Reviewerly/exHarmony huggingface dataset:
Training data debugging for the fairness of machine learning software

With the widespread application of machine learning (ML) software, especially in high-risk tasks, the concern about their unfairness has been raised towards both developers and users of ML software. The unfairness of ML software indicates the software behavior affected by the sensitive features (e.g., sex), which leads to biased and illegal decisions and has become a worthy problem for the whole software engineering community.
A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.
AutoGSR

Session-based recommendation aims to predict next click action (e.g., item) of anonymous users based on a fixed number of previous actions. Recently, Graph Neural Networks (GNNs) have shown superior performance in various applications. Inspired by the success of GNNs, tremendous endeavors have been devoted to introduce GNNs into session-based recommendation and have achieved significant results. Nevertheless, due to the highly diverse types of potential information in sessions, existing GNNs-based methods perform differently on different session datasets, leading to the need for efficient design of neural networks adapted to various session recommendation scenarios. To address this problem, we propose Automated neural architecture search for Graph-based Session Recommendation, namely AutoGSR, a framework that provides a practical and general solution to automatically find the optimal GNNs-based session recommendation model. In AutoGSR, we propose two novel GNN operations to build an expressive and compact search space. Building upon the search space, we employ a differentiable search algorithm to search for the optimal graph neural architecture. Furthermore, to consider all types of session information together, we propose to learn the item meta knowledge, which acts as a priori knowledge for guiding the optimization of final session representations. Comprehensive experiments on three real-world datasets demonstrate that AutoGSR is able to find effective neural architectures and achieve state-of-the-art results. To the best of our knowledge, we are the first to study the neural architecture search for the session-based recommendation.
V-SZZ

Vulnerabilities publicly disclosed in the National Vulnerability Database (NVD) are assigned with CVE (Common Vulnerabilities and Exposures) IDs and associated with specific software versions. Many organizations, including IT companies and government, heavily rely on the disclosed vulnerabilities in NVD to mitigate their security risks. Once a software is claimed as vulnerable by NVD, these organizations would examine the presence of the vulnerable versions of the software and assess the impact on themselves. However, the version information about vulnerable software in NVD is not always reliable. Nguyen et al. find that the version information of many CVE vulnerabilities is spurious and propose an approach based on the original SZZ algorithm (i.e., an approach to identify bug-introducing commits) to assess the software versions affected by CVE vulnerabilities.
Knowledge engineering–based DApp using blockchain technology for protract medical certificates privacy

In the Industry 4.0 era, an inherited featured technology, blockchain, plays a vital role in knowledge engineering applications. Blockchain provides privacy to sensitive data as an intelligent agent, so its adoption rate increases in all the advanced domains. Especially in the health care department, blockchain technology usage helps avoid attacks like the Wannacry ransomware attack during 2017. Therefore, this paper described a decentralised application (DApp) expert system using public blockchain to create and maintain official health documents, especially medical certificates. Current existing systems, either paper-based or database or clouds to save the medical certificates, have more scope to do attacks. Hence, proposed a blockchain-based DApp that acts as an interface between intelligent agents, blockchains, and system related to the medical certificates. The main strength of this paper is implementation results, which are not among the maximum literary works currently available. The associate cost for conducting distributed application operations on the blockchain in terms of Gas comprehensively presented here. Furthermore, it consists of comparing the system's non-functional functions by considering blockchain and non-blockchain environments. Also, presented the simulation results with the performance results compared with the existed systems.
Experiments on Generalizability of User-Oriented Fairness in Recommender Systems

Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. A fairness-aware recommender system aims to treat different user groups similarly. Relevant work on user-oriented fairness highlights the discriminant behavior of fairness-unaware recommendation algorithms towards a certain user group, defined based on users' activity level. Typical solutions include proposing a user-centered fairness re-ranking framework applied on top of a base ranking model to mitigate its unfair behavior towards a certain user group i.e., disadvantaged group. In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method. Moreover, we evaluate the final recommendations provided by the re-ranking framework from both user- (e.g., NDCG, user-fairness) and item-side (e.g., novelty, item-fairness) metrics. We discover interesting trends and trade-offs between the model's performance in terms of different evaluation metrics. For instance, we see that the definition of the advantaged/disadvantaged user groups plays a crucial role in the effectiveness of the fairness algorithm and how it improves the performance of specific base ranking models. Finally, we highlight some important open challenges and future directions in this field. We release the data, evaluation pipeline, and the trained models publicly on https://github.com/rahmanidashti/FairRecSys.
A Flexible Framework for Offline Effectiveness Metrics

The use of offline effectiveness metrics is one of the cornerstones of evaluation in information retrieval. Static resources that include test collections and sets of topics, the corresponding relevance judgments connecting them, and metrics that map document rankings from a retrieval system to numeric scores have been used for multiple decades as an important way of comparing systems. The basis behind this experimental structure is that the metric score for a system can serve as a surrogate measurement for user satisfaction.
Beyond confusion matrix: learning from multiple annotators with awareness of instance features

Learning from multiple annotators aims to induce a high-quality classifier from training instances, where each of them is associated with a set of observed labels provided by multiple annotators under the impact of their varying abilities and own biases. When modeling the probability transition process from latent true labels to observed labels, most existing methods adopt class-level confusion matrices of annotators which assume that observed labels do not depend on the instance features and are just determined by the true labels. However, in practice the labeling process of annotators is impacted not only by the correlation between classes but also by the content of instances. Thus using only class-level confusion matrices to characterize the probability transition process may limit the performance that the classifier can achieve. In this work, we propose the noise transition matrix, that incorporates the impact of instance features on annotators' performance based on confusion matrices. Furthermore, we propose a simple and effective learning framework, which consists of a classifier module and a noise transition matrix module in a unified neural network architecture. Experimental results on synthetic and real datasets demonstrate the noise transition matrix is better than the confusion matrix for modeling multiple annotators and the superiority of our method in comparison with state-of-the-art methods.
An improved density peak clustering algorithm guided by pseudo labels

Density peak clustering algorithms and their variants have achieved promising results in many fields over the last few years. However, most of these algorithms parameters requiring to be fine-tuned by users. When facing real-world data without ground-truths, it is often challenging and time-consuming to identify better parameter values for parametric clustering algorithms. Considering this, we propose a density peak clustering algorithm guided by pseudo labels (PLDPC), in which the manually pre-specified parameters are avoided through applying the mutual information criterion. Specifically, we first design a novel pseudo-label generation method based on the theory of co-occurrence. Then, we use the maximizing mutual information method to obtain better clustering results. To evaluate the effectiveness of the proposed PLDPC algorithm, we conduct extensive experiments on 23 datasets, including six synthetic and seventeen real-world datasets. The experimental results show that PLDPC outperforms three classical algorithms (i.e., K-means, DPC, and DBSCAN) and eight state-of-the-art (SOTA) clustering algorithms in most cases.
VGG16-random fourier hybrid model for masked face recognition

With the recent COVID-19 pandemic, wearing masks has become a necessity in our daily lives. People are encouraged to wear masks to protect themselves from the outside world and thus from infection with COVID-19. The presence of masks raised serious concerns about the accuracy of existing facial recognition systems since most of the facial features are obscured by the mask. To address these challenges, a new method for masked face recognition is proposed that combines a cropping-based approach (upper half of the face) with an improved VGG-16 architecture. The finest features from the un-occluded facial region are extracted using a transfer learned VGG-16 model (Forehead and eyes). The optimal cropping ratio is investigated to give an enhanced feature representation for recognition. To avoid the overhead of bias, the obtained feature vector is mapped into a lower-dimensional feature representation using a Random Fourier Feature extraction module. Comprehensive experiments on the Georgia Tech Face Dataset, Head Pose Image Dataset, and Face Dataset by Robotics Lab show that the proposed approach outperforms other state-of-the-art approaches for masked face recognition.
Agile work practices: measurement and mechanisms

Organizations increasingly follow agile management frameworks (e.g., Scrum), to implement practices that aim to enable continuous change. Currently, it is unclear how agile work practices (AWPs) are best conceptualized and measured. The present study draws from the taskwork-teamwork distinction to develop a new theoretical framework and measurement instrument of AWPs. We outline potential mechanisms of AWPs in terms of (a) temporality, (b) managerial control, (c) team processes, and (d) work design. Based on this framework, we validate measures of agile practices with data collected from 269 different teams, including multisource and multiwave data (n = 1664 observations). We first establish the factorial validity, internal consistency, test-retest reliability, and measurement invariance of the instrument. Subsequently, we show that AWPs diverge from centralized bureaucracy and converge with measures of emergent team planning, autonomy, and feedback. The pattern of relationships with variables in the nomological network supports the taskwork-teamwork model. Results of multilevel regression analyses indicate that the use of AWPs is associated with favorable team planning behaviors and enriched work design experiences. By disentangling the AWP concept from software development and popular management frameworks, this study broadens the scope of research on agility.
Machine Learning for Detecting Security Attacks on Blockchain using Software Defined Networking

As there are many portable devices such as mobile devices and Internet of Things (IoT) devices, and these devices are not secured due to mobility and other factors, attacks are the biggest threat on these devices, and security is the biggest concern. Different researchers used to research security and attacks, but they faced some latency, cost, and system failures. Over the years, blockchain has been used in various application areas to improve reliability, information protection, and frameworks. Even the blockchain structure is robust; even after this; blockchain is not resistant to cyber-attacks. As we have an idea about Ethereum is that it is a blockchain-based software-based platform with intelligent functionalities. One of the successful attacks on Ethereum has uncovered security propensity. As there are so many benefits as Software Defined Networking (SDN) is concerned, in Software Defined Networking (SDN) data packet routes via a single firewall which makes IDS more secure. This paper will detect security attacks on the blockchain using a machine learning approach and Software Defined Networks. This paper will detect security attacks on the blockchain using a machine learning approach and Software Defined Networks. This paper discusses an anomaly-based recognition method centered on an encoder-decoder prototype, and it is skilled via collective facts obtained by observing blockchain actions.
A medical question answering system using large language models and knowledge graphs

Question answering systems have become prominent in all areas, while in the medical domain it has been challenging because of the abundant domain knowledge. Retrieval based approach has become promising as large pretrained language models come forth. This study focuses on building a retrieval-based medical question answering system, tackling the challenge with large language models and knowledge extensions via graphs. We first retrieve an extensive but coarse set of answers via Elasticsearch efficiently. Then, we utilize semantic matching with pretrained language models to achieve a fine-grained ranking enhanced with named entity recognition and knowledge graphs to exploit the relation of the entities in question and answer. A new architecture based on siamese structures for answer selection is proposed. To evaluate the approach, we train and test the model on two Chinese data sets, NLPCC2017 and cMedQA. We also conduct experiments on two English data sets, TREC-QA and WikiQA. Our model achieves consistent improvement as compared to strong baselines on all data sets. Qualification studies with cMedQA and our in-house data set show that our system gains highly competitive performance. The proposed medical question answering system outperforms baseline models and systems in quantification and qualification evaluations.
Multi-instance attention network for few-shot learning

The attention mechanism is usually equipped with a few-shot learning framework and plays a key role in extracting the semantic object(s). However, most attention networks in existing few-shot learning algorithms often work on the channel and/or pixel dimension, leading to the size of attention maps being large. Due to lack of training examples, these attention networks are prone to over-fitting, and may fail to find the semantic target(s). In this paper, we split the original image into patches, extending a new dimension in image data, namely, the patch dimension. On the one hand, the number of patch dimensions is usually much smaller than the traditional three dimensions, thus greatly reducing the number of attention module parameters. On the other hand, the patch dimensional attention mechanism can benefit from multi-instance learning and achieve a good compromise between global and local features. Four comparison experiments on four typical real-world data sets (miniImageNet, tieredImageNet, Fewshot-CIFAR100, Caltech-UCSD Birds-200–2011) have demonstrated that our proposed algorithm achieves consistent improvement over 6 baseline models (Matching Networks, Relation Networks, Prototypical Networks, MAML, Baseline++, Meta Baseline) and 11 state-of-the-art models (DC, TapNet, SNAIL, TADAM, MetaOptNet, CAN, CTM, DCEM, AFHN, LEO, AWGIM). Our code is available at: ( https://github.com/rumorgin/MIAN).
Translution-SNet: A Semisupervised Hyperspectral Image Stripe Noise Removal Based on Transformer and CNN

Hyperspectral remote sensing images (HSIs) have been applied in urban planning, environmental monitoring, and other fields. However, they are susceptible to noise interference, such as Gaussian noise, stripe, and mixed noises, from various factors in the imaging process, which greatly limits their applications. Although previous efforts to improve HSI quality have achieved remarkable results, there are still many challenges to be solved. To avoid the poor generalization ability and improve the stripe removal performance of the network in real scenarios. In this paper, we proposed a novel deep learning model (Translution-SNet) for HSI stripe noise removal based on a semi-supervised training strategy that applies a convolution and transformer for feature extraction. Moreover, we used an unbiased estimation method to calculate the loss function of the unsupervised part from noisy data without a clean image. The semi-supervised method improved the ability of Translution-SNet to deal with various complex stripe noises during stripe removal and strengthened its robustness and generalization ability. Our experimental results showed that Translution-SNet could robustly handle stripe noise of images with different loads and achieve satisfactory results, proving its feasibility and effectiveness. In addition, Translution-SNet showed good generalization ability.
Blockchain-based IoT security authentication system

IoT devices have the characteristics of limited performance and mobility, which makes it difficult for traditional centralized security authentication methods to support security authentication in the current IoT environment. In order to cope with these challenges, this paper proposes a security authentication system named iot-chain in the Internet of Things, which provides attribute-based security authentication based on the Hyperledger Fabric blockchain framework. The system contains three kinds of chain codes, namely access code, device code, and policy code. The access code is the main program that implements the user safety authentication method. The device code provides a query method for the URL of the resource data generated by the storage device, and the policy code provides the access control strategy for the administrator user. Combining access control and blockchain technology, iot-chain provides dynamic security authentication management in the Internet of Things. Experimental results show that iot-chain can maintain high throughput and effectively reach consensus in a distributed system.
Multiple Sclerosis Lesions Segmentation Using Attention-Based CNNs in FLAIR Images

Objective: Multiple Sclerosis (MS) is an autoimmune and demyelinating disease that leads to lesions in the central nervous system.This disease can be tracked and diagnosed using Magnetic Resonance Imaging (MRI).A multitude of multimodality automatic biomedical approaches are used to segment lesions that are not beneficial for patients in terms of cost, time, and usability.The authors of the present paper propose a method employing just one modality (FLAIR image) to segment MS lesions accurately.Methods: A patch-based Convolutional Neural Network (CNN) is designed, inspired by 3D-ResNet and spatial-channel attention module, to segment MS lesions.The proposed method consists of three stages: (1) the Contrast-Limited Adaptive Histogram Equalization (CLAHE) is applied to the original images and concatenated to the extracted edges to create 4D images;(2) the patches of size 80 × 80 × 80 × 2 are randomly selected from the 4D images; and (3) the extracted patches are passed into an attention-based CNN which is used to segment the lesions.Finally, the proposed method was compared to previous studies of the same dataset. Results:The current study evaluates the model with a test set of ISIB challenge data.Experimental results illustrate that the proposed approach significantly surpasses existing methods of Dice similarity and Absolute Volume Difference while the proposed method uses just one modality (FLAIR) to segment the lesions. Conclusions:The authors have introduced an automated approach to segment the lesions, which is based on, at most, two modalities as an input.The proposed architecture comprises convolution, deconvolution, and an SCA-VoxRes module as an attention module.The results show, that the proposed method outperforms well compared to other methods.