Reviewerly/exHarmony huggingface dataset:
Traffic generation for benchmarking data centre networks

Benchmarking is commonly used in research fields, such as computer architecture design and machine learning, as a powerful paradigm for rigorously assessing, comparing, and developing novel technologies. However, the data centre networking community lacks a standard open-access benchmark. This is curtailing the community's understanding of existing systems and hindering the ability with which novel technologies can be developed, compared, and tested. We present TrafPy; an open-access framework for generating both realistic and custom data centre network traffic traces. TrafPy is compatible with any simulation, emulation, or experimentation environment, and can be used for standardised benchmarking and for investigating the properties and limitations of network systems such as schedulers, switches, routers, and resource managers. To demonstrate the efficacy of TrafPy, we use it to conduct a thorough investigation into the sensitivity of 4 canonical scheduling algorithms (shortest remaining processing time, fair share, first fit, and random) to varying traffic trace characteristics. We show how the fundamental scheduler performance insights revealed by these tests translate to 4 realistic data centre network types; University, Private Enterprise, Commercial Cloud, and Social Media Cloud. We then draw conclusions as to which types of scheduling policies are most suited to which types of network load conditions and traffic characteristics, leading to the possibility of application-informed decision making at the design stage and new dynamically adaptable scheduling policies. TrafPy is open-sourced via GitHub and all data associated with this manuscript via RDR.
An improved multi-modal joint segmentation and registration model based on Bhattacharyya distance measure

Joint image segmentation and registration of multi-modality images is a crucial step in the field of image prepossessing. The sensitivity of joint segmentation and registration models to noise is a significant challenge. During the registration process of multi-modal images, the similarity measure plays a vital role in measuring the results as a standard. Accordingly, an improved joint model for registering and segmenting multi-modality images is proposed, by utilising the Bhattacharyya distance measure to achieve improved noise robustness of the proposed model as compared to the existing model using the mutual information metric. The proposed model is applied to various medical and synthetic noisy images of multiple modalities. Moreover, the dataset images used in this study have been obtained from well-known, freely available BRATS 2015 and CHAOS datasets, where the proposed model produces satisfactory results as compared to the existing model. Experimental results show that the proposed model outperforms the existing model in terms of the Bhattacharyya distance measure in noisy images. Statistical analysis and comparison are performed through the relative reduction of the new distance measure, Dice similarity coefficient, Jaccard similarity coefficient and Hausdorff distance.
Teaching presence in students’ WhatsApp groups: Affordances for language learning

With the recent COVID-19 pandemic and disruption of campus-based education, the use of mobile social networking applications to supplement formal education has attracted a great deal of attention. Teachers do have opportunities to join students’ online groups to share, clarify, and exchange housekeeping information and course-related content with them. Teachers can, in particular, provide English as a foreign language (EFL) students with more sources of linguistic input, interaction, and feedback. Research investigating this potential, however, is still scarce in such contexts. The current study explores the likely affordances of teaching presence in students’ WhatsApp groups for designing, facilitating, and guiding cognitive and social processes conducive to their language learning. A mixed-method design was employed to collect both quantitative and qualitative data and information from English-major undergraduates ( N = 111) and faculty teachers ( N = 8) who joined the same WhatsApp groups for one academic semester at a major university in Oman. Descriptive and thematic analyses of data from a survey with both closed-ended and open-ended questions and semi-structured interviews indicate that the shared WhatsApp groups functioned as small close-knit communities where students were able to constantly access teachers for their assistance, feedback, and clarification of content. Despite these merits, however, the participating faculty believed that the presence of teachers in WhatsApp groups might have consequences for students’ tolerance of ambiguity, scaffolding, and autonomous language learning. The paper concludes by discussing several pedagogical implications and directions for future research.
Emergency remote English language teaching and learning: Voices of primary school students and teachers in Kazakhstan

Abstract This qualitative study documents the experiences of 38 Grade 5 students and their six female teachers from four state‐maintained schools in Kazakhstan teaching/learning English during emergency remote teaching and learning (ERT&amp;L). It was guided by Kearney et al .’s (2012) framework that provides three dimensions that influence individuals’ teaching and learning experience when using technological devices: personalisation , collaboration and authenticity . The data collected through online individual interviews and students’ drawings suggest that the participants asserted their agentive reflexivity and critical awareness of the advantages and drawbacks of their ERT&amp;L experiences. Several teachers indicated that although ERT&amp;L helped them make financial and professional gains, they had difficulty assessing their students’ progress online, and work overload had negative impacts on their well‐being and quality of life. Many students criticised some English teaching practices during COVID‐19, including the allocation of more homework, and lack of co‐operative activities and individual attention. The study has pedagogical implications and provides further evidence of the need for stakeholders to take the well‐being of teachers into account, promote collaboration between different social actors and provide teachers with proper training in the use of online platforms. It also stresses the importance of using participatory research methods involving children to capture their thoughts and interpretation of their emergency remote learning experiences.
Changes of SOC Content in China’s Shendong Coal Mining Area during 1990–2020 Investigated Using Remote Sensing Techniques

Coal mining, an important human activity, disturbs soil organic carbon (SOC) accumulation and decomposition, eventually affecting terrestrial carbon cycling and the sustainability of human society. However, changes of SOC content and their relation with influential factors in coal mining areas remained unclear. In the study, predictive models of SOC content were developed based on field sampling and Landsat images for different land-use types (grassland, forest, farmland, and bare land) of the largest coal mining area in China (i.e., Shendong). The established models were employed to estimate SOC content across the Shendong mining area during 1990–2020, followed by an investigation into the impacts of climate change and human disturbance on SOC content by a Geo-detector. Results showed that the models produced satisfactory results (R2 &gt; 0.69, p &lt; 0.05), demonstrating that SOC content over a large coal mining area can be effectively assessed using remote sensing techniques. Results revealed that average SOC content in the study area rose from 5.67 gC·kg−1 in 1990 to 9.23 gC·kg−1 in 2010 and then declined to 5.31 gC·Kg−1 in 2020. This could be attributed to the interaction between the disturbance of soil caused by coal mining and the improvement of eco-environment by land reclamation. Spatially, the SOC content of farmland was the highest, followed by grassland, and that of bare land was the lowest. SOC accumulation was inhibited by coal mining activities, with the effect of high-intensity mining being lower than that of moderate- and low-intensity mining activities. Land use was found to be the strongest individual influencing factor for SOC content changes, while the interaction between vegetation coverage and precipitation exerted the most significant influence on the variability of SOC content. Furthermore, the influence of mining intensity combined with precipitation was 10 times higher than that of mining intensity alone.
Exploring the Role of Grammar and Word Choice in Bias Toward African American English (AAE) in Hate Speech Classification

Language usage on social media varies widely even within the context of American English. Despite this, the majority of natural language processing systems are trained only on “Standard American English,” or SAE, the construction of English most prominent among white Americans. For hate speech classification, prior work has shown that African American English (AAE) is more likely to be misclassified as hate speech. This has harmful implications for Black social media users as it reinforces and exacerbates existing notions of anti-Black racism. While past work has highlighted the relationship between AAE and hate speech classification, no work has explored the linguistic characteristics of AAE that lead to misclassification. Our work uses Twitter datasets for AAE dialect and hate speech classifiers to explore the fine-grained relationship between specific characteristics of AAE such as word choice and grammatical features and hate speech predictions. We further investigate these biases by removing profanity and examining the influence of four aspects of AAE grammar that are distinct from SAE. Results show that removing profanity accounts for a roughly 20 to 30% reduction in the percentage of samples classified as ’hate’ ’abusive’ or ’offensive,’ and that similar classification patterns are observed regardless of grammar categories.
Optimization of PBFT Algorithm Based on QoS-Aware Trust Service Evaluation

In service-transaction scenarios, blockchain technology is widely used as an effective tool for establishing trust between service providers and consumers. The consensus algorithm is the core technology of blockchain. However, existing consensus algorithms, such as the practical Byzantine fault tolerance (PBFT) algorithm, still suffer from high resource consumption and latency. To solve this problem, in this study, we propose an improved PBFT blockchain consensus algorithm based on quality of service (QoS)-aware trust service evaluation for secure and efficient service transactions. The proposed algorithm, called the QoS-aware trust practical Byzantine fault tolerance (QTPBFT) algorithm, efficiently achieves consensus, significantly reduces resource consumption, and enhances consensus efficiency. QTPBFT incorporates a QoS-aware trust service global evaluation mechanism that implements service reliability ranking by conducting a dynamic evaluation according to the real-time performance of the services. To reduce the traffic of the blockchain, it uses a mechanism that selects nodes with higher values to form a consensus group that votes for consensus according to the global evaluation result of the trust service. A practical protocol is also constructed for the proposed algorithm. The results of extensive simulations and comparison with other schemes verify the efficacy and efficiency of the proposed scheme.
Automatic prostate and peri-prostatic fat segmentation based on pyramid mechanism fusion network for T2-weighted MRI

Automatic and accurate segmentation of prostate and peri-prostatic fat in male pelvic MRI images is a critical step in the diagnosis and prognosis of prostate cancer. The boundary of prostate tissue is not clear, which makes the task of automatic segmentation very challenging. The main issues, especially for the peri-prostatic fat, which is being offered for the first time, are hazy boundaries and a large form variation.We propose a pyramid mechanism fusion network (PMF-Net) to learn global features and more comprehensive context information. In the proposed PMF-Net, we devised two pyramid techniques in particular. A pyramid mechanism module made of dilated convolutions of varying rates is inserted before each down sample of the fundamental network architecture encoder. The module is intended to address the issue of information loss during the feature coding process, particularly in the case of segmentation object boundary information. In the transition stage from encoder to decoder, pyramid fusion module is designed to extract global features. The features of the decoder not only integrate the features of the previous stage after up sampling and the output features of pyramid mechanism, but also include the features of skipping connection transmission under the same scale of the encoder.The segmentation results of prostate and peri-prostatic fat on numerous diverse male pelvic MRI datasets show that our proposed PMF-Net has higher performance than existing methods. The average surface distance (ASD) and Dice similarity coefficient (DSC) of prostate segmentation results reached 10.06 and 90.21%, respectively. The ASD and DSC of the peri-prostatic fat segmentation results reached 50.96 and 82.41%.The results of our segmentation are substantially connected and consistent with those of expert manual segmentation. Furthermore, peri-prostatic fat segmentation is a new issue, and good automatic segmentation has substantial therapeutic implications.
Towards an Efficient Defense against Deep Learning based Website Fingerprinting

Website fingerprinting (WF) attacks allow an attacker to eavesdrop on the encrypted network traffic between a victim and an anonymous communication system so as to infer the real destination websites visited by a victim. Recently, the deep learning (DL) based WF attacks are proposed to extract high level features by DL algorithms to achieve better performance than that of the traditional WF attacks and defeat the existing defense techniques. To mitigate this issue, we propose a-genetic-programming-based variant cover traffic search technique to generate defense strategies for effectively injecting dummy Tor cells into the raw Tor traffic. We randomly perform mutation operations on labeled original traffic traces by injecting dummy Tor cells into the traces to derive variant cover traffic. A high level feature distance based fitness function is designed to improve the mutation rate to discover successful variant traffic traces that can fool the DL-based WF classifiers. Then the dummy Tor cell injection patterns in the successful variant traces are extracted as defense strategies that can be applied to the Tor traffic. Extensive experiments demonstrate that we can introduce 8.1% of bandwidth overhead to significantly decrease the accuracy rate below 0.4% in the realistic open-world setting.
Protect Privacy from Gradient Leakage Attack in Federated Learning

Federated Learning (FL) is susceptible to gradient leakage attacks, as recent studies show the feasibility of obtaining private training data on clients from publicly shared gradients. Existing work solves this problem by incorporating a series of privacy protection mechanisms, such as homomorphic encryption and local differential privacy to prevent data leakage. However, these solutions either incur significant communication and computation costs, or significant training accuracy loss. In this paper, we show that the sensitivity of gradient changes w.r.t. training data is an essential measure of information leakage risk. Based on this observation, we present a novel defense, whose intuition is perturbing gradients to match information leakage risk such that the defense overhead is lightweight while privacy protection is adequate. Our another key observation is that global correlations of gradients could compensate for this perturbation. Based on such compensation, training can achieve guaranteed accuracy. We conduct experiments on MNIST, Fashion-MNIST and CIFAR-10 for defending against two gradient leakage attacks. Without sacrificing accuracy, the results demonstrate that our lightweight defense can decrease the PSNR and SSIM between the reconstructed images and raw images by up to more than 60% for both two attacks, compared with baseline defensive methods.
A blockchain-based Privacy-Preserving Framework for Cross-Social Network Photo Sharing

Posting daily photos on online Social Network Platforms (SNPs) has become a trend. As a kind of sensitive privacy, social photos are often protected carefully by SNPs with various security mechanisms. However, these mechanisms can not stop users from losing control of their photos when someone forwards these photos to another platform. In this paper, we propose a blockchain-based privacy-preserving framework that provides powerful dissemination control for online photos in cross-social network sharing. In contrast to existing security mechanisms that run separately in centralized servers, which hardly create trust between each other, our framework achieves consistent consensus on photo dissemination control across independent and disparate platforms through carefully designed smart contract-based protocols. Considering the numerous participants in real photo dissemination, we present a novel concept of multi-owner dissemination tree to be compatible with all privacy preferences of subsequent forwarders in cross-SNPs photo sharing. This paper describes a prototype implementation on hyperledger Fabric 2.0 with demonstrating its preliminary performance by a real-world dataset and identifies the remaining challenges.
MUSTER: Subverting User Selection in MU-MIMO Networks

WiFi 5/6 relies on a key feature, Multi-User Multiple-In-Multiple-Out (MU-MIMO), to offer high-volume network throughput and spectrum efficiency. MU-MIMO uses a user selection algorithm, based on each user's channel state information (CSI), to schedule transmission opportunities for a group of users to maximize the service quality and efficiency. In this paper, we discover that such algorithm creates a subtle attack surface for attackers to subvert user selection in MU-MIMO, causing severe disruptions in today's wireless networks. We develop a system, named MU-MIMO user selection strategy inference and subversion (MUSTER), to systematically study the attack strategies and further to seek efficient mitigation. MUSTER is designed to include two major modules: (i) strategy inference, which leverages a new neural group-learning strategy named MC-grouping via combining Recurrent Neural Network (RNN) and Monte Carlo Tree Search (MCTS) to reverseengineer a user selection algorithm, and (ii) user selection subversion, which proactively fabricates CSI to manipulate user selection results for disruption. Experimental evaluation shows that MUSTER achieves a high accuracy rate around 98.6% in user selection prediction and effectively launches the attacks to disrupt the network performance. Finally, we create a Reciprocal Consistency Checking technique to defend against the proposed attacks to secure MU-MIMO user selection.
Quality-Aware Incentive Mechanism Design Based on Matching Game for Hierarchical Federated Learning

To protect user privacy and combined with mobile edge computing, hierarchical federated learning (HFL) is proposed. In HFL, we investigated the aggregated model quality maximization problem. Since the global model quality is influenced by the local model quality, we transformed the aggregated model quality maximization into the sum of local model quality maximization. And we proposed the model quality maximization mechanism MaxQ based on matching game to select high quality mobile devices. In MaxQ, the allocation of mobile devices to each edge server is realized so that the sum of the local model quality is maximized. And we proved that MaxQ has a $\frac{1}{2}$-approximation ratio. Finally, through a large number of simulation experiments, compared with FAIR and EHFL, the model quality of MaxQ is improved by 10.8% and 12.2%, respectively.
Thwarting Unauthorized Voice Eavesdropping via Touch Sensing in Mobile Systems

Enormous mobile applications (apps) now support voice functionality for convenient user-device interaction. However, these voice-enabled apps may spitefully invoke microphone to realize voice eavesdropping with arousing security risks and privacy concerns. To explore the issue of voice eavesdropping, in this work, we first design eavesdropping apps through native development and injection development to conduct eavesdropping attacks on a series of smart devices. The results demonstrate that eavesdropping could be carried out freely without any hint. To thwart voice eavesdropping, we propose a valid eavesdropping detection (EarDet) scheme based on the discovery that the activation of voice function in most apps requires authorization from the user by touching a specific voice icon. In the scheme, we construct a request-response time model using the Unix time stamps of touching the voice icon and microphone invoked. Through numerical analysis and hypothesis testing to effectively verify the pattern of the app’s normal access under user authorization to the microphone, we could detect eavesdropping attacks by sensing whether there is a touch operation. Finally, we apply the scheme to different smart devices and test several apps. The experimental results show that the proposed EarDet scheme can achieve a high detection accuracy.
Interpretability Evaluation of Botnet Detection Model based on Graph Neural Network

Due to the conspicuous ability to capture topology characteristics, graph neural networks (GNN) have been widely used in botnet detection and proven efficient. However, the blackbox nature of GNN models creates an obstacle for users to trust these classified instruments. In addition to high accuracy, stakeholders also hope that these models are consistent with human cognition. To cope with this problem, we propose a method to evaluate the trustworthiness of GNN-based botnet detection models, called BD-GNNExplainer. Concretely, BD-GNNExplainer extracts the data that contribute the most to GNN’s decision by reducing the loss between the classification results generated by the selected subgraph as the GNN model’s input and the results generated by the entire graph as input. We calculate the relevance between the model-relied data and the informative data to quantify a score expressing interpretability. For different-structure GNN models, these scores will tell us which one is more trustworthy and ultimately become an essential basis for model optimization. To the best of our knowledge, our work is the first time to discuss the interpretability of botnet detection systems and will provide a guideline for making the botnet detection methodology more understandable.