Reviewerly/exHarmony huggingface dataset:
Dynamic Bank Learning for Semi-supervised Federated Image Diagnosis with Class Imbalance

Despite recent progress on semi-supervised federated learning (FL) for medical image diagnosis, the problem of imbalanced class distributions among unlabeled clients is still unsolved for real-world use. In this paper, we study a practical yet challenging problem of class imbalanced semi-supervised FL (imFed-Semi), which allows all clients to have only unlabeled data while the server just has a small amount of labeled data. This imFed-Semi problem is addressed by a novel dynamic bank learning scheme, which improves client training by exploiting class proportion information. This scheme consists of two parts, i.e., the dynamic bank construction to distill various class proportions for each local client, and the sub-bank classification to impose the local model to learn different class proportions. We evaluate our approach on two public real-world medical datasets, including the intracranial hemorrhage diagnosis with 25,000 CT slices and skin lesion diagnosis with 10,015 dermoscopy images. The effectiveness of our method has been validated with significant performance improvements (7.61% and 4.69%) compared with the second-best on the accuracy, as well as comprehensive analytical studies. Code is available at https://github.com/med-air/imFedSemi .
Effects of Teacher Engagement on Students’ Achievement in an Online English as a Foreign Language Classroom: The Mediating Role of Autonomous Motivation and Positive Emotions

As an important factor promoting students' learning behavior and achievement, teacher engagement has been largely neglected in the research literature on English as a foreign language (EFL) and applied linguistics. Moreover, the few studies have focused more on conventional classrooms rather than online learning contexts and failed to reveal how teacher engagement in the online foreign language classroom affected students' achievement. The present study assessed 546 university students in China using self-report questionnaires to examine the relationship between teacher engagement and students' achievement in an online EFL course over an 18-week semester, taking into account the possible mediating effects of autonomous motivation and positive academic emotions. The results showed that teacher engagement exerted a direct and positive impact on students' English achievement. Students' autonomous motivation and enjoyment mediated the association between teacher engagement and English achievement, but the mediating effects of relief were not significant. Additionally, teacher engagement affected students' English achievement through the chain mediation of autonomous motivation and positive academic emotions (enjoyment and relief). Relief displayed a smaller effect on students' English achievement than enjoyment did. These findings elucidate the impact of teacher engagement on students' English achievement in the online environment and support the utility of self-determination theory and control-value theory in explaining foreign language learning. Directions for future research and implications for education are also presented.
Blockchain and deep learning based trust management for Internet of Vehicles

Internet of Vehicles (IoVs) works as the most advanced component of Intelligent Transportation Systems (ITSs). In IoVs, vehicles are able to communicate with nearby vehicles or RoadSide Units (RSUs). Traffic safety and efficiency can be provided by collecting and uploading real-time traffic information through vehicles, as well as broadcasting information by RSUs. However, there may be malicious vehicles in the network uploading false information, which will lead to serious traffic problems. To alleviate this problem, a trust management system based on blockchain technology is proposed in this paper. In this system, vehicles in the network firstly collect information about their surroundings and then upload valid information to nearby RSUs. To prevent malicious vehicles from uploading false messages, this paper designs a deep learning based verification model to calculate the trustworthiness of uploaded messages, and to further obtain the credibility scores of vehicles using the calculated results, and detect malicious vehicles accordingly. Moreover, a public blockchain framework is proposed and a Proof-Of-Trust (POT) consensus algorithm is designed. Vehicles are motivated to report true and valid information, and are penalized for uploading false data under this framework. Simulation results show that this mechanism can effectively detect malicious vehicles and motivate unfamiliar vehicles to upload true and reliable information to achieve trust management in the open and dynamic vehicular network environments.
Interpretable machine learning for brain tumour analysis using MRI and whole slide images

Tumour-Analyser is a web application that classifies a brain tumour into three classes, namely, lower-grade astrocytoma (A), oligodendroglioma (O), glioblastoma & diffuse astrocytic glioma (G). We use a magnetic resonance imaging (MRI) sequence and a whole slide imaging (WSI) that are classified using DenseNet and ResNet, respectively. The tool interprets the decision-making process of each classification model. Tumour-Analyser provides a viable solution to the less human understandability of existing models due to the inherent black-box nature of deep learning models and less transparency, by applying interpretability.
Approaching microwave photon sensitivity with Al Josephson junctions

Here, we experimentally test the applicability of an aluminium Josephson junction of a few micrometers size as a single photon counter in the microwave frequency range. We have measured the switching from the superconducting to the resistive state through the absorption of 10 GHz photons. The dependence of the switching probability on the signal power suggests that the switching is initiated by the simultaneous absorption of three and more photons, with a dark count time above 0.01 s.
Multiple-UAV Reinforcement Learning Algorithm Based on Improved PPO in Ray Framework

Distributed multi-agent collaborative decision-making technology is the key to general artificial intelligence. This paper takes the self-developed Unity3D collaborative combat environment as the test scenario, setting a task that requires heterogeneous unmanned aerial vehicles (UAVs) to perform a distributed decision-making and complete cooperation task. Aiming at the problem of the traditional proximal policy optimization (PPO) algorithm’s poor performance in the field of complex multi-agent collaboration scenarios based on the distributed training framework Ray, the Critic network in the PPO algorithm is improved to learn a centralized value function, and the muti-agent proximal policy optimization (MAPPO) algorithm is proposed. At the same time, the inheritance training method based on course learning is adopted to improve the generalization performance of the algorithm. In the experiment, MAPPO can obtain the highest average accumulate reward compared with other algorithms and can complete the task goal with the fewest steps after convergence, which fully demonstrates that the MAPPO algorithm outperforms the state-of-the-art.
Subverting Skynet: The Strategic Promise of Lethal Autonomous Weapons and the Perils of Exploitation

Lethal Autonomous Weapons Systems (LAWS) promise a revolution in warfare by increasing the lethality of force while reducing the costs of war. Yet these gains come at the cost of significant yet underappreciated perils. LAWS are vulnerable to subversion, allowing adversaries to degrade or disable these systems or even turn them against their makers. Subversion involves exploiting flaws in complex systems to make them behave in unexpected ways. It is possible because, like other computer systems, the behavior of LAWS is determined by logical rules and routines. These rules and routines inevitably contain flaws, creating vulnerabilities that adversaries can exploit. This paper identifies three avenues of subversion: (1) manipulating the algorithm itself during the design process, (2) poisoning the data used to train the artificial intelligence operating the LAWS, and (3) manipulating physical objects LAWS are trained to respond to. This potential for subversion creates fundamental uncertainty for strategic planners, military commanders, and soldiers in the field. LAWS are powerful capabilities that may win wars, yet they may also become liabilities that lead to defeat against crafty adversaries. Hence, fulfilling the strategic promise of LAWS requires mitigating their vulnerabilities. Examining possible mitigations, the paper shows that technical fixes are currently unavailable, necessitating strategic solutions. It identifies two possible solutions. The first is employing counterintelligence strategies and tactics to detect, neutralize, and pre-empt attempts at subversion. The second is adopting a force structure model that maintains human superiority to neutralize rogue LAWS if necessary. However, both solutions reduce operational effectiveness and the strategic value of LAWS, thus forfeiting some of the core advantages these new systems promise. Consequently, the paper concludes that the strategic challenges of deploying LAWS currently outweigh the opportunities, necessitating a cautious approach and a greater prioritization of strategy development.
Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey

Performing the most up-to-date and accurate vulnerability assessment is key to an effective earthquake disaster management. In cities like Istanbul (Turkey) with a high rate of urban expansion, the safety of the residents must not be neglected. The challenges in such studies are related to the lack of a training dataset. Some areas are highly prone to earthquakes, however, there have been no earthquakes in those areas recently. This research proposes and tests the ability of the k-mean clustering method to create the training dataset for earthquake vulnerability analysis. Subsequently, the derived sample dataset was used in four state-of-the-art models i.e. Decision Tree (DT), Support Vector Machine (SVM), Self-Organizing Map (SOM) and Logistic Regression (LR) for assessing earthquake vulnerability in Istanbul, Turkey. The multicollinearity among the variables was determined using tolerance (TOL) and variance inflation factor (VIF) which revealed no multicollinearity among the variables. The highest VIF belonged to the “distance to faults” factor. Vulnerability related variables were classified, weighed and using k-mean clustering, a training database was constructed. Then, the standardized variables were keyed in as input alongside the training site maps into DT, SVM, SOM and LR to construct an Earthquake Vulnerability Map (EVM). EVMs were created for all the four samples and graded as very-low, relatively-low, moderate, high, or extremely-high. Several statistical metrics such as Area under the ROC curve (AUC), sensitivity (SST), specificity (SPF), root-mean-squared-errors (RMSE), positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the accuracy of the resultant maps. The highest and lowest AUC prediction rates were 0.962 and 0.912 from the K-means-SOM and K-means-LR models, respectively. The lowest RSME results using the testing dataset (0.329) belonged to K-means-SVM model. The region's most prone vulnerability maps were found to be in the districts 9, 13, 20, 21 and 35. Finally, an analysis of the buildings and population distribution was carried out among the 39 districts of Istanbul considering the SOM outcomes. The research outcome could help in laying strategies for earthquake preparedness in the Istanbul city.
Application of cloud computing in the process of professional training of physics teachers

The use of cloud computing in the form of virtual laboratory workshops is a way to solve the existing problems of training future physics teachers, especially in the widespread use of e-learning. The world educational community has developed and is using a variety of electronic educational resources combined in collections and libraries, and the use of them in the educational activities of future physics teachers helps not only to diversify educational material but also to increase digital competence of all participants in the educational process. The study analyzed the available virtual laboratory workshops, proposed a scheme for the use of physical simulations, gave examples of their use in the study of physics.
"Did you miss my comment or what?"

Online toxicity is ubiquitous across the internet and its negative impact on the people and that online communities that it effects has been well documented. However, toxicity manifests differently on various platforms and toxicity in open source communities, while frequently discussed, is not well understood. We take a first stride at understanding the characteristics of open source toxicity to better inform future work on designing effective intervention and detection methods. To this end, we curate a sample of 100 toxic GitHub issue discussions combining multiple search and sampling strategies. We then qualitatively analyze the sample to gain an understanding of the characteristics of open-source toxicity. We find that the pervasive forms of toxicity in open source differ from those observed on other platforms like Reddit or Wikipedia. In our sample, some of the most prevalent forms of toxicity are entitled, demanding, and arrogant comments from project users as well as insults arising from technical disagreements. In addition, not all toxicity was written by people external to the projects; project members were also common authors of toxicity. We also discuss the implications of our findings. Among others we hope that our findings will be useful for future detection work.
DAWAR

With the ever-increasing popularity of microservice architecture, a considerable number of enterprises or organizations have encapsulated their complex business services into various lightweight functions as published them accessible APIs (Application Programming Interfaces). Through keyword search, a software developer could select a set of APIs from a massive number of candidates to implement the functions of a complex mashup, which reduces the development cost significantly. However, traditional keyword search methods for APIs often suffer from several critical issues such as functional compatibility and limited diversity in search results, which may lead to mashup creation failures and lower development productivity. To deal with these challenges, this paper designs DAWAR, a diversity-aware Web APIs recommendation approach that finds diversified and compatible APIs for mashup creation. Specifically, the APIs recommendation problem for mashup creating is modelled as a graph search problem that aims to find the minimal group Steiner trees in a correlation graph of APIs. DAWAR innovatively employs the determinantal point processes to diversify the recommended results. Empirical evaluation is performed on commonly-used real-world datasets, and the statistic results show that DAWAR is able to achieve significant improvements in terms of recommendation diversity, accuracy, and compatibility.
Partially Supervised Compatibility Modeling

Fashion Compatibility Modeling (FCM), which aims to automatically evaluate whether a given set of fashion items makes a compatible outfit, has attracted increasing research attention. Recent studies have demonstrated the benefits of conducting the item representation disentanglement towards FCM. Although these efforts have achieved prominent progress, they still perform unsatisfactorily, as they mainly investigate the visual content of fashion items, while overlooking the semantic attributes of items (e.g., color and pattern), which could largely boost the model performance and interpretability. To address this issue, we propose to comprehensively explore the visual content and attributes of fashion items towards FCM. This problem is non-trivial considering the following challenges: a) how to utilize the irregular attribute labels of items to partially supervise the attribute-level representation learning of fashion items; b) how to ensure the intact disentanglement of attribute-level representations; and c) how to effectively sew the multiple granulairites (i.e, coarse-grained item-level and fine-grained attribute-level) information to enable performance improvement and interpretability. To address these challenges, in this work, we present a partially supervised outfit compatibility modeling scheme (PS-OCM). In particular, we first devise a partially supervised attribute-level embedding learning component to disentangle the fine-grained attribute embeddings from the entire visual feature of each item. We then introduce a disentangled completeness regularizer to prevent the information loss during disentanglement. Thereafter, we design a hierarchical graph convolutional network, which seamlessly integrates the attribute- and item-level compatibility modeling, and enables the explainable compatibility reasoning. Extensive experiments on the real-world dataset demonstrate that our PS-OCM significantly outperforms the state-of-the-art baselines. We have released our source codes and well-trained models to benefit other researchers (https://site2750.wixsite.com/ps-ocm).
Faster Learned Sparse Retrieval with Guided Traversal

Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency.
Hope speech detection in YouTube comments

Recent work on language technology has tried to recognize abusive language such as those containing hate speech and cyberbullying and enhance offensive language identification to moderate social media platforms. Most of these systems depend on machine learning models using a tagged dataset. Such models have been successful in detecting and eradicating negativity. However, an additional study has lately been conducted on the enhancement of free expression through social media. Instead of eliminating ostensibly unpleasant words, we created a multilingual dataset to recognize and encourage positivity in the comments, and we propose a novel custom deep network architecture, which uses a concatenation of embedding from T5-Sentence. We have experimented with multiple machine learning models, including SVM, logistic regression, K-nearest neighbour, decision tree, logistic neighbours, and we propose new CNN based model. Our proposed model outperformed all others with a macro F1-score of 0.75 for English, 0.62 for Tamil, and 0.67 for Malayalam.
Anti-noise performance of the pulse coupled neural network applied in discrimination of neutron and gamma-ray

Abstract In this study, the anti-noise performance of a pulse-coupled neural network (PCNN) was investigated in the neutron and gamma-ray ( n − $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> ) discrimination field. The experiments were conducted in two groups. In the first group, radiation pulse signals were pre-processed using a Fourier filter to reduce the original noise in the signals, whereas in the second group, the original noise was left untouched to simulate an extremely high-noise scenario. For each part, artificial Gaussian noise with different intensity levels was added to the signals prior to the discrimination process. In the aforementioned conditions, the performance of the PCNN was evaluated and compared with five other commonly used methods of n − $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> discrimination: (1) zero crossing, (2) charge comparison, (3) vector projection, (4) falling edge percentage slope, and (5) frequency gradient analysis. The experimental results showed that the PCNN method significantly outperforms other methods with outstanding $$\mathrm{FoM}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>FoM</mml:mi> </mml:math> -value at all noise levels. Furthermore, the fluctuations in $$\mathrm{FoM}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>FoM</mml:mi> </mml:math> -value of PCNN were significantly better than those obtained via other methods at most noise levels and only slightly worse than those obtained via the charge comparison and zero-crossing methods under extreme noise conditions. Additionally, the changing patterns and fluctuations of the $$\mathrm{FoM}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>FoM</mml:mi> </mml:math> -value were evaluated under different noise conditions. Hence, based on the results, the parameter selection strategy of the PCNN was presented. In conclusion, the PCNN method is suitable for use in high-noise application scenarios for n − $$\gamma$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>γ</mml:mi> </mml:math> discrimination because of its stability and remarkable discrimination performance. It does not rely on strict parameter settings and can realize satisfactory performance over a wide parameter range.
Breast Ultrasound Volume Sweep Imaging: A New Horizon in Expanding Imaging Access for Breast Cancer Detection

The majority of people in the world lack basic access to breast diagnostic imaging resulting in delay to diagnosis of breast cancer. In this study, we tested a volume sweep imaging (VSI) ultrasound protocol for evaluation of palpable breast lumps that can be performed by operators after minimal training without prior ultrasound experience as a means to increase accessibility to breast ultrasound.Medical students without prior ultrasound experience were trained for less than 2 hours on the VSI breast ultrasound protocol. Patients presenting with palpable breast lumps for standard of care ultrasound examination were scanned by a trained medical student with the VSI protocol using a Butterfly iQ handheld ultrasound probe. Video clips of the VSI scan imaging were later interpreted by an attending breast imager. Results of VSI scan interpretation were compared to the same-day standard of care ultrasound examination.Medical students scanned 170 palpable lumps with the VSI protocol. There was 97% sensitivity and 100% specificity for a breast mass on VSI corresponding to 97.6% agreement with standard of care (Cohen's κ = 0.95, P < .0001). There was a detection rate of 100% for all cancer presenting as a sonographic mass. High agreement for mass characteristics between VSI and standard of care was observed, including 87% agreement on Breast Imaging-Reporting and Data System assessments (Cohen's κ = 0.82, P < .0001).Breast ultrasound VSI for palpable lumps offers a promising means to increase access to diagnostic imaging in underserved areas. This approach could decrease delay to diagnosis for breast cancer, potentially improving morbidity and mortality.