Reviewerly/exHarmony huggingface dataset:
Learning to Optimize DAG Scheduling in Heterogeneous Environment

Scheduling job flows efficiently and rapidly on distributed computing clusters is one of huge challenges for daily operation of data centers. In a practical scenario, a single job consists of numerous stages with complex dependency relation represented as a Directed Acyclic Graph (DAG) structure. Nowa-days a data center usually equips with a cluster of heterogeneous computing servers which are different in the hardware/software configuration. From both the cost saving and environmental friendliness, the data centers could benefit a lot from optimizing the job scheduling problems in the heterogeneous environment. Thus the problem has attracted more and more attention from both the industry and academy. In this paper, we propose a task-duplication based learning algorithm, namely LACHESIS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> The second of the Three Fates in ancient Greek mythology, who deter-mines destiny., aiming to optimize the problem. In the proposed approach, it first perceives the topological dependencies between jobs using a reinforcement learning framework and a specially designed graph neural network (GNN) to select the most promising task to be executed. Then the task is assigned to a specific executor with the consideration of duplicating all its precedent tasks according to an expert-designed rules. We have conducted extensive experiments over standard workloads to evaluate the proposed solution. The experimental results suggest that LACHESIS can achieve at most 26.7% reduction of makespan and 35.2% improvement of speedup ratio over seven strong baseline algorithms, including the state-of-the-art heuristics methods and a variety of deep reinforcement learning based algorithms.
Wireless-Powered Cooperative Key Generation for e-Health: A Reservoir Learning Approach

Digital healthcare services are rapidly evolving for new methodologies, including hospital-to-home (H2H) services and intelligent Internet-of-Medical-Things (IIoMT). The sixth generation (6G) technology is considered as the fabric that facilitates the realization of these technologies, creating a paradigm shift towards personalized e-health services. To deal with the high security requirements and the energy constraints of 6G-enabled e-health services, we propose a lightweight learning-based key generation scheme for a pair of wireless-powered nodes in a cooperative communication system, where the legitimate nodes and the intermediate node have low-cost hardware-impaired transceivers. We utilize an echo state network (ESN) to enhance the “randomness distillation” phase, in which the legitimate parties try to obtain a common source of randomness as the raw data for key agreement. The PHY-based observed data is passed to the ESN, containing a reservoir of sparsely connected neurons to compensate for observation mismatches caused by the unbalanced hardware impairments. The output of the ESN can then be utilized to extract the secret key between e-health endpoints. Numerical experiments verify the performance gain of our proposed echo-based approach, resulting in 50% less required inference time compared with a fully-connected neural network (FCNN). Moreover, a performance gain of about 32% in terms of mean-square error (MSE) is achieved compared with a conventional PHY-only scheme.
An Optimized Feature Selection Method Using Ensemble Classifiers in Software Defect Prediction for Healthcare Systems

The healthcare systems are extensively being used with increased focus on safety of patients. Software engineering for healthcare applications is an emerging research area. Detecting defects is a critical step of software development process of healthcare applications. The performance of the Software Defect Prediction model (SDP) depends on the features of healthcare system; irrelevant features decrease the performance of the model. An optimized feature selection technique is needed to recognize and remove the irrelevant features. In this study, a new optimized feature selection technique, i.e., multiobjective Harris Hawk Optimization (HHO), is proposed for binary classification problem with Adaptive Synthetic Sampling (ADASYN) Technique. Multiobjective HHO is proposed with two main objectives, one to reduce the total amount of selected features and the other to maximize the performance of the proposed model. The multiobjective feature selection technique helps to find the optimal solution to achieve the desired objectives and increase the classification performance in terms of accuracy, AUC, precision, recall, and F1-measure. The study conducts an experiment on a healthcare dataset. Six different search techniques (RF, SVM, bagging, adaptive boosting, voting, and stacking) are implemented on the dataset. The proposed model helps to predict the software defects with a significant classification accuracy of 0.990 and AUC score of 0.992.
Breast Cancer Detection from Histology Images Using Deep Feature Selection

Screening of breast cancer from histology images is a popular research problem in medical imaging. Most of the methods in recent days used deep learning models for predicting the same. But, at times, such methods dealt with not only higher-dimensional features but also may suffer from containing irrelevant and sometimes redundant features. To overcome this shortcoming, in the present work, we employ a popularly used particle swarm optimization (PSO) algorithm to obtain the near-optimal feature set. To extract the features from images we first preprocess the images to obtain stain normalized images and then pass them through a pre-trained MobileNet model for extracting the features. We have evaluated our model on a recent dataset, published through ICIAR BACH 2018 grand challenge. The experimental results show an improvement of 6.25% recognition accuracy with around 54% reduced features. We have also compared our result with two state-of-the-art CNN models: InceptionResNet and DenseNet, and we found that the use of MobileNet is better. The capability of the present model is comparable with some state-of-the-art methods on the BACH dataset.
A Mixed Method for Feature Extraction Based on Resonance Filtering

Machine learning tasks such as image classification need to select the features that can describe the image well.The image has individual features and common features, and they are interdependent.If only the individual features of the image are emphasized, the neural network is prone to overfitting.If only the common features of images are emphasized, neural networks will not be able to adapt to diversified learning environments.In order to better integrate individual features and common features, based on skeleton and edge individual features extraction, this paper designed a mixed feature extraction method based on resonance filtering, named resonance layer.Resonance layer is in front of the neural network input layer, using K3M algorithm to extract image skeleton, using the Canny algorithm to extract image border, using resonance filtering to reconstruct training image by filtering image noise, through the common features of the images in the training set and efficient expression of individual characteristics to improve the efficiency of feature extraction of neural network, so as to improve the accuracy of neural network prediction.Taking the fully connected neural network and LeNet-5 neural networks for example, the experiment on handwritten digits database shows that the proposed mixed feature extraction method can improve the accuracy of training while filtering out part of image noise data.
Shadow Removal from UAV Images Based on Color and Texture Equalization Compensation of Local Homogeneous Regions

Due to imaging and lighting directions, shadows are inevitably formed in unmanned aerial vehicle (UAV) images. This causes shadowed regions with missed and occluded information, such as color and texture details. Shadow detection and compensation from remote sensing images is essential for recovering the missed information contained in these images. Current methods are mainly aimed at processing shadows with simple scenes. For UAV remote sensing images with a complex background and multiple shadows, problems inevitably occur, such as color distortion or texture information loss in the shadow compensation result. In this paper, we propose a novel shadow removal algorithm from UAV remote sensing images based on color and texture equalization compensation of local homogeneous regions. Firstly, the UAV imagery is split into blocks by selecting the size of the sliding window. The shadow was enhanced by a new shadow detection index (SDI) and threshold segmentation was applied to obtain the shadow mask. Then, the homogeneous regions are extracted with LiDAR intensity and elevation information. Finally, the information of the non-shadow objects of the homogeneous regions is used to restore the missed information in the shadow objects of the regions. The results revealed that the average overall accuracy of shadow detection is 98.23% and the average F1 score is 95.84%. The average color difference is 1.891, the average shadow standard deviation index is 15.419, and the average gradient similarity is 0.726. The results have shown that the proposed method performs well in both subjective and objective evaluations.
Light in AI: Toward Efficient Neurocomputing With Optical Neural Networks—A Tutorial

In the post Moore’s era, conventional electronic digital computing platforms have encountered escalating challenges to support massively parallel and energy-hungry artificial intelligence (AI) workloads. Intelligent applications in data centers, edge devices, and autonomous vehicles have restricted requirements in throughput, power, and latency, which raises a high demand for a revolutionary neurocomputing solution. Optical neural network (ONN) is a promising hardware platform that could represent a paradigm shift in efficient neurocomputing with its ultra-fast speed, high parallelism, and low energy consumption. In recent years, efforts have been made to facilitate the ONN design stack and push forward the practical application of optical neural accelerators. In this tutorial, we give an overview of state-of-the-art cross-layer co-design methodologies for scalable, robust, and self-learnable ONN designs across the circuit, architecture, and algorithm levels. Besides, we analyze challenges and highlight emerging directions targeting next-generation optics for AI.
Embodied virtual geographies: Linkages between bodies, spaces, and digital environments

Abstract Within an ongoing debate about the relationships between the body and technological experiences within virtual reality (VR), there has hitherto been limited consideration of the spatial. Geographers, meanwhile, have only just begun to engage with VR and its spatialities but have paid less attention to its embodiment. The technology allows users to go beyond merely imagining themselves in a different world, creating a real sense of presence in the digital realm. Immersion and presence in VR are, however, a mix of space, embodiment and the digital. As such, any discussion of VR requires critical consideration of both embodiment and space. This paper therefore explores some of the linkages between bodies, spaces and VR to demonstrate how engagement with VR can enrich geographical scholarship.
AllTogether: Effect of Avatars in Mixed-Modality Conferencing Environments

Visual representation in most video conferencing systems is a binary option between camera on and off. With such systems, voice-only participants might feel left-out, particularly in configurations that situate video participants in a shared virtual environment (e.g., Figure 1). Motivated by the results of a large-scale (n=1140) preliminary study indicating a perceived need for avatar-supported meeting attendance, we developed AllTogether, a system that provides voice-only participants with the option to be represented by an avatar in a call with other video participants. Past research has compared the effect of avatar representations with video and audio, but focused on single-modality calls (i.e. all participants represented as either avatars or video or audio only). We studied the use of our system across three conferencing sessions with 9 participants being represented by a mixture of avatar, video, or voice-only (no visual) representations to better understand users' perceptions and feelings of co-presence when being represented through these modalities. We found that the visual representation of self and others as well as body motion agency affected participants' feelings of co-presence and the level to which participants felt others were present in the video call respectively. Our results highlight the implications of visual realism and agency of control on users' perception of self and others. We propose avatars as a way to expand the binary choice of camera on and off to a spectrum of choices for the user, offer design implications for integrating avatars into video conferencing systems, and update the literature on users' avatar preferences.
Self-admitted technical debt in R: detection and causes

Abstract Self-Admitted Technical Debt (SATD) is primarily studied in Object-Oriented (OO) languages and traditionally commercial software. However, scientific software coded in dynamically-typed languages such as R differs in paradigm, and the source code comments’ semantics are different (i.e., more aligned with algorithms and statistics when compared to traditional software). Additionally, many Software Engineering topics are understudied in scientific software development, with SATD detection remaining a challenge for this domain. This gap adds complexity since prior works determined SATD in scientific software does not adjust to many of the keywords identified for OO SATD, possibly hindering its automated detection. Therefore, we investigated how classification models (traditional machine learning, deep neural networks, and deep neural Pre-Trained Language Models (PTMs)) automatically detect SATD in R packages. This study aims to study the capabilities of these models to classify different TD types in this domain and manually analyze the causes of each in a representative sample. Our results show that PTMs (i.e., RoBERTa) outperform other models and work well when the number of comments labelled as a particular SATD type has low occurrences. We also found that some SATD types are more challenging to detect. We manually identified sixteen causes, including eight new causes detected by our study. The most common cause was failure to remember , in agreement with previous studies. These findings will help the R package authors automatically identify SATD in their source code and improve their code quality. In the future, checklists for R developers can also be developed by scientific communities such as rOpenSci to guarantee a higher quality of packages before submission.
Blockchain-Centered Educational Program Embodies and Advances 2030 Sustainable Development Goals

Education is the foundation of basic human rights and the peace of mankind as well as the pursuit of sustainable development. In this article, we have developed a blockchain-centered educational program contributing to Goal 4, Quality Education, among the Sustainable Development Goals (SDGs) and Education for SD (ESD). The education program featured herein consisted of three lessons in total and has been developed to accommodate gamification techniques. We implemented the education program on 704 elementary school students in Korea for three years from 2019 to 2021. The effectiveness of the education program was primarily analyzed by paired t-test and technical statistics analysis. In a pre-and post-education survey, digital literary, literacy, and numeracy were all significantly improved, with satisfaction level rated at 3.91 out of 5 points. We hope that the blockchain-themed education program proposed herein will provide implications for the promotion of inclusive and equitable quality education.
Classification of Toxicity in Comments using NLP and LSTM

With the increased usage of online social media platforms, there has been a sharp hike in toxic comments. Toxicity must be reduced. Classification of toxicity in comments has been an effective research field with various newly proposed approaches. This research and analysis provide a novel usage of the Natural Language Processing approach to classify the type of toxicity in comments. This analysis intends to interpret the type of comment and determine the various types of toxic classes such as obscene, identity hate, threat, toxic, insult, severe toxic. The input to our algorithm is comments from online platforms like toxic or non-toxic. Our model aims to predict the toxicity class. This project intends to analyze in phases. In Phase I, the objective is to evaluate the toxicity in comments by giving data through various techniques like TDIDF, spacy that helps data to perceive how every word in a comment is classified into a particular category of toxic class. Here, Algorithm will take comments from test data and predict the type of toxicity for test data like a toxic, threat, and so on. In Phase II, Data is analyzed to organize the comments into toxic and non-toxic categories. This promotes us to perceive the particular comment is toxic or not.
Disentangled Bad Weather Removal GAN for Pedestrian Detection

Bad weather such as rain and haze will lower the visibility and contrast of captured images and often occur at the same time, which makes the situation worse. Existing CNN-based methods can achieve impressive results when processing each condition individually. But few works consider removing rain and haze under a unified framework. Besides, these models will experience degraded performance when the distribution of the data changes. In this work, we attempt to find a method that can remove all bad weather without switching between single task models and paired training data for driving scene applications like pedestrian detection. In specific, we adopt a disentanglement strategy to obtain weather layer from subtraction. The statistic distance is calculated between two weather layers from each pipeline. In addition, the weather layer serves as information guidance and input to the reconstruct generator. Experiments on mixed dataset from RainCityscapes and Foggy Cityscapes show that the effectiveness of pedestrian detector is improved. Finally, we contribute a new dataset called Realistic Driving Scene under Bad Weather (RDSBW), which contains over 70K real bad weather images.
LRCN Based Human Activity Recognition from Video Data

Nowadays, people’s lives have been facilitated by the advancement and widespread use of technology. Deep learning has recently developed as an area of artificial intelligence that enables the development of intelligent software and machines capable of assisting people with all of their daily activities. Human Activity Recognition (HAR) is a cutting-edge technique that is frequently used to help the elderly and individuals with Vascular dementia. Deep Learning which is a subclass of Machine Learning-based Complex Artificial Neural Networks (ANNs) is mostly used to identify human activity from smartphone data with higher accuracy. In this paper, we develop a ConvLSTM and LRCN based model to detect human activities. The model leverages a large number of videos from the publicly available datasets UCF50 and HMDB51, which contain a wide range of activity attributes, to build the statistical model. The findings demonstrate that the LRCN based model is more accurate than other models.
Research on Signal Modulation Classification under Low SNR Based on ResNext Network

To address the shortcomings of existing methods such as low recognition accuracy and poor anti-interference performance under low signal-to-noise ratios, this paper proposes the RFSE-ResNeXt (Residual-fusion squeeze–excitation aggregated residual for networks, RFSE-ResNeXt) network. In this paper, we improve the residual structure of the network based on the ResNeXt network and then introduce the compressed excitation structure to improve the generalization ability of the network. The improvement of the residual structure of the network leads to a good improvement in the overall recognition accuracy of the network; meanwhile, the compressed excitation structure improves the confusion phenomenon when the network faces complex signals with low signal-to-noise ratios. The experimental results show that the proposed network improves the recognition accuracy by 4% on average at a very low SNR of -10dB and reduces the misclassification of AM-DSB into CPFSK by about 27%.
Deep belief rule based photovoltaic power forecasting method with interpretability

Accurate prediction of photovoltaic (PV) output power is of great significance for reasonable scheduling and development management of power grids. In PV power generation prediction system, there are two problems: the uncertainty of PV power generation and the inexplicability of the prediction result. The belief rule base (BRB) is a rule-based modeling method and can deal with uncertain information. Moreover, the modeling process of BRB has a certain degree of interpretability. However, rule explosion and the inexplicability of the optimized model limit the modeling ability of BRB in complex systems. Thus, a PV output power prediction model is proposed based on a deep belief rule base with interpretability (DBRB-I). In the DBRB-I model, the deep BRB structure is constructed to solve the rule explosion problem, and inefficient rules are simplified by a sensitivity analysis of the rules, which reduces the complexity of the model. Moreover, to ensure that the interpretability of the model is not destroyed, a new optimization method based on the projection covariance matrix adaptation evolution strategy (P-CMA-ES) algorithm is designed. Finally, a case study of the prediction of PV output power is conducted to illustrate the effectiveness of the proposed method.
An Improved YOLOv5-Based Tapping Trajectory Detection Method for Natural Rubber Trees

The object detection algorithm is one of the core technologies of the intelligent rubber tapping robot, but most of the existing detection algorithms cannot effectively meet the tapping trajectory detection of natural rubber trees in the complex forest environment. This paper proposes a tapping trajectory detection method for natural rubber trees based on an improved YOLOv5 model to accomplish fast and accurate detection. Firstly, the coordinate attention (CA) mechanism is added to the Backbone network to embed the location information into the channel attention, which effectively improves the detection accuracy. Secondly, a module called convolution and GhostBottleneck (CGB) is designed, based on the Ghost module, to substitute the Cross Stage Partial Network (CSP) module in the Neck network, which ensures the detection accuracy while reducing model parameters. Finally, the EIoU loss function is introduced to enable a more accurate regression of the model. The experimental results show that the overall performance of the YOLOv5-CCE model outperforms the original YOLOv5 and other classical lightweight detection algorithms. Compared with the original YOLOv5 model, the YOLOv5-CCE model has a 2.1% improvement in mAP value, a 2.5% compression of model parameters, and a 7.0% reduction in the number of floating point operations (FLOPs). Therefore, the improved model can fully meet the requirements of real-time detection, providing a robust detection method for rubber tapping robots.