Reviewerly/exHarmony huggingface dataset:
RSigELU: A nonlinear activation function for deep neural networks

In deep learning models, the inputs to the network are processed using activation functions to generate the output corresponding to these inputs. Deep learning models are of particular importance in analyzing big data with numerous parameters and forecasting and are useful for image processing, natural language processing, object recognition, and financial forecasting. Sigmoid and tangent activation functions, which are traditional activation functions, are widely used in deep learning models. However, the sigmoid and tangent activation functions face the vanishing gradient problem. In order to overcome this problem, the ReLU activation function and its derivatives were proposed in the literature. However, there is a negative region problem in these activation functions. In this study, novel RSigELU activation functions, such as single-parameter RSigELU (RSigELUS) and double-parameter (RSigELUD), which are a combination of ReLU, sigmoid, and ELU activation functions, were proposed. The proposed RSigELUS and RSigELUD activation functions can overcome the vanishing gradient and negative region problems and can be effective in the positive, negative, and linear activation regions. Performance evaluation of the proposed RSigELU activation functions was performed on the MNIST, Fashion MNIST, CIFAR-10, and IMDb Movie benchmark datasets. Experimental evaluations showed that the proposed activation functions perform better than other activation functions.
Action recognition using kinematics posture feature on 3D skeleton joint locations

Action recognition is a very widely explored research area in computer vision and related fields. We propose Kinematics Posture Feature (KPF) extraction from 3D joint positions based on skeleton data for improving the performance of action recognition. In this approach, we consider the skeleton 3D joints as kinematics sensors. We propose Linear Joint Position Feature (LJPF) and Angular Joint Position Feature (AJPF) based on 3D linear joint positions and angles between bone segments. We then combine these two kinematics features for each video frame for each action to create the KPF feature sets. These feature sets encode the variation of motion in the temporal domain as if each body joint represents kinematics position and orientation sensors. In the next stage, we process the extracted KPF feature descriptor by using a low pass filter, and segment them by using sliding windows with optimized length. This concept resembles the approach of processing kinematics sensor data. From the segmented windows, we compute the Position-based Statistical Feature (PSF). These features consist of temporal domain statistical features (e.g., mean, standard deviation, variance, etc.). These statistical features encode the variation of postures (i.e., joint positions and angles) across the video frames. For performing classification, we explore Support Vector Machine (Linear), RNN, CNNRNN, and ConvRNN model. The proposed PSF feature sets demonstrate prominent performance in both statistical machine learning- and deep learning-based models. For evaluation, we explore five benchmark datasets namely UTKinect-Action3D, Kinect Activity Recognition Dataset (KARD), MSR 3D Action Pairs, Florence 3D, and Office Activity Dataset (OAD). To prevent overfitting, we consider the leave-one-subject-out framework as the experimental setup and perform 10-fold cross-validation. Our approach outperforms several existing methods in these benchmark datasets and achieves very promising classification performance.
Attachment styles moderate customer responses to frontline service robots: Evidence from affective, attitudinal, and behavioral measures

Abstract Despite the growing application of interactive technologies like service robots in customer service, there is limited understanding about how customers respond to interactions with frontline service robots compared to those with frontline human employees. Moreover, it is unclear whether all customers respond to the interaction with frontline service robots in the same way. Our research looks at how individual differences in social behaviors, specifically in customers' attachment styles, influence three types of customer responses: affective responses (experienced pleasantness), attitudinal responses (perceived empathy, satisfaction), and behavioral responses (word‐of‐mouth). Three experimental studies reveal that customers with low (vs. high) scores on anxious attachment style (AAS) measures respond more negatively to frontline service robot (compared to a frontline human agent). We investigate alternative explanations for these findings, such as robots' level of anthropomorphism and we show that human‐likeness features such as voice type and level of human‐like physical appearance, cannot explain our findings. Our results indicate that for low‐AAS customers replacing frontline human service agent with frontline robot undermines customer attitude and behavioral responses to service robots, leading to possible implications on customer segmentation, targeting, and marketing communication.
Attention-Based Multi-Source Domain Adaptation

Multi-source domain adaptation (MSDA) aims to transfer knowledge from multi-source domains to one target domain. Inspired by single-source domain adaptation, existing methods solve MSDA by aligning the data distributions between the target domain and each source domain. However, aligning the target domain with the dissimilar source domain would harm the representation learning. To address the above issue, an intuitive motivation of MSDA is using the attention mechanism to enhance the positive effects of the similar domains, and suppress the negative effects of the dissimilar domains. Therefore, we propose Attention-Based Multi-Source Domain Adaptation (ABMSDA) by considering the domain correlations to alleviate the effects caused by dissimilar domains. To obtain the domain correlations between source and target domains, ABMSDA firstly trains a domain recognition model to calculate the probability that the target images belong to each source domain. Based on the domain correlations, Weighted Moment Distance (WMD) is proposed to pay more attention on the source domains with higher similarities. Furthermore, Attentive Classification Loss (ACL) is developed to constrain that the feature extractor can generate the alignment and discriminative visual representations. The evaluations on two benchmarks demonstrate the effectiveness of the proposed model, e.g., an average of 6.1% improvement on the challenging DomainNet dataset.
Deep convolutional neural network and emotional learning based breast cancer detection using digital mammography

Breast cancer is one of the deadly diseases among women. However, the chances of death are highly reduced if it gets diagnosed and treated at its early stage. Mammography is one of the reliable methods used by the radiologist to detect breast cancer at its initial stage. Therefore, an automatic and secure breast cancer detection system that accurately detects abnormalities not only increases the radiologist's diagnostic confidence but also provides more objective evidence. In this work, an automatic Diverse Features based Breast Cancer Detection (DFeBCD) system is proposed to classify a mammogram as normal or abnormal. Four sets of distinct feature types are used. Among them, features based on taxonomic indexes, statistical measures and local binary patterns are static. The proposed DFeBCD dynamically extracts the fourth set of features from mammogram images using a highway-network based deep convolution neural network (CNN). Two classifiers, Support Vector Machine (SVM) and Emotional Learning inspired Ensemble Classifier (ELiEC), are trained on these distinct features using a standard IRMA mammogram dataset. The reliability of the system performance is ensured by applying 5-folds cross-validation. Through experiments, we have observed that the performance of the DFeBCD system on dynamically generated features through highway network-based CNN is better than that of all the three individual sets of ad-hoc features. Furthermore, the hybridization of all four types of features improves the system's performance by nearly 2–3%. The performance of both the classifiers is comparable using the individual sets of ad-hoc features. However, the ELiEC classifier's performance is better than SVM using both hybrid and dynamic features.
MobileNetV3 for Image Classification

Convolution neural network (CNN) is a kind of deep neural networks, which extracts image features through multiple convolution layers and is widely used in image classifications. With the increasing number of image data processed by mobile devices, application of neural network for mobile terminals becomes popular. However, these networks need massive computation and advanced hardware support, making them difficult to adapt to mobile devices. This paper demonstrates that MobileNetV3 can get a superior balance between efficiency and accuracy for real-life image classification tasks on mobile terminals. In our experiments, classification performances are compared among MobileNetV3 and several other commonly used pre-trained CNN models on various image datasets. The chosen datasets are all good representatives of the application scenarios of mobile devices. The result shows that as a lightweight neural network, MobileNetV3 achieved good accuracy performance in an effective manner compared to other large networks. Furthermore, ROC confirmed the advantages of MobileNetV3 over other experimented models. Some conjectures are also brought out about the characteristics of image datasets that are suitable for MobileNetV3.
Empirical research on the blockchain adoption – based on TAM

Among the modern digital technologies that are reshaping many aspects of our society, blockchain is one of the most innovative, and this cutting-edge technology has drawn a great deal of public attention. However, little research is available on the actual effect of trust, which is a key core attraction for the blockchain technology. Furthermore, few studies have investigated the relationship between trust and user acceptance. In this paper, we develop a research model based on the technology acceptance model (TAM) and the core features of blockchain technology, and we conduct an empirical study. We collect 254 questionnaires about blockchain applications and analyse the responses with SmartPLS3.0. The results show that constructs such as trust and information quality have positive effects on the users’ behavioural intentions, except in terms of output quality. The findings have both theoretical significance and empirical insights, which will be helpful for the further development of blockchain theory while guiding the operation of relevant enterprises.
Face Mask Detection Using OpenCV

The COVID-19 pandemic is causing a worldwide emergency in healthcare. This virus mainly spreads through droplets which emerge from a person infected with coronavirus and poses a risk to others. The risk of transmission is highest in public places. One of the best ways to stay safe from getting infected is wearing a face mask in open territories as indicated by the World Health Organization (WHO). In this project, we propose a method which employs TensorFlow and OpenCV to detect face masks on people. A bounding box drawn over the face of the person describes weather the person is wearing a mask or not. If a person's face is stored in the database, it detects the name of the person who is not wearing face mask and an email will be sent to that person warning them that they are not wearing a mask so that they can take precautions.
Hybrid Transfer Learning and Broad Learning System for Wearing Mask Detection in the COVID-19 Era

In the era of Corona Virus Disease 2019 (COVID-19), wearing a mask can effectively protect people from infection risk and largely decrease the spread in public places, such as hospitals and airports. This brings a demand for the monitoring instruments that are required to detect people who are wearing masks. However, this is not the objective of existing face detection algorithms. In this article, we propose a two-stage approach to detect wearing masks using hybrid machine learning techniques. The first stage is designed to detect candidate wearing mask regions as many as possible, which is based on the transfer model of Faster_RCNN and InceptionV2 structure, while the second stage is designed to verify the real facial masks using a broad learning system. It is implemented by training a two-class model. Moreover, this article proposes a data set for wearing mask detection (WMD) that includes 7804 realistic images. The data set has 26403 wearing masks and covers multiple scenes, which is available at "https://github.com/BingshuCV/WMD." Experiments conducted on the data set demonstrate that the proposed approach achieves an overall accuracy of 97.32% for simple scene and an overall accuracy of 91.13% for the complex scene, outperforming the compared methods.
GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds

We present GANcraft, an unsupervised neural rendering framework for generating photorealistic images of large 3D block worlds such as those created in Minecraft. Our method takes a semantic block world as input, where each block is assigned a semantic label such as dirt, grass, or water. We represent the world as a continuous volumetric function and train our model to render view-consistent photorealistic images for a user-controlled camera. In the absence of paired ground truth real images for the block world, we devise a training technique based on pseudo-ground truth and adversarial training. This stands in contrast to prior work on neural rendering for view synthesis, which requires ground truth images to estimate scene geometry and view-dependent appearance. In addition to camera trajectory, GANcraft allows user control over both scene semantics and output style. Experimental results with comparison to strong baselines show the effectiveness of GANcraft on this novel task of photorealistic 3D block world synthesis. The project website is available at https://nvlabs.github.io/GANcraft/.
On the Just-In-Time Discovery of Profit-Generating Transactions in DeFi Protocols

Decentralized Finance (DeFi) is a blockchain-asset-enabled finance ecosystem with millions of daily USD transaction volume, billions of locked up USD, as well as a plethora of newly emerging protocols (for lending, staking, and exchanges). Because all transactions, user balances, and total value locked in DeFi are publicly readable, a natural question that arises is: how can we automatically craft profitable transactions across the intertwined DeFi platforms?In this paper, we investigate two methods that allow us to automatically create profitable DeFi trades, one well-suited to arbitrage and the other applicable to more complicated settings. We first adopt the Bellman-Ford-Moore algorithm with DeFiPoser-ARB and then create logical DeFi protocol models for a theorem prover in DeFiPoser-SMT. While DeFiPoser-ARB focuses on DeFi transactions that form a cycle and performs very well for arbitrage, DeFiPoser-SMT can detect more complicated profitable transactions. We estimate that DeFiPoser-ARB and DeFiPoser-SMT can generate an average weekly revenue of 191.48 ETH (76,592 USD) and 72.44 ETH (28,976 USD) respectively, with the highest transaction revenue being 81.31 ETH (32,524 USD) and 22.40 ETH (8,960 USD) respectively. We further show that DeFiPoser-SMT finds the known economic bZx attack from February 2020, which yields 0.48M USD. Our forensic investigations show that this opportunity existed for 69 days and could have yielded more revenue if exploited one day earlier. Our evaluation spans 150 days, given 96 DeFi protocol actions, and 25 assets.Looking beyond the financial gains mentioned above, forks deteriorate the blockchain consensus security, as they increase the risks of double-spending and selfish mining. We explore the implications of DeFiPoser-ARB and DeFiPoser-SMT on blockchain consensus. Specifically, we show that the trades identified by our tools exceed the Ethereum block reward by up to 874×. Given optimal adversarial strategies provided by a Markov Decision Process (MDP), we quantify the value threshold at which a profitable transaction qualifies as Miner Extractable Value (MEV) and would incentivize MEV-aware miners to fork the blockchain. For instance, we find that on Ethereum, a miner with a hash rate of 10% would fork the blockchain if an MEV opportunity exceeds 4× the block reward.
Multi-objective hybrid genetic algorithm for task scheduling problem in cloud computing

Abstract The cloud computing systems are sorts of shared collateral structure which has been in demand from its inception. In these systems, clients are able to access existing services based on their needs and without knowing where the service is located and how it is delivered, and only pay for the service used. Like other systems, there are challenges in the cloud computing system. Because of a wide array of clients and the variety of services available in this system, it can be said that the issue of scheduling and, of course, energy consumption is essential challenge of this system. Therefore, it should be properly provided to users, which minimizes both the cost of the provider and consumer and the energy consumption, and this requires the use of an optimal scheduling algorithm. In this paper, we present a two-step hybrid method for scheduling tasks aware of energy and time called Genetic Algorithm and Energy-Conscious Scheduling Heuristic based on the Genetic Algorithm. The first step involves prioritizing tasks, and the second step consists of assigning tasks to the processor. We prioritized tasks and generated primary chromosomes, and used the Energy-Conscious Scheduling Heuristic model, which is an energy-conscious model, to assign tasks to the processor. As the simulation results show, these results demonstrate that the proposed algorithm has been able to outperform other methods.
An Incentive Mechanism for Cross-Silo Federated Learning: A Public Goods Perspective

In cross-silo federated learning (FL), organizations cooperatively train a global model with their local data. The organizations, however, may be heterogeneous in terms of their valuation on the precision of the trained global model and their training cost. Meanwhile, the computational and communication resources of the organizations are non-excludable public goods. That is, even if an organization does not perform any local training, other organizations cannot prevent that organization from using the outcome of their resources (i.e., the trained global model). To address the organization heterogeneity and the public goods feature, in this paper, we formulate a social welfare maximization problem and propose an incentive mechanism for cross-silo FL. With the proposed mechanism, organizations can achieve not only social welfare maximization but also individual rationality and budget balance. Moreover, we propose a distributed algorithm that enables organizations to maximize the social welfare without knowing the valuation and cost of each other. Our simulations with MNIST dataset show that the proposed algorithm converges faster than a benchmark method. Furthermore, when organizations have higher valuation on precision, the proposed mechanism and algorithm are more beneficial in the sense that the organizations can achieve higher social welfare through participating in cross-silo FL.
Assessment of solar and wind energy complementarity in Algeria

The recent transition towards sustainable energy resources, aiming for the decarbonisation of the electrical power sector has gained global and regional importance. Therefore, there is a great need to create roadmaps to efficiently achieve this goal. In this regard, this paper evaluates the spatial and temporal complementarity between solar and wind energy in Algeria for different timescales. To this end, a grid with 0.5° x 0.5° spatial resolution that covers the Algerian territory is created and then representative datasets of wind speed and solar radiation were obtained for each point. The Spearman’s rank correlation coefficient is applied as an index to measure the relationship between the underlying resources. The results of this work indicate the existence of moderate complementarity on daily timescale in the coastal regions. The highest complementarity is observed in the province of Annaba, with a complementarity index equals −0.52. Whereas, the south of the country is characterized by synchronised solar and wind resources. Further analysis reveals that the assessment of complementarity based on power generation or resource potential leads almost to the same results. The relationship analysis shows good concordance with a determination coefficient of 0.98, which means that the components specifications have a low impact on the complementarity index. Additionally, dispersed wind systems show a promising smoothing effect, while less spatial complementarity is observed for solar-solar and solar-wind scenarios. The analysis performed in this paper provides informative guidelines for future investments in Algeria by identifying sites with the highest complementarity between solar and wind resources.
Achieving a quantum smart workforce

Interest in building dedicated Quantum Information Science and Engineering (QISE) education programs has greatly expanded in recent years. These programs are inherently convergent, complex, often resource intensive and likely require collaboration with a broad variety of stakeholders. In order to address this combination of challenges, we have captured ideas from many members in the community. This manuscript not only addresses policy makers and funding agencies (both public and private and from the regional to the international level) but also contains needs identified by industry leaders and discusses the difficulties inherent in creating an inclusive QISE curriculum. We report on the status of eighteen post-secondary education programs in QISE and provide guidance for building new programs. Lastly, we encourage the development of a comprehensive strategic plan for quantum education and workforce development as a means to make the most of the ongoing substantial investments being made in QISE.
When Does Contrastive Visual Representation Learning Work?

Recent self-supervised representation learning techniques have largely closed the gap between supervised and unsupervised learning on ImageNet classification.While the particulars of pretraining on ImageNet are now relatively well understood, the field still lacks widely accepted best practices for replicating this success on other datasets.As a first step in this direction, we study contrastive selfsupervised learning on four diverse large-scale datasets.By looking through the lenses of data quantity, data domain, data quality, and task granularity, we provide new insights into the necessary conditions for successful selfsupervised learning.Our key findings include observations such as: (i) the benefit of additional pretraining data beyond 500k images is modest, (ii) adding pretraining images from another domain does not lead to more general representations, (iii) corrupted pretraining images have a disparate impact on supervised and self-supervised pretraining, and (iv) contrastive learning lags far behind supervised learning on fine-grained visual classification tasks.