Reviewerly/exHarmony huggingface dataset:
GRAIN: Granular multi-label encrypted traffic classification using classifier chain

Granular traffic classification categorizes traffic into detailed classes like application names and services. Application names represent parent applications, such as Facebook, while application services are the individual actions within the parent application, such as Facebook-comment. These granular classes are still insufficient to keep pace with modern applications that offer various services. Accordingly, this paper further divides the application service class into inter-application and intra-application services to provide more insights. Inter-application service refers to a similar service between different parent applications, such as Facebook-comment and Youtube-comment, whereas intra-application service differentiates services within the same parent application, such as Facebook-comment and Facebook-post. Most studies focus on classification at the application name and inter-application service levels. In contrast, classification at the intra-application service level receives far less attention due to its complexity despite providing the highest flexibility. Therefore, this paper presents GRAIN, a granular multi-label approach to classify encrypted traffic at all three levels of granular classification: application name, inter-application and intra-application service levels using a classifier chain. GRAIN chains two random forest classifiers to produce a multi-label classification using seven novel statistical features based on packet payload length. The utilized features are independent of the packet payload content, thus unaffected by packet encryption and preserving user privacy. Our performance evaluation showed that GRAIN achieved an average F-measure of 99% at the application name level, 93% at the inter-application service level and 88% at the intra-application service level. To test for robustness, we compared GRAIN against four baseline classifiers and the ISCX VPN-nonVPN public dataset in which GRAIN maintained its comparable performance across all tests.
Geyser

Compared to widely-used superconducting qubits, neutral-atom quantum computing technology promises potentially better scalability and flexible arrangement of qubits to allow higher operation parallelism and more relaxed cooling requirements. The high performance computing (HPC) and architecture community is beginning to design new solutions to take advantage of neutral-atom quantum architectures and overcome its unique challenges.
Technical, environmental and ranking analysis of using solar heating: A case study in South Africa

The process of heating water consumes a lot of energy. In South Africa (SA) up to 40% of household energy consumption is utilized for water heating. In this regard, the use of renewable energies, especially solar, can help reduce the energy crisis in this country. In this study, applying the solar water heaters (SWHs) in home-scale has been explored for the first time, by using climatic information for 21 cities in South Africa. The techno-environmental assessment was performed by TSOL PRO 5.5 on two types of the evacuated tube (ET) and flat plate (FP water heaters). Furthermore, these cities are ranked using GAMS 24.1 and two types of DEA methods. The results indicate the efficiency of evacuated tube SWHs is better than that of flat plate SWHs at all cities and if we use the FP water heater, the average solar fraction is 95.93%, which prevents about 23.5 tons of CO2 emissions annually. These values for ET water heaters are 99.16% and 24.4 tons per year, respectively. For the FP collector, the findings indicate Beaufort West, Mmabatho and Welkom cities are preferable cities in both models, DEA-CCR and DEA-Additive models.
A blockchain-based infection tracing and notification system by non-fungible tokens

SARS-CoV2 pandemic is heavily affecting our lives. Many actions have been undertaken to slow down its expansion and, among the others, contact tracing applications are the less invasive to monitor the spread of the virus. The idea behind contact tracing is to track contacts between people by the exchange of identifiers, not linked to individuals, exploiting the use of Bluetooth Low Energy (BLE) technology to estimate the duration and proximity of contacts. The data collected in this way is used for the sole purpose of notifying a potential contact with an infected person without revealing their identity and location. This paper presents a contact tracing protocol based on blockchain technology that exploits smart contracts for reporting contacts at risk of contagion. The novelty of the proposed solution is the use of Non Fungible Tokens (NFT) to guarantee user privacy through a decentralized approach, equipped with a reliable non-proprietary notification mechanism that allows public access to anonymous infections data.
A semi-supervised learning approach for bladder cancer grading

Recent advances in semi-supervised learning algorithms (SSL) have made great strides in reducing the training dependency on labeled datasets and requiring that only a subset of the data be labeled. The presented work explores a class of semi-supervised learning algorithms that uses consistency regularization and self-ensembling to leverage the unlabeled portion of the dataset. Labeling medical image datasets are time-consuming and prohibitively expensive, requiring hundreds of hours of effort from expert diagnosticians. This research presents an approach for building and training a deep learning model to grade medical images while requiring only a minimal number of labels. Consistency regularization has been used in SSL to great success in datasets of natural images but not for more complex images such as pathology slides where the dataset consists of cell patterns. This research successfully proposes and applies an SSL algorithm based on the VGG-16 neural network, which combines techniques introduced by the Π model and FixMatch algorithms to a cell pattern-based pathology image dataset. The results presented in this research show that using the proposed approach, it is possible to label only 3% of the samples in a dataset, use the remaining 97% of samples as unlabeled data, and achieve a 19% increase over the baseline accuracy. The second contribution of this research shows a ratio of labeled vs. unlabeled images in a dataset beyond which continuing to label the data increases the cost but offers little performance gains.
MB-MaaS: Mobile Blockchain-based Mining-as-a-Service for IIoT environments

In this paper, we propose a mobile blockchain (MB) based mining-as-a-service (MaaS) scheme, MB-MaaS for resource-constrained industrial internet-of-things (IIoT) environments. The scheme addresses the research gaps of fixed static allocation for miners to perform computationally intensive mining tasks through a multi-hop computational offloading (CO) scheme and addresses an auction mechanism for a fair bidding process among the miner nodes. The scheme operates in three phases. In the first phase, a multi-hop CO scheme with a fair incentive policy is formulated for miners. The CO schemes offer guaranteed offloading services to mobile devices from far-edge systems through a chain of neighbor nodes. Then, in the second phase, MaaS is proposed to leverage expensive mining tasks through 5G-enabled pico/femtocells. Integration of 5G allows massive end-to-end device and service connectivity. As IIoT ecosystems have limited memory and compute requirements, MaaS assures that the proposed consensus has a responsive validation and mining time. To make the data exchange in the consensus process lightweight, and allow a large number of sensors to share the data in a lightweight manner, an effective consensus mechanism Lightweight Proof-of-Proximity (LPoP), is proposed that forms group validations instead of single block validation. The data is exchanged through javascript object notation (JSON) format, maintaining a steady transaction rate. MB-MaaS is compared against the existing scheme for parameters bid thresholds and request servicing times, and mining and consensus formation. For example, the request serving time at 12 requests is improved by 56.78%, and a significant improvement of 26.47% is observed for processed blocks; parsing time on average is improved by 7.89%. The comparative analysis suggests that the scheme is more efficient than other competing approaches.
Detection of Liver Cancer through Computed Tomography Images using Deep Convolutional Neural Networks

Liver cancer is the fifth most common type of tumor in men and the ninth most common type of tumor in women. After taking a sample of liver tissue, imaging tests like computed tomography (CT), ultrasound, and magnetic resonance imaging (MRI), can be used to diagnose the liver tumor. In recent studies, accurate detection of liver cancer with minimum computational time and computational complexity is a major issue remained the challenge. This research proposes a framework to segment the cancerous area through CT scan images using entropy thresholding technique. Additionally, it uses two CNN models, U-Net and Google-Net, for the classification of liver cancer. The proposed method employs the 3D-IRCADb01 dataset, which consists of CT slices of liver tumor patients. The U-Net performed better than other networks with 98.5% accuracy, 0.83 DSC, 99.5% recall, and 98.75% F1. Biotechnology uses this method for an early and accurate diagnosis of liver tumors that is likely to save many lives. Proposed method outperformed than existing state-of-art methods and is suitable for clinical applications to assist doctors in diagnosing liver cancer.
A novel model to enhance the data security in cloud environment

Nowadays cloud computing has given a new paradigm of computing. Despite several benefits of cloud computing there is still a big challenge of ensuring confidentiality and integrity for sensitive information on the cloud. Therefore to address these challenges without loss of any sensitive information and privacy, we present a novel and robust model called ‘Enhanced Cloud Security using Hyper Elliptic Curve and Biometric’ (ECSHB). The model ECSHB ensures the preservation of data security, privacy, and authentication of data in a cloud environment. The proposed approach combines biometric and hyperelliptic curve cryptography (HECC) techniques to elevate the security of data accessing and resource preservations in the cloud. ECSHB provides a high level of security using less processing power, which will automatically reduce the overall cost. The efficacy of the ECSHB has been evaluated in the form of recognition rate, biometric similarity score, False Matching Ratio (FMR), and False NonMatching Ratio (FNMR). ECSHB has been validated using security threat model analysis in terms of confidentiality. The measure of collision attack, replay attack and non-repudiation is also considered in this work. The evidence of results is compared with some existing work, and the results obtained exhibit better performance in terms of data security and privacy in the cloud environment.
PPMLAC

Privacy issue is a main concern restricting data sharing and cross-organization collaborations. While Privacy-Preserving Machine Learning techniques such as Multi-Party Computations (MPC), Homomorphic Encryption, and Federated Learning are proposed to solve this problem, no solution exists with both strong security and high performance to run large-scale, complex machine learning models. This paper presents PPMLAC, a novel chipset architecture to accelerate MPC, which combines MPC's strong security and hardware's high performance, eliminates the communication bottleneck from MPC, and achieves several orders of magnitudes speed up over software-based MPC. It is carefully designed to only rely on a minimum set of simple hardware components in the trusted domain, thus is robust against side-channel attacks and malicious adversaries. Our FPGA prototype can run mainstream large-scale ML models like ResNet in near real-time under a practical network environment with non-negligible latency, which is impossible for existing MPC solutions.
Super-resolution of very low-resolution face images with a wavelet integrated, identity preserving, adversarial network

Super-resolution of face images, known as Face Hallucination (FH), has been excessively studied in recent years. Modern FH methods use deep Convolution Neural Networks (CNN) with a pixel-wise MSE loss function to infer high-resolution facial images. The MSE-oriented approaches generate over-smooth results, particularly when dealing with very low-resolution images. Recently, Generative Adversarial Networks (GANs) have successfully been exploited to synthesize perceptually more pleasant images. However, the GAN-based models do not guarantee identity preservation during face super-resolution. To address these challenges, we have proposed a novel Wavelet-integrated, Identity Preserving, Adversarial (WIPA) approach. Specifically, we present Wavelet Prediction blocks attached to a Baseline CNN network to predict wavelet missing details of facial images. The extracted wavelet coefficients are concatenated with original feature maps in different scales to recover fine details. Unlike other wavelet-based FH methods, this algorithm exploits the wavelet-enriched feature maps as complementary information to facilitate the hallucination task. We introduce a wavelet prediction loss to push the network to generate wavelet coefficients. In addition to the wavelet-domain cost function, a combination of perceptual, adversarial, and identity loss functions has been utilized to achieve low-distortion and perceptually high-quality images while maintaining identity. The extensive experiments prove the superiority of the proposed approach over the state-of-the-art methods by achieving PSNR of 25.16 dB for CelebA dataset and verification rate of 86.1% for LFW dataset; both conducted on 8X magnification factor.
Understanding Shoulder Surfer Behavior and Attack Patterns Using Virtual Reality

In this work, we explore attacker behavior during shoulder surfing. As such behavior is often opportunistic and difficult to observe in real world settings, we leverage the capabilities of virtual reality (VR). We recruited 24 participants and observed their behavior in two virtual waiting scenarios: at a bus stop and in an open office space. In both scenarios, participants shoulder surfed private screens displaying different types of content. From the results we derive an understanding of factors influencing shoulder surfing behavior, reveal common attack patterns, and sketch a behavioral shoulder surfing model. Our work suggests directions for future research on shoulder surfing and can serve as a basis for creating novel approaches to mitigate shoulder surfing.
TDDA-Net: A transitive distant domain adaptation network for industrial sample enhancement

The small sample problem is an open challenge in data-driven industrial fault diagnosis, which leads to low accuracy and weak generalization for modeling. Domain adaptation (DA) attempts to transfer samples of the source domain to the target domain for small sample enhancement. However, existing approaches cannot be directly applied to distant domains, due to insufficient exploration of domains with large discrepancies and negative transfer. To tackle the above issues, this paper proposes a transitive distant domain adaptation network (TDDA-Net), in which the sample features are decomposed into three orthogonalized dimensions to reliably express the sample information. Then, distant domain samples are explored and negative transfer is alleviated in different feature dimensions. In particular, distant domain samples are transitively explored to obtain abundant types of samples, wherein useful information from intermediate domains can be employed. Thus, the modeling generalization is improved. Additionally, the marginal and conditional distributions of the samples are adaptively matched to correct distribution drift, such that the negative transfer is alleviated. Thus, the modeling accuracy is improved. Benchmark simulated experiments and real-world application experiments are conducted to evaluate the proposed network. All the results demonstrate that our TDDA-Net performs favorably against the state-of-the-art methods.
Small Object Detection Network Based on Feature Information Enhancement

Due to the small size and weak characteristics of small objects, the performance of existing object detection algorithms for small objects is not ideal. In this paper, we propose a small object detection network based on feature information enhancement to improve the detection effect of small objects. In our method, two key modules, information enhancement module and dense atrous convolution module, are proposed to enhance the expression and discrimination ability of feature information. The detection accuracy of this method on PASCAL VOC, MS COCO, and UCAS-AOD data sets is 81.3%, 34.8%, and 87.0%, respectively. In addition, the detection results of this paper in detecting small objects are slightly (0.2% and 0.1%) higher than the current advanced algorithms (YOLOv4 and DETR, respectively). Moreover, when these two modules are integrated into other algorithms, such as RFBNet, it can also bring considerable improvement.
Lung Cancer Detection Based on Kernel PCA-Convolution Neural Network Feature Extraction and Classification by Fast Deep Belief Neural Network in Disease Management Using Multimedia Data Sources

In lung cancer, tumor histology is a significant predictor of treatment response and prognosis. Although tissue samples for pathologist view are the most pertinent approach for histology classification, current advances in DL for medical image analysis point to the importance of radiologic data in further characterization of disease characteristics as well as risk stratification. Cancer is a complex global health problem that has seen an increase in death rates in recent years. Progress in cancer disease detection based on subset traits has enabled awareness of significant as well as exact disease diagnosis, thanks to the rapid flowering of high-throughput technology as well as numerous ML techniques that have emerged in recent years. As a result, advanced ML approaches that can successfully distinguish lung cancer patients from healthy people are of major importance. This paper proposed lung tumor detection based on histopathological image analysis using deep learning architectures. Here, the input image is taken as a histopathological image, and it has also been processed for removing noise, image resizing, and enhancing the image. Then the image features are extracted using Kernel PCA integrated with a convolutional neural network (KPCA-CNN), in which KPCA has been used in the feature extraction layer of CNN. The classification of extracted features has been put into effect using a Fast Deep Belief Neural Network (FDBNN). Finally, the classified output will give the tumorous cell and nontumorous cell of the lung from the input histopathological image. The experimental analysis has been carried out for various histopathological image datasets, and the obtained parameters are accuracy, precision, recall, and F-measure. Confusion matrix gives the actual class and predicted class of tumor in an input image. From the comparative analysis, the proposed technique obtains enhanced output in detecting the tumor once compared with an existing methodology for the various datasets.
Exploring Manipulating In-VR Audio To Facilitate Verbal Interactions Between VR Users And Bystanders

Despite recent work investigating how VR users can be made aware of bystanders, few have explored how bystander-VR user interactions may be facilitated by, for example, increasing the user's auditory awareness so they can better converse with bystanders. Through a lab study (N=15) we investigated 4 approaches of manipulating in-VR audio to facilitate verbal interactions between a VR user and bystander: (1) dynamically reducing application volume, (2) removing background audio, (3) removing sound effects and (4) removing all audio. Our results show audio manipulations can be used to significantly improve a VR user's auditory awareness at the cost of reducing sense of presence in VR. They also show most preferred increased awareness be balanced with decreased presence in VR, however, they also identify a subset of participants who prioritised increasing awareness no matter the cost to presence.
Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors

Convolutional Neural Networks (CNNs) play a crucial role in many image recognition and classification tasks, recommender systems, brain-computer interfaces, etc. As a consequence, there is a notable interest in developing high performance realizations of the convolution operators, which concentrate a significant portion of the computational cost of this type of neural networks. In a previous work, we introduced a portable, high performance convolution algorithm, based on the BLIS realization of matrix multiplication, which eliminates most of the runtime and memory overheads that impair the performance of the convolution operators appearing in the forward training pass, when performed via explicit im2col transform. In this paper, we extend our ideas to the full training process of CNNs on multicore processors, proposing new high performance strategies to tackle the convolution operators that are present in the more complex backward pass of the training process, while maintaining the portability of the realizations. In addition, we conduct a full integration of these algorithms into a framework for distributed training of CNNs on clusters of computers, providing a complete experimental evaluation of the actual benefits in terms of both performance and memory consumption. Compared with baseline implementation, the use of the new convolution operators using pre-allocated memory can accelerate the training by a factor of about 6%–25%, provided there is sufficient memory available. In comparison, the operator variants that do not rely on persistent memory can save up to 70% of memory.