Reviewerly/exHarmony huggingface dataset:
Machine Learning Based on Resampling Approaches and Deep Reinforcement Learning for Credit Card Fraud Detection Systems

The problem of imbalanced datasets is a significant concern when creating reliable credit card fraud (CCF) detection systems. In this work, we study and evaluate recent advances in machine learning (ML) algorithms and deep reinforcement learning (DRL) used for CCF detection systems, including fraud and non-fraud labels. Based on two resampling approaches, SMOTE and ADASYN are used to resample the imbalanced CCF dataset. ML algorithms are, then, applied to this balanced dataset to establish CCF detection systems. Next, DRL is employed to create detection systems based on the imbalanced CCF dataset. The diverse classification metrics are indicated to thoroughly evaluate the performance of these ML and DRL models. Through empirical experiments, we identify the reliable degree of ML models based on two resampling approaches and DRL models for CCF detection. When SMOTE and ADASYN are used to resampling original CCF datasets before training/test split, the ML models show very high outcomes of above 99% accuracy. However, when these techniques are employed to resample for only the training CCF datasets, these ML models show lower results, particularly in terms of logistic regression with 1.81% precision and 3.55% F1 score for using ADASYN. Our work reveals the DRL model is ineffective and achieves low performance, with only 34.8% accuracy.
Bright squeezed vacuum for two-photon spectroscopy: simultaneously high resolution in time and frequency, space and wavevector

Entangled photons offer two advantages for two-photon absorption spectroscopy. One of them, the linear scaling of two-photon absorption rate with the input photon flux, is valid only at very low photon fluxes and is therefore impractical. The other is the overcoming of the classical constraints for simultaneous resolution in time-frequency and in space-wavevector. Here we consider bright squeezed vacuum (BSV) as an alternative to entangled photons. The efficiency increase it offers in comparison with coherent light is modest, but it does not depend on the photon flux. Moreover, and this is what we show in this work, BSV also provides simultaneously high resolution in time and frequency, and in space and wavevector. In our experiment, we measure the widths of the second-order correlation functions in space, time, frequency, and angle and demonstrate the violation of the constraint given by the Fourier transformation, in the case of photon pairs, known as the Mancini criterion of entanglement.
Reservoir Computing with Delayed Input for Fast and Easy Optimisation

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.
Breast Invasive Ductal Carcinoma Classification on Whole Slide Images with Weakly-Supervised and Transfer Learning

Invasive ductal carcinoma (IDC) is the most common form of breast cancer. For the non-operative diagnosis of breast carcinoma, core needle biopsy has been widely used in recent years for the evaluation of histopathological features, as it can provide a definitive diagnosis between IDC and benign lesion (e.g., fibroadenoma), and it is cost effective. Due to its widespread use, it could potentially benefit from the use of AI-based tools to aid pathologists in their pathological diagnosis workflows. In this paper, we trained invasive ductal carcinoma (IDC) whole slide image (WSI) classification models using transfer learning and weakly-supervised learning. We evaluated the models on a core needle biopsy (n = 522) test set as well as three surgical test sets (n = 1129) obtaining ROC AUCs in the range of 0.95-0.98. The promising results demonstrate the potential of applying such models as diagnostic aid tools for pathologists in clinical practice.
Classification of imbalanced oral cancer image data from high-risk population

Early detection of oral cancer is vital for high-risk patients, and machine learning-based automatic classification is ideal for disease screening. However, current datasets collected from high-risk populations are unbalanced and often have detrimental effects on the performance of classification.To reduce the class bias caused by data imbalance.We collected 3851 polarized white light cheek mucosa images using our customized oral cancer screening device. We use weight balancing, data augmentation, undersampling, focal loss, and ensemble methods to improve the neural network performance of oral cancer image classification with the imbalanced multi-class datasets captured from high-risk populations during oral cancer screening in low-resource settings.By applying both data-level and algorithm-level approaches to the deep learning training process, the performance of the minority classes, which were difficult to distinguish at the beginning, has been improved. The accuracy of "premalignancy" class is also increased, which is ideal for screening applications.Experimental results show that the class bias induced by imbalanced oral cancer image datasets could be reduced using both data- and algorithm-level methods. Our study may provide an important basis for helping understand the influence of unbalanced datasets on oral cancer deep learning classifiers and how to mitigate.
Lightweight Federated Learning for Large-Scale IoT Devices With Privacy Guarantee

With the massive deployment of the Internet of Things (IoT) devices, many data analysis applications emerge for the large amount of data accumulated by IoT. Federated learning (FedL) on IoT devices is an appealing mode to train a precise data analysis model. However, existing FedL schemes either take expensive computation costs (e.g., public-key cryptographic operations) or a large number of interactions among participants. Obviously, these schemes are unsuitable for IoT devices due to the limited computational and communication resources. In this work, we propose a lightweight privacy-preserving FedL scheme for IoT devices. To protect the privacy of individual local data, we add masks to intervening parameters. An effective secret-sharing scheme is adopted to ensure that masks can be eliminated accurately. Considering that FedL involves multiple iterations and mask generation for each iteration costs a large number of interactions among users for privacy guarantee, we also design a secure mask reusing mechanism for large-scale FedL tasks. We prove that our scheme is secure against the honest-but-curious model. In addition, we also expand our scheme to deal with the collusion attack. Extensive experiments on real IoT devices demonstrate the accuracy and efficiency of our work.
Cross-Modal Prostate Cancer Segmentation via Self-Attention Distillation

The automatic and accurate segmentation of the prostate cancer from the multi-modal magnetic resonance images is of prime importance for the disease assessment and follow-up treatment plan. However, how to use the multi-modal image features more efficiently is still a challenging problem in the field of medical image segmentation. In this paper, we develop a cross-modal self-attention distillation network by fully exploiting the encoded information of the intermediate layers from different modalities, and the generated attention maps of different modalities enable the model to transfer significant and discriminative information that contains more details. Moreover, a novel spatial correlated feature fusion module is further employed for learning more complementary correlation and non-linear information of different modality images. We evaluate our model in five-fold cross-validation on 358 MRI images with biopsy confirmed. Without bells and whistles, our proposed network achieves state-of-the-art performance on extensive experiments.
BARThez: a Skilled Pretrained French Sequence-to-Sequence Model

Inductive transfer learning has taken the entire NLP field by storm, with models such as BERT and BART setting new state of the art on countless NLU tasks.However, most of the available models and research have been conducted for English.In this work, we introduce BARThez, the first large-scale pretrained seq2seq model for French.Being based on BART, BARThez is particularly well-suited for generative tasks.We evaluate BARThez on five discriminative tasks from the FLUE benchmark and two generative tasks from a novel summarization dataset, Orange-Sum, that we created for this research.We show BARThez to be very competitive with state-of-the-art BERT-based French language models such as CamemBERT and FlauBERT.We also continue the pretraining of a multilingual BART on BARThez' corpus, and show our resulting model, mBARThez, to significantly boost BARThez' generative performance.Code, data and models are publicly available.
The Jaynes–Cummings model of a two-level atom in a single-mode para-Bose cavity field

Abstract The coherent states in the parity deformed analog of standard boson Glauber coherent states are generated, which admit a resolution of unity with a positive measure. The quantum-mechanical nature of the light field of these para-Bose states is studied, and it is found that para-Bose order plays an important role in the nonclassical behaviors including photon antibunching, sub-Poissonian statistics, signal-to-quantum noise ratio, quadrature squeezing effect, and multi-peaked number distribution. Furthermore, we consider the Jaynes-Cummings model of a two-level atom in a para-Bose cavity field with the initial states of the excited and Glauber coherent ones when the atom makes one-photon transitions, and obtain exact energy spectrum and eigenstates of the deformed model. Nonclassical properties of the time-evolved para-Bose atom-field states are exhibited through evaluating the fidelity, evolution of atomic inversion, level damping, and von Neumann entropy. It is shown that the evolution time and the para-Bose order control these properties.
FabricFL: Blockchain-in-the-Loop Federated Learning for Trusted Decentralized Systems

Federated learning (FL) enables collaborative training of machine learning (ML) models while preserving user data privacy. Existing FL approaches can potentially facilitate collaborative ML, but ensuring secure trading/sharing of training data is challenging in practice, particularly in the presence of adversarial FL clients. The ongoing security concerns around FL and strict laws on personally identifiable information necessitate the design of a robust and trusted FL framework, for example, using blockchain. Existing blockchain-based solutions are generally not of industrial strength, where limitations include scalability and lack of engagement by participating clients. In this article, blockchain-in-the-loop FL is our proposed approach of intertwining classic FL and Hyperledger Fabric with a gamification component. Our proposed approach is a fusion of secure application integrated to seal and sign-off asynchronous and synchronous collaborative tasks of FL. The enterprise-level blockchain network provides an immutable ledger that can be leveraged at different FL layers to ensure auditable tracing and level-up security in industrial settings. We evaluate our proposed approach with three different datasets to demonstrate the security enhancements that improve the FL process, resulting in a more accurate global ML model to converge with the possible best performance.
CFNTT: Scalable Radix-2/4 NTT Multiplication Architecture with an Efficient Conflict-free Memory Mapping Scheme

Number theoretic transform (NTT) is widely utilized to speed up polynomial multiplication, which is the critical computation bottleneck in a lot of cryptographic algorithms like lattice-based post-quantum cryptography (PQC) and homomorphic encryption (HE). One of the tendency for NTT hardware architecture is to support diverse security parameters and meet resource constraints on different computing platforms. Thus flexibility and Area-Time Product (ATP) become two crucial metrics in NTT hardware design. The flexibility of NTT in terms of different vector sizes and moduli can be obtained directly. Whereas the varying strides in memory access of in-place NTT render the design for different radix and number of parallel butterfly units a tough problem. This paper proposes an efficient conflict-free memory mapping scheme that supports the configuration for both multiple parallel butterfly units and arbitrary radix of NTT. Compared to other approaches, this scheme owns broader applicability and facilitates the parallelization of non-radix-2 NTT hardware design. Based on this scheme, we propose a scalable radix-2 and radix-4 NTT multiplication architecture by algorithm-hardware co-design. A dedicated schedule method is leveraged to reduce the number of modular additions/subtractions and modular multiplications in radix-4 butterfly unit by 20% and 33%, respectively. To avoid the bit-reversed cost and save memory footprint in arbitrary radix NTT/INTT, we put forward a general method by rearranging the loop structure and reusing the twiddle factors. The hardware-level optimization is achieved by excavating the symmetric operators in radix-4 butterfly unit, which saves almost 50% hardware resources compared to a straightforward implementation. Through experimental results and theoretical analysis, we point out that the radix-4 NTT with the same number of parallel butterfly units outperforms the radix-2 NTT in terms of area-time performance in the interleaved memory system. This advantage is enlarged when increasing the number of parallel butterfly units. For example, when processing 1024 14-bit points NTT with 8 parallel butterfly units, the ATP of LUT/FF/DSP/BRAM n radix-4 NTT core is approximately 2.2 × /1.2 × /1.1 × /1.9 × less than that of the radix-2 NTT core on a similar FPGA platform.
Towards Discovery of Influence and Personality Traits through Social Link Prediction

Estimation of a person's influence and personality traits from social media data has many applications. We use social linkage criteria, such as number of followers and friends, as proxies to form corpora, from popular blogging site Livejournal, for examining two two-class classification problems: influential vs. non-influential, and extraversion vs. introversion. Classification is performed using automatically-derived psycholinguistic and mood-based features of a user's textual messages. We experiment with three sub-corpora of 10000 users each, and present the most effective predictors for each category. The best classification result, at 80%, is achieved using psycholinguistic features; e.g., influentials are found to use more complex language, than non-influentials, and use more leisure-related terms.
Gesture-Based Human–Machine Interaction: Taxonomy, Problem Definition, and Analysis

The possibility for humans to interact with physical or virtual systems using gestures has been vastly explored by researchers and designers in the last 20 years to provide new and intuitive interaction modalities. Unfortunately, the literature about gestural interaction is not homogeneous, and it is characterized by a lack of shared terminology. This leads to fragmented results and makes it difficult for research activities to build on top of state-of-the-art results and approaches. The analysis in this article aims at creating a common conceptual design framework to enforce development efforts in gesture-based human-machine interaction (HMI). The main contributions of this article can be summarized as follows: 1) we provide a broad definition for the notion of functional gesture in HMI; 2) we design a flexible and expandable gesture taxonomy; and 3) we put forward a detailed problem statement for gesture-based HMI. Finally, to support our main contribution, this article presents and analyzes 83 most pertinent articles classified on the basis of our taxonomy and problem statement.
DarkLighter: Light Up the Darkness for UAV Tracking

Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems. However, current CNN-based trackers can hardly generalize well to low-light scenes that are commonly lacked in the existing training set. In indistinguishable night scenarios frequently encountered in unmanned aerial vehicle (UAV) tracking-based applications, the robustness of the state-of-the-art (SOTA) trackers drops significantly. To facilitate aerial tracking in the dark through a general fashion, this work proposes a low-light image enhancer namely DarkLighter, which dedicates to alleviate the impact of poor illumination and noise iteratively. A lightweight map estimation network, i.e., ME-Net, is trained to efficiently estimate illumination maps and noise maps jointly. Experiments are conducted with several SOTA trackers on numerous UAV dark tracking scenes. Exhaustive evaluations demonstrate the reliability and universality of DarkLighter, with high efficiency. Moreover, DarkLighter has further been implemented on a typical UAV system. Real-world tests at night scenes have verified its practicability and dependability.
Chaos in coupled Kerr-nonlinear parametric oscillators

A Kerr-nonlinear parametric oscillator (KPO) can generate a quantum superposition of two oscillating states, known as a Schr\"odinger cat state, via quantum adiabatic evolution and can be used as a qubit for gate-based quantum computing and quantum annealing. In this work, we investigate complex dynamics, i.e., chaos, in two coupled nondissipative KPOs at a few-photon level. After showing that a classical model for this system is nonintegrable and consequently exhibits chaotic behavior, we provide quantum counterparts for the classical results, which are quantum versions of the Poincar\'e surface of section and its lower-dimensional version defined with time integrals of the Wigner and Husimi functions and also the initial and long-term behavior of out-of-time-ordered correlators. We conclude that some of them can be regarded as quantum signatures of chaos, together with energy-level spacing statistics (conventional signature). Thus, the system of coupled KPOs is expected to offer not only an alternative approach to quantum computing but also a promising platform for the study on quantum chaos.
xFraud

At online retail platforms, it is crucial to actively detect the risks of transactions to improve customer experience and minimize financial loss. In this work, we propose xFraud, an explainable fraud transaction prediction framework which is mainly composed of a detector and an explainer. The xFraud detector can effectively and efficiently predict the legitimacy of incoming transactions. Specifically, it utilizes a heterogeneous graph neural network to learn expressive representations from the informative heterogeneously typed entities in the transaction logs. The explainer in xFraud can generate meaningful and human-understandable explanations from graphs to facilitate further processes in the business unit. In our experiments with xFraud on real transaction networks with up to 1.1 billion nodes and 3.7 billion edges, xFraud is able to outperform various baseline models in many evaluation metrics while remaining scalable in distributed settings. In addition, we show that xFraud explainer can generate reasonable explanations to significantly assist the business analysis via both quantitative and qualitative evaluations.
An unsupervised method for social network spammer detection based on user information interests

Abstract Online Social Networks (OSNs) are a popular platform for communication and collaboration. Spammers are highly active in OSNs. Uncovering spammers has become one of the most challenging problems in OSNs. Classification-based supervised approaches are the most commonly used method for detecting spammers. Classification-based systems suffer from limitations of “data labelling”, “spam drift”, “imbalanced datasets” and “data fabrication”. These limitations effect the accuracy of a classifier’s detection. An unsupervised approach does not require labelled datasets. We aim to address the limitation of data labelling and spam drifting through an unsupervised approach.We present a pure unsupervised approach for spammer detection based on the peer acceptance of a user in a social network to distinguish spammers from genuine users. The peer acceptance of a user to another user is calculated based on common shared interests over multiple shared topics between the two users. The main contribution of this paper is the introduction of a pure unsupervised spammer detection approach based on users’ peer acceptance. Our approach does not require labelled training datasets. While it does not better the accuracy of supervised classification-based approaches, our approach has become a successful alternative for traditional classifiers for spam detection by achieving an accuracy of 96.9%.
Resource Competition in Blockchain Networks Under Cloud and Device Enabled Participation

Blockchain technology is a promising resource management architecture due to its ability of building trust in a decentralized transaction.Block mining participants, i.e. miners, are incentivized with reward for successfully mining blocks.Unfortunately, solving the proof-of-work puzzle consumes substantial computing powers during the mining period, which greatly challenges miners.Mobile devices also fail to participate in mining because of limited resource.To solve these issues, we are motivated to propose a mining framework of alleviating miner's computation-intensive mining burdens, as well as enabling mobile devices' participation.Depending on the proposed model, miners are capable of offloading their computation-intensive tasks to the edge cloud and mobile devices.The interactions among them formulate a muti-leader multi-follower Stackelberg game.We achieve the Subgame Perfect Equilibrium (SPE) in the game, which guarantees three types of participants to realize profit maximization.Simulation results demonstrate the effectiveness of the proposed model.