Reviewerly/exHarmony huggingface dataset:
Homomorphic Encryption-Based Federated Privacy Preservation for Deep Active Learning

Active learning is a technique for maximizing performance of machine learning with minimal labeling effort and letting the machine automatically and adaptively select the most informative data for labeling. Since the labels on records may contain sensitive information, privacy-preserving mechanisms should be integrated into active learning. We propose a privacy-preservation scheme for active learning using homomorphic encryption-based federated learning. Federated learning provides distributed computation from multiple clients, and homomorphic encryption enhances the privacy preservation of user data with a strong security level. The experimental result shows that the proposed homomorphic encryption-based federated learning scheme can preserve privacy in active learning while maintaining model accuracy. Furthermore, we also provide a Deep Leakage Gradient comparison. The proposed scheme has no gradient leakage compared to the related schemes that have more than 74% gradient leakage.
Data augmentation with improved regularisation and sampling for imbalanced blood cell image classification

Due to progression in cell-cycle or duration of storage, classification of morphological changes in human blood cells is important for correct and effective clinical decisions. Automated classification systems help avoid subjective outcomes and are more efficient. Deep learning and more specifically Convolutional Neural Networks have achieved state-of-the-art performance on various biomedical image classification problems. However, real-world data often suffers from the data imbalance problem, owing to which the trained classifier is biased towards the majority classes and does not perform well on the minority classes. This study presents an imbalanced blood cells classification method that utilises Wasserstein divergence GAN, mixup and novel nonlinear mixup for data augmentation to achieve oversampling of the minority classes. We also present a minority class focussed sampling strategy, which allows effective representation of minority class samples produced by all three data augmentation techniques and contributes to the classification performance. The method was evaluated on two publicly available datasets of immortalised human T-lymphocyte cells and Red Blood Cells. Classification performance evaluated using F1-score shows that our proposed approach outperforms existing methods on the same datasets.
Deep Domain Adversarial Learning for Species-Agnostic Classification of Histologic Subtypes of Osteosarcoma

Osteosarcomas (OSs) are aggressive bone tumors with many divergent histologic patterns. During pathology review, OSs are subtyped based on the predominant histologic pattern; however, tumors often demonstrate multiple patterns. This high tumor heterogeneity coupled with scarcity of samples compared with other tumor types render histology-based prognosis of OSs challenging. To combat lower case numbers in humans, dogs with spontaneous OSs have been suggested as a model species. Herein, a convolutional neural network was adversarially trained to classify distinct histologic patterns of OS in humans using mostly canine OS data during training. Adversarial training improved domain adaption of a histologic subtype classifier from canines to humans, achieving an average multiclass F1 score of 0.77 (95% CI, 0.74-0.79) and 0.80 (95% CI, 0.78-0.81) when compared with the ground truth in canines and humans, respectively. Finally, this trained model, when used to characterize the histologic landscape of 306 canine OSs, uncovered distinct clusters with markedly different clinical responses to standard-of-care therapy.
An extensive review on quantum computers

• Quantum Error Correction (QEC) secured quantum data from decoherence and quantum noise. • Quantum key distribution developed an information-theoretically safe solution for key commerce. • Quantum computers illustrated very efficient and tremendously fast. • Qiskit converted programming language to quantum machine language. Quantum Computing is a rapidly arising innovation that tackles the laws of quantum mechanics to take care of issues excessively complex for already available classical computers. The development of quantum computers is the most remarkable achievement in the field and legacy of quantum computers. For instance, an already available classical computer can represent any number between 0 and 255 using just eight bits. However, a quantum computer can simultaneously represent all 256 numbers between 0 and 255 with just eight qubits. More numbers could be represented by a few hundred entangled qubits than there are atoms in the universe. There are many quantum computers available such as quantum circuit models, quantum Turing machine, adiabatic quantum computer, one-way quantum computers, and various quantum cellular automata. D-wave quantum computers have been available for more than eight years which use a process called quantum annealing to search for solutions to a problem. IBM, Microsoft, Google, Intel, and NASA and various universities around the globe have engaged themselves in the development of quantum computers and their applications. This paper presents the fundamental ideas of quantum computing and portrays notable quantum applications for non-physicists. The ongoing status of the improvements in quantum computers is likewise introduced.
"I Want To See How Smart This AI Really Is": Player Mental Model Development of an Adversarial AI Player

Understanding players' mental models are crucial for game designers who wish to successfully integrate player-AI interactions into their game. However, game designers face the difficult challenge of anticipating how players model these AI agents during gameplay and how they may change their mental models with experience. In this work, we conduct a qualitative study to examine how a pair of players develop mental models of an adversarial AI player during gameplay in the multiplayer drawing game iNNk. We conducted ten gameplay sessions in which two players (n = 20, 10 pairs) worked together to defeat an AI player. As a result of our analysis, we uncovered two dominant dimensions that describe players' mental model development (i.e., focus and style). The first dimension describes the focus of development which refers to what players pay attention to for the development of their mental model (i.e., top-down vs. bottom-up focus). The second dimension describes the differences in the style of development, which refers to how players integrate new information into their mental model (i.e., systematic vs. reactive style). In our preliminary framework, we further note how players process a change when a discrepancy occurs, which we observed occur through comparisons (i.e., compare to other systems, compare to gameplay, compare to self). We offer these results as a preliminary framework for player mental model development to help game designers anticipate how different players may model adversarial AI players during gameplay.
LRB-Net: Improving VQA via division of labor strategy and multimodal classifiers

Visual question answering (VQA), along with multiple types of image and textual questions, makes it a challenging task to infer the correct answer. Consequently, traditional methods rely on relevant cross-modal objects and seldom leverage the cooperation of visual appearance and textual understanding. Here, we present LRBNet, a model-based approach to the problem by analyzing VQA as a division of labor strategy. Using a dictionary and pre-trained GloVe vectors, we embedded region captions and questions by the LSTM. Furthermore, we use graphs to model image region captions and features and then feed them into two GNN-based networks to capture the semantic and visual relations. Finally, we modulate the vertex features of multimodal graphs. Thus, the question embedding and vertex features are fed into the multi-level answer predictor to produce the results. We experimentally validate that LRBNet is an exciting framework for visual–textual understanding and a more challenging alternative to better VQA because it needs to understand the image to be successful. Our study provides complementary prediction through hierarchical representation within and beyond the interactive understanding of the textual sequence and the images, and the experimental results show that LRBNet outperforms other leading models in most cases.
A parallel differential evolution with cooperative multi-search strategy for sizing truss optimization

The increasing complexity of modern structural design problems requires optimization algorithms to have an acceptable completion time regarding the huge number of design variables. This paper proposes a parallel differential evolution with cooperative multi-search strategies (PDECMS) and the implementation with Compute Unified Device Architecture (CUDA) for improving execution time by leveraging the Graphical Processing Unit (GPU). Three sub-populations with dedicated mutation schemes are used to establish island models, which start searching at distinct initial points. As the evolution process begins, the exchange of knowledge between islands is synchronously conducted via the migration of elite individuals. The PDECMS is used to solve five discrete sizing optimization problems of a truss structure to demonstrate the achieved solution quality, convergence speed, and scalability. It has been found that the computing time of PDECMS was at least two times faster than its serial implementation for the large population size and the attained solution quality was generally agreeable with other methods despite the sacrifice for the enhancement of performance. Numerical results reveal that the accomplishment of optimal solutions with fewer iterations and a shorter time comes from the cooperative multi-search strategy and the use of GPU. This outcome, therefore, shows that the PDECMS is capable of optimally solving multi-variable problems with a large search space. • A parallel differential evolution with cooperative multi-search strategy for sizing truss optimization. • The parallelization of the proposed algorithm using Compute Unified Device Architecture (CUDA). • Minimize computational cost specifically for huge population size problems. • Significant improvement on convergence with the information exchange between populations.
Breast cancer detection model using fuzzy entropy segmentation and ensemble classification

• Exploits the advanced Self Improved Cat Swarm Optimization (SI-CSO) to propose a new Breast Cancer Detection Model. • Promotes SI-CSO based optimization, where includes “(i) Pre-processing (ii) Segmentation (iii) Feature extraction (iv) Optimal feature selection and (ii) Classification”. • The performance of SI-CSO based Detection model is better than standard ensemble + WOA, ensemble + MFO, ensemble + GA and ensemble + CSO models. According to studies, breast cancer is the deadliest disease and the main cause of the elevated mortality rates among women. The main method for detecting breast cancer is mammography. Even today, using a mammography image to make earlier detection of breast cancer remains a complex undertaking. In this work, the proposed system includes the following processes: “(i) pre-processing (ii) segmentation (iii) feature extraction (iv) optimal feature selection and (v) classification”. Initially, RGB-Grey scale conversion is done during pre-processing, and segmentation is carried out using the proposed fuzzy entropy segmentation model. Then the features including “fractal features like lacunarity, fractal dimension and texture features such as Grey Level Co-occurrence Matrix (GLCM) and Proposed Local Binary Patterns (LBP)” are determined. Then, in the prediction phase, an optimized ensemble classifier is introduced. The proposed ensemble classifier is constructed by amalgamating the “Random Forest (RF), Support Vector Machine (SVM) Neural Network (NN) and Optimized CNN” respectively. The extracted features are subjected to SVM, NN and RF, simultaneously. Here, optimized CNN determines the final results. In particular, Self Improved Cat Swarm Optimization (SI-CSO) is used to fine-tune CNN's weights. Finally, many metrics demonstrate the proposed model's efficiency.
AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation

Temporal action proposal generation (TAPG) is a challenging task, which requires localizing action intervals in an untrimmed video. Intuitively, we as humans, perceive an action through the interactions between actors, relevant objects, and the surrounding environment. Despite the significant progress of TAPG, a vast majority of existing methods ignore the aforementioned principle of the human perceiving process by applying a backbone network into a given video as a black-box. In this paper, we propose to model these interactions with a multi-modal representation network, namely, Actors-Objects-Environment Interaction Network (AOE-Net). Our AOE-Net consists of two modules, i.e., perception-based multi-modal representation (PMR) and boundary-matching module (BMM). Additionally, we introduce adaptive attention mechanism (AAM) in PMR to focus only on main actors (or relevant objects) and model the relationships among them. PMR module represents each video snippet by a visual-linguistic feature, in which main actors and surrounding environment are represented by visual information, whereas relevant objects are depicted by linguistic features through an image-text model. BMM module processes the sequence of visual-linguistic features as its input and generates action proposals. Comprehensive experiments and extensive ablation studies on ActivityNet $$-$$ 1.3 and THUMOS-14 datasets show that our proposed AOE-Net outperforms previous state-of-the-art methods with remarkable performance and generalization for both TAPG and temporal action detection. To prove the robustness and effectiveness of AOE-Net, we further conduct an ablation study on egocentric videos, i.e. EPIC-KITCHENS 100 dataset. Our source code is publicly available at https://github.com/UARK-AICV/AOE-Net .
Strategies for training optical neural networks

Optical neural networks have great potential to process information efficiently with the speed of light. Compared with electronic competitors, multiple tasks have been successfully demonstrated in such computing systems with several orders of magnitude improvement in energy consumption. However, current training strategies, as a critical step for bringing optical neural networks into reality, are still at an early stage. The high complexity and long training time fail to meet the requirement for training optical neural networks massively and efficiently. Therefore, appropriate training strategies are still in great demand for future deployment of large scale optical neural networks. In this paper, we investigate current training approaches and the possible paths which could make optical neural networks highly expressive and realistic are also discussed.
Video restoration based on deep learning: a comprehensive survey

Abstract Video restoration concerns the recovery of a clean video sequence starting from its degraded version. Different video restoration tasks exist, including denoising, deblurring, super-resolution, and reduction of compression artifacts. In this paper, we provide a comprehensive review of the main features of existing video restoration methods based on deep learning. We focus our attention on the main architectural components, strategies for motion handling, and loss functions. We analyze the standard benchmark datasets and use them to summarize the performance of video restoration methods, both in terms of effectiveness and efficiency. In conclusion, the main challenges and future research directions in video restoration using deep learning are highlighted.
Efficient and Accurate Homomorphic Comparisons

We design and implement a new efficient and accurate fully homomorphic argmin/min or argmax/max comparison operator, which finds its application in numerous real-world use cases as a classifier. In particular we propose two versions of our algorithms using different tools from TFHE's functional bootstrapping toolkit. Our algorithm scales to any number of input data points with linear time complexity and logarithmic noise-propagation. Our algorithm is the fastest on the market for non-parallel comparisons with a high degree of accuracy and precision. For MNIST and SVHN datasets, which work under the PATE framework, using our algorithm, we achieve an accuracy of around 99.95% for both.
A Secure Multi-factor Authentication Protocol for Healthcare Services Using燙loud-based SDN

Cloud-based SDN (Software Defined Network) integration offers new kinds of agility, flexibility, automation, and speed in the network.Enterprises and Cloud providers both leverage the benefits as networks can be configured and optimized based on the application requirement.The integration of cloud and SDN paradigms has played an indispensable role in improving ubiquitous health care services.It has improved the real-time monitoring of patients by medical practitioners.Patients' data get stored at the central server on the cloud from where it is available to medical practitioners in no time.The centralisation of data on the server makes it more vulnerable to malicious attacks and causes a major threat to patients' privacy.In recent days, several schemes have been proposed to ensure the safety of patients' data.But most of the techniques still lack the practical implementation and safety of data.In this paper, a secure multi-factor authentication protocol using a hash function has been proposed.BAN (Body Area Network) logic has been used to formally analyse the proposed scheme and ensure that no unauthenticated user can steal sensitivepatient information.Security Protocol Animator (SPAN)-Automated Validation of Internet Security Protocols and Applications (AVISPA) tool has been used for simulation.The results prove that the proposed scheme ensures secure access to the database in terms of spoofing and identification.Performance comparisons of the proposed scheme with other related historical schemes regarding time complexity, computation cost which accounts to only 423 ms in proposed, and security parameters such as identification and spoofing prove its efficiency.
Gender, political inclusion, and democracy in Africa: Some empirical evidence

Abstract The objective of this study is to examine the effect of political inclusion on democracy in Africa. The results of the analyses through ordinary least squares, system generalized method of moments, IV Tobit and instrumental variables two stage least squares show that gender political inclusion enhances democracy in Africa. This result is robust across alternative specifications of political inclusion and democracy. The results equally stood when controlled for colonization and internal conflicts. In terms of policy implications, policy makers in Africa should enhance their fight for political inclusion as one of the gateways to promoting democracy. In this respect, national laws could be put in place which impose gender quotas in political positions in every country. Equally, the African Union could sign a convention on these quotas for respective countries to ratify. Related Articles Bingham, Natasha. 2016. “Fighting for Our Cause: The Impact of Women's NGOs on Gender Policy Adoption in Four Former Soviet Republics.” Politics &amp; Policy 44(2): 294–318. https://doi.org/10.1111/polp.12155 . Hankivsky, Olena. 2013. “Gender Mainstreaming: A Five‐Country Examination.” Politics &amp; Policy 41(5): 629–55. https://doi.org/10.1111/polp.12037 . Herrick, Rebekah. 2008. “Gender Gap in Early Congressional Retirement.” Politics &amp; Policy 32(3): 294–318. https://doi.org/10.1111/j.1747‐1346.2004.tb00189.x .
An imbalanced binary classification method based on contrastive learning using multi-label confidence comparisons within sample-neighbors pair

For imbalanced classification, data-level methods can achieve inter-class balance, but the samples generated do not contain new information and cannot avoid the problem of introducing noise. Algorithm-level methods may lead to overfitting of the model, and its classification effect is more dependent on the specific dataset and classification task, which means they lack universality. In addition, how to deeply mine the differences in the distribution of data overlap areas, and how to effectively mine the differences between categories when the absolute number of minority samples is small, are also important challenges in imbalanced classification. This paper proposes an imbalanced binary classification method using multi-label confidence comparisons based on contrastive learning. Different from the previous idea of directly learning its distribution characteristics from minority samples, combined with the idea of contrastive learning, the classification task is redefined as the multi-label matching task by mining the deep features that can represent the commonality and difference between the neighboring samples. Multiple differentiated contrastive sample groups are obtained through random sampling in its neighbor sample pool for each sample. This sample is combined with its contrastive sample groups to form multiple sample-neighbor pairs as training samples in the multi-label matching task. The original dataset is multiplied without introducing noise, laying a foundation for the effective mining of class differences when the absolute number of minority class samples is small. Based on the corresponding reconstruction error generated by Variational AutoEncoder (VAE), for sample-neighbor pairs, a multi-label matching loss between target samples and contrastive sample groups that integrates the idea of contrastive learning is designed. a robust classifier is obtained through simultaneous iterative learning of reconstruction error and multi-label matching loss, which can better mine the distribution differences of overlapping regions. In the testing phase, multiple different contrastive sample groups and the corresponding prediction results of the samples to be classified are obtained, which categories can be judged by integrating the predictions of each group for reverse reasoning. Experimental results on 38 public datasets show that the method outperforms typical imbalanced classification methods in both F1-measure and G-mean.
The case of rWallet: A blockchain-based tool to navigate some challenges related to irregular migration

Migration (irregular and forced) represents one of the major challenges the international community faces today. Inasmuch as the phenomenon of irregular and forced migration is the marker of the state of socio-economic systems around the world, the response to and the ways of navigating the resulting multi-scalar challenges mirror not only the efficiency of the global regulatory frameworks, but also our civility. Recognizing the potential inherent in sophisticated information and communication technology (ICT), specifically the blockchain technology and smart contracts, this paper focuses on the special case of “welcome centers” that irregular migrants enter in the hope of acquiring international legal protection and thus refugee status. Since the process may be time-consuming and the living conditions undignified, this paper proposes a tool, named here “responsible wallet”, aka rWallet, that bears the promise of navigating some of these challenges. rWallet derives from the recognition that in modern societies ICT should serve the purpose of improving the quality of life of all people. • (Irregular) migration is the key challenge our societies face today. • Precarity and immobility are the key issues that challenge irregular migrants. • Also the receiving communities are challenged and their vulnerability rises. • Blockchain-based tools offer a way of navigating these challenges. • The rWallet is a blockchain-based tool that does it.