a path. The Multiple consideration is to be considered for an ideal path planning which includes path completeness, optimality. The execution of path planning becomes a challenging task in terms of trajectory generation and also require careful consideration. Unmanned Aerial Vehicle (UAV) path planning techniques have been extensively studied and involve two key stages. First, 3-D objects and obstacles (static and dynamic) are identified. Second, these objects and obstacles are mapped to plan an appropriate path. In the context of UAVs, we can explain path planning techniques in terms of roadmaps/Voronoi diagrams (a representation technique), or we can adopt an optimization approach using either cooperative or non-cooperative techniques. These two approaches, and the techniques used in each, are summarized in terms of the key factors that have been discussed in the context of UAVs. This paper, enables future studies to easily compare with existing and previous studies.
Integrating Cultural Relevance into a Behavioral mHealth Intervention for Native American Youth

Native American communities are disproportionately affected by a number of behavioral health disparities, including higher rates of depression, substance abuse, and suicide. As mobile health (mHealth) interventions gain traction as methods for addressing these disparities, they continue to lack relevance to Native American youth. In an effort to explore the design of relevant behavioral mHealth intervention for Native American communities, we have developed ARORA (Amplifying Resilience Over Restricted Internet Access), a prototype behavioral mHealth intervention that has been co-designed with Native American youth, a community advisory board, and a clinical psychologist. In this paper, we qualitatively analyze our co-design and focus group sessions using a grounded theory approach and identify the key themes that Native American community members have identified as being critical components of relevant mHealth designs. Notably, we find that the Native American youth who participated in our focus groups desired a greater level of didactic interaction with cultural and behavioral health elements. We conclude with a discussion of the significant challenges we faced in our efforts to co-design software with Native American stakeholders and provide recommendations that might guide other HCI researchers and designers through challenges that arise during the process of cross-cultural design.
Exploring data literacy via a librarian-faculty learning community: A case study

Faculty learning communities (FLCs) are year-long professional development opportunities available at many higher education institutions in the United States. While the literature reflects some librarian engagement with FLCs, it seems limited primarily to areas of traditional librarian expertise such as information literacy and outreach. This article describes a case study of a librarian-facilitated FLC focused on data literacy, which resulted in the development of a teaching toolkit, library-led data literacy instruction, and ongoing collaborations between librarians and faculty. The FLC structure proved to be a valuable framework that facilitated collaborative learning in topics relevant to both disciplinary faculty and librarians. In addition, the tangible work products produced by the FLC serve to advance the strategic, curricular goals of the university while giving the library an opportunity to showcase its value in the academic lifecycle.
AI-Mediated Communication

AI-Mediated Communication (AI-MC) is interpersonal communication that involves an artificially intelligent system that can modify, augment, or even generate content to achieve communicative and relational goals. AI-MC is increasingly involved in human communication and has the potential to impact core aspects of human communication, such as language production, interpersonal perception and task performance. Through a between-subjects experimental design we examine how these processes are influenced when integrating AI-generated language in the form of suggested text responses (Google's smart replies) into a text-based referential communication task. Our study replicates and extends the impacts of a positivity bias in AI-generated language and introduces the adjacency pair framework into the study of AI-MC. We also find preliminary yet mixed evidence to suggest that AI-generated language has the potential to undermine some dimensions of interpersonal perception, such as social attraction. This study contributes important concepts for future work in AI-MC and offers findings with implications for the design of AI systems in human-to-human communication.
Deep Hough Transform for Semantic Line Detection

In this paper, we put forward a simple yet effective method to detect meaningful straight lines, a.k.a. semantic lines, in given scenes. Prior methods take line detection as a special case of object detection, while neglect the inherent characteristics of lines, leading to less efficient and suboptimal results. We propose a one-shot end-to-end framework by incorporating the classical Hough transform into deeply learned representations. By parameterizing lines with slopes and biases, we perform Hough transform to translate deep representations to the parametric space and then directly detect lines in the parametric space. More concretely, we aggregate features along candidate lines on the feature map plane and then assign the aggregated features to corresponding locations in the parametric domain. Consequently, the problem of detecting semantic lines in the spatial domain is transformed to spotting individual points in the parametric domain, making the post-processing steps, i.e. non-maximal suppression, more efficient. Furthermore, our method makes it easy to extract contextual line features, that are critical to accurate line detection. Experimental results on a public dataset demonstrate the advantages of our method over state-of-the-arts. Codes are available at https://mmcheng.net/dhtline/ .
BLOWN: A Blockchain Protocol for Single-Hop Wireless Networks Under Adversarial SINR

Known as a distributed ledger technology (DLT), blockchain has attracted much attention due to its properties such as decentralization, security, immutability and transparency, and its potential of servicing as an infrastructure for various applications. Blockchain can empower wireless networks with identity management, data integrity, access control, and high-level security. However, previous studies on blockchain-enabled wireless networks mostly focus on proposing architectures or building systems with popular blockchain protocols. Nevertheless, such existing protocols have obvious shortcomings when adopted in wireless networks where nodes may have limited physical resources, may fall short of well-established reliable channels, or may suffer from variable bandwidths impacted by environments or jamming attacks. \hl{In this paper, we propose a novel consensus protocol named Proof-of-Channel (PoC) leveraging the natural properties of wireless communications, and develop a permissioned BLOWN protocol (BLOckchain protocol for Wireless Networks) for single-hop wireless networks under an adversarial SINR model. }We formalize BLOWN with the universal composition framework and prove its security properties, namely persistence and liveness, as well as its strengths in countering against adversarial jamming, double-spending, and Sybil attacks, which are also demonstrated by extensive simulation studies.
Contextualized game-based intervention for digital literacy for the Pacific Islands

In the digital age, advocating and improving digital literacy is a global challenge. There have been scales developed to measure individuals' digital literacy competencies; however, intervention programs have been only a few. This research paper articulates design details, validity, reliability and effectiveness of a new online modulated digital literacy intervention program (DLIP). For the development of DLIP, digital literacy is stipulated in this research as a universal framework that consists of six different literacies; media, information, technology, computer, visual, and communication literacy. An online module has been designed for each of these six literacies, and the concept of game-based learning has been used to engage the users and secure high user satisfaction. To test the reliability of the intervention, the Kuder- Richardson- 20 (KR-20) test was performed. The developed intervention was deemed to be reliable with the KR-20 value of 0.86. The construct validity was measured using the spearman's correlation test and since the values for all the constructs were above 0.3, the DLIP was valid. The effectiveness of the DLIP was evaluated by calculating the effect size. The Cohen's d test was used and the results show that the intervention was moderately effective. Although DLIP has been developed for the Pacific Islands it has global applicability.
Face Mask Detection System using CNN

Amid current pandemic, Covid-19 has made us realize the importance of Face Masks and we need to understand the crucial effects of not wearing one, now more than ever. Right now, there are no face mask detectors installed at the crowded places. But we believe that it is of utmost importance that at transportation junctions, densely populated residential area, markets, educational institutions and healthcare areas, it is now very important to set up face mask detectors to ensure the safety of the public. In this paper we have tried to build a two phased face mask detector which will be easy to deploy at the mentioned outlets. With the help of Computer Vision, it is now possible to detect and implement this on large scale. We have used CNN/ MobileNet V2 architecture for the implementation of our model. The implementation is done in Python, and the python script implementation will train our face mask detector on our selected dataset using TensorFlow and Keras. We have added more robust features and trained our model on various variations, we made sure to have large varied and augmented dataset so that the model is able to clearly identify and detection the face masks in real time videos. The trained model was tested on both real-time videos and static pictures and in both the cases the accuracy was more than the other designed models.
Time series analysis and prediction of nonlinear systems with ensemble learning framework applied to deep learning neural networks

In this paper, we design a framework to predict the value of time series for nonlinear systems. In order to achieve this goal, many studies of applications and plans for machine learning and even deep learning become currently popular. First, we select four nonlinear systems: including a proposed four-dimensional chaotic system, Lorenz system, Duffing oscillator, and Rössler attractor. The framework has three learning parts as Long Short-Term Memory (LSTM) based on Generate Performance Model (GPM), ensemble learning based on Restrict and Control Model (RCM), and one-dimensional convolution neural network (1-DCNN) of dirichlet distribution based on Overall Verification Model (OVM). Before learning steps, we exploit K-means method as pre-processing and hypothesis verification to improve the prediction accuracy. After learning steps, we construct four forecasting progresses as Point by Point Generated Method (PPGM), Sequence Full Generated Method (SFGM), Sequence Multiple Generated Method (SMGM), and Improvement with RCM and OVM (IPRO) to predict the value of the time steps. Finally, we use Mean Average Error (MAE) as the criterion of the prediction, and estimate the accuracy by comparing the error region of the average standard deviation.
Deep multi-scale attentional features for medical image segmentation

Automatic segmentation of medical images is a difficult task in the field of computer vision owing to the various backgrounds, shapes, size, and colors of polyps or tumors. Despite the success of deep learning (DL)-based encoder–decoder architectures in medical image segmentation, these models have several disadvantages. First, an architecture such as U-Net cannot encode multi-scale semantic information at a different level on the decoder side. Second, it fails to reimpose the feature maps adeptly due to its limited capability on capturing long-range feature dependencies. In this study, we solve this problem by capturing multi-scale global feature maps, which forces the network to learn different semantic information at each scale. Further, we utilize the attention mechanism to suppress noise and the undesirable features, leading to a thorough restoration of contextual feature dependencies. Finally, we propose a novel method which leverages the compound scaled EfficientNet as a encoder backbone for efficient feature extraction and the UNet decoder to reconstruct the fine-grained details. We evaluated the proposed method using three different medical datasets: Kvasir-SEG, nuclei segmentation, and skin-lesion segmentation. The experimental results demonstrate that the proposed method takes an unassailable lead in terms of segmentation accuracy over the baseline models across different datasets and backbone architectures. Further, the proposed method strengthens the segmentation quality of varying shapes, object shapes, suppresses the noise, and leads to a better performance.
Transaction-Based Hidden Strategies against General Phishing Detection Framework on Ethereum

With the prosperous development of blockchain technologies in the past few years, some cybercrimes have emerged in the blockchain ecosystem, such as the phishing scams on Ethereum. To alleviate these security problems, a few anomaly detection frameworks were proposed. Specifically, previous studies usually model the transfer relationship between accounts in the blockchain ecosystem as a transaction network, where nodes represent accounts and edges represent the corresponding transaction records. Inspired by the adversarial attacks on graph data, we believe the robustness of existing detection frameworks still needs to be further verified even though they have achieved good performance. In this paper, a phishing detection framework based on feature learning and a phishing hidden framework based on inserting transaction records are proposed, respectively. Experimental results show the effectiveness of our phishing detection framework and the superiority of the phishing hidden strategies, which indicate that existing phishing detection frameworks are lack of robustness and still need further improvement against malicious attacks.
Optimization of Block Query Response Using Evolutionary Algorithm

The term blockchain can be practically viewed as a database as storage, and retrieval of information can be performed within blocks. But database cannot be termed as blockchain. In the simplest definition, it is the time stamped series of data along with heterogeneity which is managed by a cluster of systems with security implementing the cryptographic applications. Practically, it uniformly stores the information in blocks, where each block contains the hashed information from the previous block to provide cryptographic security. This implementation mechanism is absolutely decentralized supporting peer-to-peer storage of data to all the access points. Accordingly, it captures the prerequisite and relevant information logically structured in blocks. Each block is attached to its preceding blocks by applying the cryptographic generic function, where the details of the initial block are required to be focused. Based upon the non-recursive processing of data, it cannot repeat the data already validated in the block. In case of decentralization, rather having faith on single entity, it is essential to focus on other linked entities and to maintain integrity of data during the application. After that as soon as the data is associated with the blockchain, the information can be stored permanently, and the particular block will be associated with the succeeding blocks to update the information. In this paper, it is intended to optimize the response of queries blocks implementing evolutionary algorithms approach.
Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI

Objective: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs.Method & Materials: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer.An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI.Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers.The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN).Results: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN.However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per 100 × 100 pixels.Conclusion: The result of this study implies the potential application of ACNS to real-time resolution enhancement of 4D MRI in MRI guided radiation therapy.
Coexistence of quantum key distribution and optical transport network based on standard single-mode fiber at high launch power

There is an increasing demand for multiplexing of quantum key distribution with optical communications in single fiber in consideration of high costs and practical applications in the metropolitan optical network. Here, we realize the integration of quantum key distribution and an optical transport network of 80 Gbps classical data at 15 dBm launch power over 50 km of the widely used standard (G.652 Recommendation of the International Telecom Union Telecom Standardization Sector) telecom fiber. A secure key rate of 11 Kbps over 20 km is obtained. By tolerating a high classical optical power up to 18 dBm of 160 Gbps classical data on single-mode fiber, our result shows the potential and tolerance of quantum key distribution being used in future large capacity transmission systems, such as metropolitan area networks and data centers. The quantum key distribution system is stable, practical, and insensitive to the polarization disturbance of channels by using a phase coding system based on a Faraday–Michelson interferometer. We also discuss the fundamental limit for quantum key distribution performance in the multiplexing environment.
A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures

Solar energy is playing an increasingly important role in the production of electricity through photovoltaic and Concentrated Solar Power (CSP) systems. This type of energy requires an efficient storage system that will allow its use when the sunset or during cloudy weather. Hence, 0-48 hours forecast lead time is crucial for electrical network managers to anticipate energy deficits during periods of cloud occurrence. This study presents a new approach to forecast solar irradiances using a Numerical Weather Prediction (NWP) model outputs, coupled with a Radiative Transfer operator, namely libRadtran. The NWP model used in this study is the WRF-Solar configuration of the Weather Research and Forecasting (WRF), designed for solar energy in order to forecast solar irradiances. The other parameters forecasted by WRF like the vertical profiles of cloud liquid water (CLW), cloud ice water (IWC), clouds fraction, relative humidity, and temperature will be used as input for libRadtran to compute off-line the solar irradiances. Both forecasted irradiances were first compared with the Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) data, then validated a second time by ground measurements for three locations in Algeria for the years 2016 and 2017. The results clearly show that the solar irradiances forecasted directly by WRF-Solar underestimate the MERRA-2 data for high solar zenith angles. With regard to the proposed approach, there is an improvement in the forecast of the solar irradiances during clear sky days and the rRMSE of the calculated Global Tilted Irradiance (GTI) reaches 12% in winter and increases to 22% in summer.
Detection of Flying Birds in Airport Monitoring Based on Improved YOLOv5

Flying birds affect the safety of aircraft, and it’s difficult to effectively detect and discriminate bird targets because of their small sizes in large-field monitoring. To solve the problem of insufficient feature information of tiny targets and improve the detection performance, in this paper we introduce a method of channel attention mechanisms into the YOLOv5. By modeling the interdependence between channels, the proposed method adaptively learns the weights, to calibrate the feature responses between channels, guides the model to pay more attention to the features with abundant information, and finally improves the accuracy of tiny target detection. We also setup a measured dataset of tiny birds by taking images with optical equipment deployed in airports. The experimental results show that the improved model achieves a certain improvement in detection accuracy and recall rate compared with the original YOLOv5 algorithm.