Reviewerly/exHarmony huggingface dataset:
Benign and malignant classification of mammogram images based on deep learning

Breast cancer is one of the most common malignant tumors in women, which seriously affect women's physical and mental health and even threat to life. At present, mammography is an important criterion for doctors to diagnose breast cancer. However, due to the complex structure of mammogram images, it is relatively difficult for doctors to identify breast cancer features. At present, deep learning is the most mainstream image classification algorithm. Therefore, this study proposes an improved DenseNet neural network model, also known as the DenseNet-II neural network model, for the effective and accurate classification of benign and malignant mammography images. Firstly, the mammogram images are preprocessed. Image normalization avoids interference from light, while the adoption of data enhancement prevents over-fitting cause by small data set. Secondly, the DenseNet neural network model is improved, and a new DenseNet-II neural network model is invented, which is to replace the first convolutional layer of the DenseNet neural network model with the Inception structure. Finally, the pre-processed mammogram datasets are input into AlexNet, VGGNet, GoogLeNet, DenseNet network model and DenseNet-II neural network model, and the experimental results are analyzed and compared. According to the 10-fold cross validation method, the results show that the DenseNet-II neural network model has better classification performance than other network models. The average accuracy of the model reaches 94.55%, which improves the accuracy of the benign and malignant classification of mammogram images. At the same time, it also proves that the model has good generalization and robustness.
Recurrent Attentive Zooming for Joint Crowd Counting and Precise Localization

Crowd counting is a new frontier in computer vision with far-reaching applications particularly in social safety management. A majority of existing works adopt a methodology that first estimates a person-density map and then calculates integral over this map to obtain the final count. As noticed by several prior investigations, the learned density map can significantly deviate from the true person density even though the final reported count is precise. This implies that the density map is unreliable for localizing crowd. To address this issue, this work proposes a novel framework that simultaneously solving two inherently related tasks - crowd counting and localization. The contributions are several-fold. First, our formulation is based on a crucial observation that localization tends to be inaccurate at high-density regions, and increasing the resolution is an effective albeit simple solution for improving localization. We thus propose Recurrent Attentive Zooming Network, which recurrently detects ambiguous image region and zooms it into high resolution for re-inspection. Second, the two tasks of counting and localization mutually reinforce each other. We propose an adaptive fusion scheme that effectively elevates the performance. Finally, a well-defined evaluation metric is proposed for the rarely-explored localization task. We conduct comprehensive evaluations on several crowd benchmarks, including the newly-developed large-scale UCF-QNRF dataset and demonstrate superior advantages over state-of-the-art methods.
Quantum optical neural networks

Abstract Physically motivated quantum algorithms for specific near-term quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). Through numerical simulation and analysis we train the QONN to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation, and one-way quantum repeaters. We consistently demonstrate that our system can generalize from only a small set of training data onto inputs for which it has not been trained. Our results indicate that QONNs are a powerful design tool for quantum optical systems and, leveraging advances in integrated quantum photonics, a promising architecture for next-generation quantum processors.
Sprites and State Channels: Payment Networks that Go Faster Than Lightning

Bitcoin, Ethereum and other blockchain-based cryptocurrencies, as deployed today, cannot support more than several transactions per second. Off-chain payment channels, a “layer 2” solution, are a leading approach for cryptocurrency scaling. They enable two mutually distrustful parties to rapidly send payments between each other and can be linked together to form a payment network, such that payments between any two parties can be routed through the network along a path that connects them. We propose a novel payment channel protocol, called Sprites. The main advantage of Sprites compared with earlier protocols is a reduced “collateral cost,” meaning the amount of money $$\times $$ time that must be locked up before disputes are settled. In the Lightning Network and Raiden, a payment across a path of $$\ell $$ channels requires locking up collateral for $$\varTheta (\ell \varDelta )$$ time, where $$\varDelta $$ is the time to commit an on-chain transaction; every additional node on the path forces an increase in lock time. The Sprites construction provides a constant lock time, reducing the overall collateral cost to $$\varTheta (\ell + \varDelta ).$$ Our presentation of the Sprites protocol is also modular, making use of a generic state channel abstraction. Finally, Sprites improves on prior payment channel constructions by supporting partial withdrawals and deposits without any on-chain transactions.
Hybrid context enriched deep learning model for fine-grained sentiment analysis in textual and visual semiotic modality social data

Detecting sentiments in natural language is tricky even for humans, making its automated detection more complicated. This research proffers a hybrid deep learning model for fine-grained sentiment prediction in real-time multimodal data. It reinforces the strengths of deep learning nets in combination to machine learning to deal with two specific semiotic systems, namely the textual (written text) and visual (still images) and their combination within the online content using decision level multimodal fusion. The proposed contextual ConvNet-SVMBoVW model, has four modules, namely, the discretization, text analytics, image analytics, and decision module. The input to the model is multimodal text, m ε {text, image, info-graphic}. The discretization module uses Google Lens to separate the text from the image, which is then processed as discrete entities and sent to the respective text analytics and image analytics modules. Text analytics module determines the sentiment using a hybrid of a convolution neural network (ConvNet) enriched with the contextual semantics of SentiCircle. An aggregation scheme is introduced to compute the hybrid polarity. A support vector machine (SVM) classifier trained using bag-of-visual-words (BoVW) for predicting the visual content sentiment. A Boolean decision module with a logical OR operation is augmented to the architecture which validates and categorizes the output on the basis of five fine-grained sentiment categories (truth values), namely ‘highly positive,’ ‘positive,’ ‘neutral,’ ‘negative’ and ‘highly negative.’ The accuracy achieved by the proposed model is nearly 91% which is an improvement over the accuracy obtained by the text and image modules individually.
Who Are the Phishers? Phishing Scam Detection on Ethereum via Network Embedding

Recently, blockchain technology has become a topic in the spotlight but also a hotbed of various cybercrimes. Among them, phishing scams on blockchain have been found making a notable amount of money, thus emerging as a serious threat to the trading security of the blockchain ecosystem. In order to create a favorable environment for investment, an effective method for detecting phishing scams is urgently needed in the blockchain ecosystem. To this end, this paper proposes an approach to detect phishing scams on Ethereum by mining its transaction records. Specifically, we first crawl the labeled phishing addresses from two authorized websites and reconstruct the transaction network according to the collected transaction records. Then, by taking the transaction amount and timestamp into consideration, we propose a novel network embedding algorithm called trans2vec to extract the features of the addresses for subsequent phishing identification. Finally, we adopt the oneclass support vector machine (SVM) to classify the nodes into normal and phishing ones. Experimental results demonstrate that the phishing detection method works effectively on Ethereum, and indicate the efficacy of trans2vec over existing state-of-the-art algorithms on feature extraction for transaction networks. This work is the first investigation on phishing detection on Ethereum via network embedding and provides insights into how features of large-scale transaction networks can be embedded.
Dealing with Noise Problem in Machine Learning Data-sets: A Systematic Review

The occurrences of noisy data in data set can significantly impact prediction of any meaningful information. Many empirical studies have shown that noise in data set dramatically led to decreased classification accuracy and poor prediction results. Therefore, the problem of identifying and handling noise in prediction application has drawn considerable attention over past many years. In our study, we performed a systematic literature review of noise identification and handling studies published in various conferences and journals between January 1993 to July 2018. We have identified 79 primary studies are of noise identification and noise handling techniques. After investigating these studies, we found that among the noise identification schemes, the accuracy of identification of noisy instances by using ensemble-based techniques are better than other techniques. But regarding efficiency, usually single based techniques method is better; it is more suitable for noisy data sets. Among noise handling techniques, polishing techniques generally improve classification accuracy than filtering and robust techniques, but it introduced some errors in the data sets.
Cyber Security Awareness, Knowledge and Behavior: A Comparative Study

Cyber-attacks represent a potential threat to information security. As rates of data usage and internet consumption continue to increase, cyber awareness turned to be increasingly urgent. This study focuses on the relationships between cyber security awareness, knowledge and behavior with protection tools among individuals in general and across four countries: Israel, Slovenia, Poland and Turkey in particular. Results show that internet users possess adequate cyber threat awareness but apply only minimal protective measures usually relatively common and simple ones. The study findings also show that higher cyber knowledge is connected to the level of cyber awareness, beyond the differences in respondent country or gender. In addition, awareness is also connected to protection tools, but not to information they were willing to disclose. Lastly, findings exhibit differences between the explored countries that affect the interaction between awareness, knowledge, and behaviors. Results, implications, and recommendations for effective based cyber security training programs are presented and discussed.
Super-resolution reconstruction of turbulent velocity fields using a generative adversarial network-based artificial intelligence framework

A general super-resolution reconstruction strategy was proposed for turbulent velocity fields using a generative adversarial network-based artificial intelligence framework. Two advanced neural networks, i.e., super-resolution generative adversarial network (SRGAN) and enhanced-SRGAN (ESRGAN), were first applied in fluid mechanics to augment the spatial resolution of turbulent flow. As a validation, the flow around a single-cylinder and a more complicated wake flow behind two side-by-side cylinders were experimentally measured using particle image velocimetry. The spatial resolution of the coarse flow field can be successfully augmented by 42 and 82 times with remarkable accuracy. The reconstruction performances of SRGAN and ESRGAN were comprehensively investigated and compared, including an analysis of the recovered instantaneous flow field, statistical flow quantities, and spatial correlations. The results convincingly demonstrated that both models can reconstruct the high-spatial-resolution flow field accurately even in an intricate flow configuration, and ESRGAN can provide a better reconstruction result than SRGAN in the mean and fluctuation flow field.
Security and privacy protection in cloud computing: Discussions and challenges

With the development of cloud computing, privacy security issues have become increasingly prominent, which is of concern to industry and academia. We review the research progress on privacy security issues from the perspective of several privacy security protection technologies in cloud computing. First, we introduce some privacy security risks of cloud computing and propose a comprehensive privacy security protection framework. Second, we show and discuss the research progress of several technologies, such as access control; ciphertext policy attribute-based encryption (CP-ABE); key policy attribute-based encryption (KP-ABE); the fine-grain, multi-authority, revocation mechanism; the trace mechanism; proxy re-encryption (PRE); hierarchical encryption; searchable encryption (SE); and multi-tenant, trust, and a combination of multiple technologies, and then compare and analyze the characteristics and application scope of typical schemes. Last, we discuss current challenges and highlight possible future research directions.
Virtual reality in language learning: a systematic review and implications for research and practice

The increasing popularity of Virtual Reality (VR) has provoked scholars’ and educators’ interest to explore its potential as a learning environment for various fields of education. Along this line, several literature reviews have analysed and synthesised the educational use of VR; however, scholar activity is lacking a recent review of VR on a specific field of interest such as language learning. Thus this paper delineates the contour of scholarly literature on VR as an emerging technology in language teaching and learning. Using 17 high-impact journals and conferences in the fields of Computer-Assisted Language Learning and Educational Technology as a source, 26 scholarly manuscripts were retrieved from 2015 to 2018, analysed and synthesised under the following foci: (a) technologies used, language learning settings and duration of educational activities; (b) benefits and limitations from using VR as an educational tool in the language classroom; (c) future research directions regarding the educational use of VR based on the reviewed literature. This paper argues that VR is an invaluable tool in the language classrooms but entails challenges regarding its technical configuration, as well as its pedagogical grounding. The study concludes with some discussion and implications for researchers and practitioners.
Transmission Dynamics of COVID-19 Outbreaks Associated with Child Care Facilities — Salt Lake City, Utah, April–July 2020

Reports suggest that children aged ≥10 years can efficiently transmit SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19) (1,2). However, limited data are available on SARS-CoV-2 transmission from young children, particularly in child care settings (3). To better understand transmission from young children, contact tracing data collected from three COVID-19 outbreaks in child care facilities in Salt Lake County, Utah, during April 1-July 10, 2020, were retrospectively reviewed to explore attack rates and transmission patterns. A total of 184 persons, including 110 (60%) children had a known epidemiologic link to one of these three facilities. Among these persons, 31 confirmed COVID-19 cases occurred; 13 (42%) in children. Among pediatric patients with facility-associated confirmed COVID-19, all had mild or no symptoms. Twelve children acquired COVID-19 in child care facilities. Transmission was documented from these children to at least 12 (26%) of 46 nonfacility contacts (confirmed or probable cases). One parent was hospitalized. Transmission was observed from two of three children with confirmed, asymptomatic COVID-19. Detailed contact tracing data show that children can play a role in transmission from child care settings to household contacts. Having SARS-CoV-2 testing available, timely results, and testing of contacts of persons with COVID-19 in child care settings regardless of symptoms can help prevent transmission. CDC guidance for child care programs recommends the use of face masks, particularly among staff members, especially when children are too young to wear masks, along with hand hygiene, frequent cleaning and disinfecting of high-touch surfaces, and staying home when ill to reduce SARS-CoV-2 transmission (4).
Privacy-Preserving Multiobjective Sanitization Model in 6G IoT Environments

The next revolution of the smart industry relies on the emergence of the Industrial Internet of Things (IoT) and 5G/6G technology. The properties of such sophisticated communication technologies will change our perspective of information and communication by enabling seamless connectivity and bring closer entities, data, and “things.” Terahertz-based 6G networks promise the best speed and reliability, but they will face new man-in-the-middle attacks. In such critical and high-sensitive environments, the security of data and privacy of information still a big challenge. Without privacy-preserving considerations, the configuration state may be attacked or modified, thus causing security problems and damage to data. In this article, motivated by the need to secure 6G IoT networks, an ant colony optimization (ACO) approach is presented by adopting multiple objectives as well as using transaction deletion to secure confidential and sensitive information. Each ant in the population is represented as a set of possible deletion transactions for hiding sensitive information. We utilize the use of a prelarge concept to assist in the reduction of multiple database scans in the evaluation progress. We then also adopt external solutions to maintain discovered Pareto solutions, thus improving effectiveness to find optimized solutions. Experiments are conducted comparing our methodology to state-of-the-art bioinspired particle swarm optimization (PSO) as well as genetic algorithm (GA). Our strong results clearly show that the designed approach achieves fewer side effects while maintaining low computational cost overall (Chen et al., 2020).
PIoU Loss: Towards Accurate Oriented Object Detection in Complex Environments

Object detection using an oriented bounding box (OBB) can better target rotated objects by reducing the overlap with background areas. Existing OBB approaches are mostly built on horizontal bounding box detectors by introducing an additional angle dimension optimized by a distance loss. However, as the distance loss only minimizes the angle error of the OBB and that it loosely correlates to the IoU, it is insensitive to objects with high aspect ratios. Therefore, a novel loss, Pixels-IoU (PIoU) Loss, is formulated to exploit both the angle and IoU for accurate OBB regression. The PIoU loss is derived from IoU metric with a pixel-wise form, which is simple and suitable for both horizontal and oriented bounding box. To demonstrate its effectiveness, we evaluate the PIoU loss on both anchor-based and anchor-free frameworks. The experimental results show that PIoU loss can dramatically improve the performance of OBB detectors, particularly on objects with high aspect ratios and complex backgrounds. Besides, previous evaluation datasets did not include scenarios where the objects have high aspect ratios, hence a new dataset, Retail50K, is introduced to encourage the community to adapt OBB detectors for more complex environments.
Classes Matter: A Fine-Grained Adversarial Approach to Cross-Domain Semantic Segmentation

Despite great progress in supervised semantic segmentation, a large performance drop is usually observed when deploying the model in the wild. Domain adaptation methods tackle the issue by aligning the source domain and the target domain. However, most existing methods attempt to perform the alignment from a holistic view, ignoring the underlying class-level data structure in the target domain. To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains. We adopt a fine-grained domain discriminator that not only plays as a domain distinguisher, but also differentiates domains at class level. The traditional binary domain labels are also generalized to domain encodings as the supervision signal to guide the fine-grained feature alignment. An analysis with Class Center Distance (CCD) validates that our fine-grained adversarial strategy achieves better class-level alignment compared to other state-of-the-art methods. Our method is easy to implement and its effectiveness is evaluated on three classical domain adaptation tasks, i.e., GTA5 $$\rightarrow $$ Cityscapes, SYNTHIA $$\rightarrow $$ Cityscapes and Cityscapes $$\rightarrow $$ Cross-City. Large performance gains show that our method outperforms other global feature alignment based and class-wise alignment based counterparts. The code is publicly available at https://github.com/JDAI-CV/FADA .
Extensive comparison of physical models for photovoltaic power forecasting

Forecasting the power production of grid-connected photovoltaic (PV) power plants is essential for both the profitability and the prospects of the technology. Physically inspired modelling represents a common approach in calculating the expected power output from numerical weather prediction data. The model selection has a high effect on physical PV power forecasting accuracy, as the difference between the most and least accurate model chains is 13% in mean absolute error (MAE), 12% in root mean square error (RMSE), and 23–33% in skill scores for a PV plant on average. The power forecast performance analysis performed and verified for one-year 15-min resolution production data of 16 PV plants in Hungary for day-ahead and intraday time horizons on all possible combinations of nine direct and diffuse irradiance separation, ten tilted irradiance transposition, three reflection loss, five cell temperature, four PV module performance, two shading loss, and three inverter models. The two most critical calculation steps are identified as irradiance separation and transposition modelling, while the inverter models are the least important. Absolute and squared errors are two conflicting metrics, as the more detailed models result in the lowest MAE, while the simplest ones have the lowest RMSE. Wind speed forecasts have only a marginal effect on the PV power prediction. The results of this study contribute to a deeper understanding of the physical forecasting approach in the research community, while the main conclusions are also beneficial for PV plant owners in preparing their generation forecasts.