Reviewerly/exHarmony huggingface dataset:
Breast Cancer Detection and Classification: A Comparative Analysis Using Machine Learning Algorithms

The risk of fatality from breast cancer is increasing exponentially as the population rises. It is the most typical kind of cancer and the major cause of death in women throughout the world. Early detection and treatment of breast cancer could significantly improve the prognosis and survival rate. It is an important phase in rehabilitation and medication since it can help patients receive prompt medical services. An automated disease detection technique that employs machine learning (ML) and deep learning (DL) techniques assist medical professionals in the diagnosis of diseases and provide a reliable, efficient, and faster response thereby minimizing the chance of death. This research aims to perform a comparison among ML and DL methods for breast cancer detection and diagnosis. The five most popular supervised ML techniques named support vector machine (SVM), decision tree (DT), logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), and a DL technique were used for classification using cross-validation technique. The Breast Cancer Wisconsin (diagnostic) dataset has been used as a training set to evaluate and compare each algorithm's effectiveness and efficiency through classification accuracy, recall, specificity, precision, false-negative rate (FNR), false-positive rate (FPR), F1-score, and Matthews correlation coefficient (MCC). Experimental results show that random forest (tuned) outperformed all the other models with accuracy and F1-score of 96.66% and 0.963, respectively.
Assessment of students’ digital competences in primary school: a systematic review

Although there is a growing body of literature that recognizes the importance of being digitally competent today, there have been few empirical investigations into the assessment of primary school students’ digital competences. This study presents a systematic review of the empirical research on the assessment of primary school students’ digital competences. In total, 14 studies were selected and reviewed. The purpose of this review is twofold. First, the areas of digital competence that were measured by the assessment instruments were labeled according to the European Digital Competence Framework. Results showed that most studies evaluated digital competences as ‘information and data literacy’, ‘communication and collaboration’ and ‘creation of digital content’. Less attention is paid to the assessment of the competence areas ‘safely and responsible use’ and ‘problem solving’. Besides, the emphasis of most instruments is rather on the measurement of skills. Attention towards knowledge and attitudes, as important aspects of competences, remain underexposed. Second, an analysis of the provided evidence of the quality of assessment instruments for measuring primary school students’ digital competences is given, based on the Research Centre for Examination and Certification Framework. Results indicate different approaches to increase the quality of the assessment instruments, but there is generally poor reporting of the psychometric properties of the tests. Based on these results, suggestions for further research and practice are discussed.
A model-driven network for guided image denoising

Guided image denoising recovers clean target images by fusing guidance images and noisy target images. Several deep neural networks have been designed for this task, but they are black-box methods lacking interpretability. To overcome the issue, this paper builds a more interpretable network. To start with, an observation model is proposed to account for modality gap between target and guidance images. Then, this paper formulates a deep prior regularized optimization problem, and solves it by alternating direction method of multipliers (ADMM) algorithm. The update rules are generalized to design the network architecture. Extensive experiments conducted on FAIP and RNS datasets manifest that the novel network outperforms several state-of-the-art and benchmark methods regarding both evaluation metrics and visual inspection.
Dual-Pyramidal Image Inpainting With Dynamic Normalization

Deep autoencoder-based approaches have achieved significant improvements on restoring damaged images, yet they still suffer from artifacts due to the inadequate representation and inaccurate regularization of existing features. In this paper, we propose a dual-pyramidal inpainting framework called DPNet to address these two limitations, which seamlessly integrates sufficient feature learning and dynamic regularization within an autoencoder network. Specifically, to exhaustively extract multi-scale features, we adopt layer-wise pyramidal convolution in encoder, which provides an arbitrary combination pool of various receptive fields. Subsequently, to tackle the patch deterioration problem in previous cross-scale non-local schemes, we further propose a Pyramidal Attention Mechanism (PAM) in decoder to acquire finer patches directly from learned layers. Mutually benefited with pyramidal features extraction in encoder, the dissemination space for non-local pixels in our PAM is notably enlarged to pyramidal level, thus significantly benefiting the feature representation. Moreover, to avoid the mask error accumulation in existing works, a dynamic normalization mechanism utilizing the spatial mask information updated in encoder is introduced, which further ensures the feature integrity and consistency. Such a dual-pyramidal structure along with dynamic normalization significantly improve the inpainting quality, outperforming existing competitors. Comprehensive experiments conducted on three benchmark datasets demonstrate that our DPNet performs favorably against the state-of-the-arts.
FedRecAttack: Model Poisoning Attack to Federated Recommendation

Federated Recommendation (FR) has received con-siderable popularity and attention in the past few years. In FR, for each user, its feature vector and interaction data are kept locally on its own client thus are private to others. Without the access to above information, most existing poisoning attacks against recommender systems or federated learning lose validity. Benifiting from this characteristic, FR is commonly considered fairly secured. However, we argue that there is still possible and necessary security improvement could be made in FR. To prove our opinion, in this paper we present FedRecAttack, a model poisoning attack to FR aiming to raise the exposure ratio of target items. In most recommendation scenarios, apart from pri-vate user-item interactions (e.g., clicks, watches and purchases), some interactions are public (e.g., likes, follows and comments). Motivated by this point, in FedRecAttack we make use of the public interactions to approximate users' feature vectors, thereby attacker can generate poisoned gradients accordingly and control malicious users to upload the poisoned gradients in a well-designed way. To evaluate the effectiveness and side effects of FedRecAttack, we conduct extensive experiments on three real-world datasets of different sizes from two completely different scenarios. Experimental results demonstrate that our proposed FedRecAttack achieves the state-of-the-art effectiveness while its side effects are negligible. Moreover, even with small proportion (3%) of malicious users and small proportion (1%) of public interactions, FedRecAttack remains highly effective, which reveals that FR is more vulnerable to attack than people commonly considered.
ALODAD: An Anchor-Free Lightweight Object Detector for Autonomous Driving

Vision-based object detection is an essential component of autonomous driving. Because vehicles typically have limited on-board computing resources, a small-sized detection model is required. Simultaneously, high object detection accuracy and real-time inference detection speeds are required to ensure safety while driving. In this paper, an anchor-free lightweight object detector for autonomous driving called ALODAD is proposed. ALODAD incorporates an attention scheme into the lightweight neural network GhostNet and builds an anchor-free detection framework to achieve lower computational costs and provide parameters with high detection accuracy. Specifically, the lightweight backbone neural network integrates a convolutional block attention model that analyzes the valuable features from traffic scene images to generate an accurate bounding box, and then constructs feature pyramids for multi-scale object detection. The proposed method adds an intersection over union (IoU) branch to the decoupled detector to rank the vast number of candidate detections accurately. To increase the data diversity, data augmentation was used during training. Extensive experiments based on benchmarks demonstrate that the proposed method offers improved performance compared to the baseline. The proposed method can achieve an increased detection accuracy while meeting the real-time requirements of autonomous driving. The proposed method was compared with the YOLOv5 and RetinaNet models and 98.7% and 94.5% were obtained for the average precision metrics AP50 and AP75, respectively, on the BCTSDB dataset.
Practical continuous-variable quantum secret sharing using plug-and-play dual-phase modulation

Continuous-variable quantum secret sharing (CVQSS) allows a legitimate user, i.e., the dealer, to share a string of secret keys with multiple distant users. These users cannot individually recover the dealer's secret key unless they work cooperatively. Although the theoretical security proof of CVQSS has been well established, its practical security and implementation still face challenges. In this paper, we suggest a practical scheme for CVQSS using plug-and-play (P&P) configuration and dual-phase-modulated coherent state (DPMCS). The proposed scheme, called P&P DPM-based CVQSS, waives the necessity that each user has to prepare respective coherent states with their own lasers, thereby eliminating synchronous loopholes caused by different lasers and reducing the complexity of deployment of the user's stations. Moreover, the local oscillator (LO) can be generated locally by the dealer so that the whole CVQSS system could be naturally immune to all LO-aimed attacks. We derive the security bounds for P&P DPM-based CVQSS by properly making most of the existing security analysis techniques of continuous-variable quantum key distribution (CVQKD). In addition, an experimental concept of P&P DPM-based CVQSS is also presented, which can be deemed a guideline for future implementation.
CDGNet: Class Distribution Guided Network for Human Parsing

The objective of human parsing is to partition a human in an image into constituent parts. This task involves labeling each pixel of the human image according to the classes. Since the human body comprises hierarchically structured parts, each body part of an image can have its sole position distribution characteristic. Probably, a human head is less likely to be under the feet, and arms are more likely to be near the torso. Inspired by this observation, we make instance class distributions by accumulating the original human parsing label in the horizontal and vertical directions, which can be utilized as supervision signals. Using these horizontal and vertical class distribution labels, the network is guided to exploit the intrinsic position distribution of each class. We combine two guided features to form a spatial guidance map, which is then superimposed onto the baseline network by multiplication and concatenation to distinguish the human parts precisely. We conducted extensive experiments to demonstrate the effectiveness and superiority of our method on three well-known benchmarks: LIP, ATR, and CIHP databases. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">†</sup> Our code is available at https://github.com/tjpulkl/CDGNet.
Simultaneous Execution of Quantum Circuits on Current and Near-Future NISQ Systems

In the noisy intermediate-scale quantum (NISQ) era, the idea of quantum multiprogramming, running multiple quantum circuits (QCs) simultaneously on the same hardware, helps to improve the throughput of quantum computation.However, the crosstalk, unwanted interference between qubits on NISQ processors, may cause performance degradation when using multiprogramming.To address this challenge, we introduce palloq (parallel allocation of QCs), a novel compilation protocol.Palloq improves the performance of quantum multiprogramming on NISQ processors, while paying attention to 1) the combination of QCs chosen for parallel execution and 2) the assignment of program qubit variables to physical qubits, to reduce unwanted interference among the active set of QCs.We also propose a software-based crosstalk detection protocol using a new combination of randomized benchmarking methods.Our method successfully characterizes the suitability of hardware for multiprogramming with relatively low detection costs.We found a tradeoff between the success rate and execution time of the multiprogramming.Our results will be of value when device throughput becomes a significant bottleneck.Until service providers have enough quantum processors available to more than meet demand, this approach will be attractive to the service providers and users who want to optimize job management and throughput of the processor.
A Prototypical Knowledge Oriented Adaptation Framework for Semantic Segmentation

A prevalent family of fully convolutional networks are capable of learning discriminative representations and producing structural prediction in semantic segmentation tasks. However, such supervised learning methods require a large amount of labeled data and show inability of learning cross-domain invariant representations, giving rise to overfitting performance on the source dataset. Domain adaptation, a transfer learning technique that demonstrates strength on aligning feature distributions, can improve the performance of learning methods by providing inter-domain discrepancy alleviation. Recently introduced output-space based adaptation methods provide significant advances on cross-domain semantic segmentation tasks, however, a lack of consideration for intra-domain divergence of domain discrepancy remains prone to over-adaptation results on the target domain. To address the problem, we first leverage prototypical knowledge on the target domain to relax its hard domain label to a continuous domain space, where pixel-wise domain adaptation is developed upon a soft adversarial loss. The development of prototypical knowledge allows to elaborate specific adaptation strategies on under-aligned regions and well-aligned regions of the target domain. Furthermore, aiming to achieve better adaptation performance, we employ a unilateral discriminator to alleviate implicit uncertainty on prototypical knowledge. At last, we theoretically and experimentally demonstrate that the proposed prototypical knowledge oriented adaptation approach provides effective guidance on distribution alignment and alleviation on over-adaptation. The proposed approach shows competitive performance with state-of-the-art methods on two cross-domain segmentation tasks.
Reasoning With Scene Graphs for Robot Planning Under Partial Observability

Robot planning in partially observable domains is difficult, because a robot needs to estimate the current state and plan actions at the same time. When the domain includes many objects, reasoning about the objects and their relationships makes robot planning even more difficult. In this letter, we develop an algorithm called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">scene analysis for robot planning</i> (SARP) that enables robots to reason with visual contextual information toward achieving long-term goals under uncertainty. SARP constructs scene graphs, a factored representation of objects and their relations, using images captured from different positions, and reasons with them to enable context-aware robot planning under partial observability. Experiments have been conducted using multiple 3D environments in simulation, and a dataset collected by a real robot. In comparison to standard robot planning and scene analysis methods, in a target search domain, SARP improves both efficiency and accuracy in task completion.
Bagua

Recent years have witnessed a growing list of systems for distributed data-parallel training. Existing systems largely fit into two paradigms, i.e., parameter server and MPI-style collective operations. On the algorithmic side, researchers have proposed a wide range of techniques to lower the communication via "system relaxations": quantization, decentralization, and communication delay. However, most, if not all, existing systems only rely on standard synchronous and asynchronous stochastic gradient (SG) based optimization, therefore, cannot take advantage of all possible optimizations that the machine learning community has been developing recently. Given this emerging gap between the current landscapes of systems and theory, we build Bagua, a MPI-style communication library, providing a collection of primitives, that is both flexible and modular to support state-of-the-art system relaxation techniques of distributed training. Powered by this design, Bagua has a great ability to implement and extend various state-of-the-art distributed learning algorithms. In a production cluster with up to 16 machines (128 GPUs), Bagua can outperform PyTorch-DDP, Horovod and BytePS in the end-to-end training time by a significant margin (up to 2X) across a diverse range of tasks. Moreover, we conduct a rigorous tradeoff exploration showing that different algorithms and system relaxations achieve the best performance over different network conditions.
Trust-Based Secure Multi-Cloud Collaboration Framework in Cloud-Fog-Assisted IoT

Cloud-Fog-Assisted Internet-of-Things (IoT) is a convincing paradigm to provide users with on-demand and low-latency services through Fog nodes in the edge of multiple clouds (Multi-Cloud). Multi-Cloud is a scalable multi-domain service-oriented net-centric system and can respond to complicated user requirements leveraging Multi-Cloud Service Composition (MCSC). However, in MCSC, user security can be easily compromised by untrusted and curious cloud service providers that may collect and violate the privacy and other essential assets of cloud users. Although many trust-based MCSC solutions have been proposed to seek a trustworthy composite service with highest trust level, most of them are vulnerable to malicious users intending to break through the clouds and inflict serious data leakage or asset damage. Considering these security concerns on both malicious users and untrusted service providers, in this article, we present a trust-based secure multi-cloud collaboration framework for Cloud-Fog-Assisted IoT systems. Specifically, to guarantee the security of users, we develop a role-based trust evaluation method to enhance the trustworthiness of MCSC. To preserve the security of services, we design an efficient user authentication scheme and a secure collaboration scheme to provide collaborative user authentication and access control mechanism for MCSC. We develop a proof of concept implementation for our framework and demonstrate its practicability by performance evaluation with extensive experiments.
SalFBNet: Learning pseudo-saliency distribution via feedback convolutional networks

Feed-forward only convolutional neural networks (CNNs) may ignore intrinsic relationships and potential benefits of feedback connections in vision tasks such as saliency detection, despite their significant representation capabilities. In this work, we propose a feedback-recursive convolutional framework (SalFBNet) for saliency detection. The proposed feedback model can learn abundant contextual representations by bridging a recursive pathway from higher-level feature blocks to low-level layers. Moreover, we create a large-scale Pseudo-Saliency dataset to alleviate the problem of data deficiency in saliency detection. We first use the proposed feedback model to learn saliency distribution from pseudo-ground-truth. Afterwards, we fine-tune the feedback model on existing eye-fixation datasets. Furthermore, we present a novel Selective Fixation and Non-Fixation Error (sFNE) loss to facilitate the proposed feedback model to better learn distinguishable eye-fixation-based features. Extensive experimental results show that our SalFBNet with fewer parameters achieves competitive results on the public saliency detection benchmarks, which demonstrate the effectiveness of proposed feedback model and Pseudo-Saliency data.
Blockchain 2.O: A Smart Contract

Blockchain technology has the ability to significantly lower transaction costs while also enabling faster and more secure operations. The financial industry has been influenced by the rapid growth of blockchain technology and cryptocurrencies. Finance is just one field where blockchain technology used for the first time. There are many other Health care, Agriculture, Food industries and many more industries where blockchain technology is being used. The only reason for the for rapid development and trust is decentralization and consensus protocols.With the advent of Blockchain technology, smart contracts came into being and are very popular today, but the question is what and what problems do they have. In this paper the details of the conversation will be done the same. The term smart contract was first used by Nick Szabo in 1997.Smart contract concept, to use a distributed ledger of the Blockchain platform to store contracts. Now smart contracts on Blockchain are like contracts in the real world. The only difference between a traditional contract and a Blockchain-based contract, is that they are completely digital. In fact a smart contract is actually a small computer program stored within a blockchain.Smart contract is a Self-executing and Self verifying block of code(agreements) which is useful for applications involving third party, that can operate independently. This chapter presents the study of smart contracts, how is i evolved. Chapter also discuss about how smart contract is different from the traditional contract from the technical point of view. Development platform are very important to understand to go into depth of the technicality related to Smart contract. paper also explores various Applications and platforms for Smart Contract.