Reviewerly/exHarmony huggingface dataset:
Interpretable AI for bio-medical applications

This paper presents the use of two popular explainability tools called Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP) to explain the predictions made by a trained deep neural network. The deep neural network used in this work is trained on the UCI Breast Cancer Wisconsin dataset. The neural network is used to classify the masses found in patients as benign or malignant based on 30 features that describe the mass. LIME and SHAP are then used to explain the individual predictions made by the trained neural network model. The explanations provide further insights into the relationship between the input features and the predictions. SHAP methodology additionally provides a more holistic view of the effect of the inputs on the output predictions. The results also present the commonalities between the insights gained using LIME and SHAP. Although this paper focuses on the use of deep neural networks trained on UCI Breast Cancer Wisconsin dataset, the methodology can be applied to other neural networks and architectures trained on other applications. The deep neural network trained in this work provides a high level of accuracy. Analyzing the model using LIME and SHAP adds the much desired benefit of providing explanations for the recommendations made by the trained model.
SELF: a stacked-based ensemble learning framework for breast cancer classification

Nowadays, breast cancer is the most prevalent and jeopardous disease in women after lung cancer. During the past few decades, a substantial amount of cancer cases have been reported throughout the world. Breast cancer has been a widely acknowledged category of cancer disease in women due to the lack of awareness. According to the world cancer survey report 2020, about 2.3 million cases and 685,000 deaths have been reported worldwide. As, the patient-doctor ratio (PDR) is very high; consequently, there is an utmost need for a machine-based intelligent breast cancer diagnosis system that can detect cancer at its early stage and cure it more efficiently. The plan is to assemble scientists in both the restorative and the machine learning fields to progress toward this clinical application. This paper presents SELF, a stacked-based ensemble learning framework, to classify breast cancer at an early stage from the histopathological images of tumor cells with computer-aided diagnosis tools. In this work, we use the BreakHis dataset with 7909 histopathological images and Wisconsin Breast Cancer Database (WBCD) with 569 instances for the performance evaluation of our proposed framework. We have trained several distinct classifiers on both datasets and selected the best five classifiers on the basis of their accuracy measure to create our ensemble model. We use the stacking ensemble technique and consider the Extra tree, Random Forest, AdaBoost, Gradient Boosting, and KNN9 classifiers as the base learners, and the logistic regression model as a final estimator. We have evaluated the performance of SELF on the BreakHis dataset and WBCD datasets and achieved testing accuracy of approximately 95% and 99% respectively. The result of the other performance parameters on the BreakHis and the WBCD datasets also showed that our proposed framework outperforms with F1-Score, ROC, and MCC scores.
Design of Lossless Compression System for CCSDS On-board Data Based on FPGA

星载数据的无损压缩对星载数据的存储和传输具有重要意义.CCSDS推荐的RICE算法对于实现数据的无损压缩具有很好的效果.本文针对RICE算法中k值选择算法计算量大的缺点，利用预处理样本最大值优化k值的方法，使优化后的算法计算复杂度更低，资源占用率更小，运行时间更短，但压缩比相近.利用FPGA进行硬件实现并通过仿真和试验测试，使星载数据的压缩比达到1.5以上，可以满足星载系统对数据压缩的要求，为星上其他载荷提供更多资源.
Obscene image detection using transfer learning and feature fusion

Deep learning-based methods have been proven excellent performance in detecting pornographic images/videos flooded on social media. However, in a dearth of huge yet well-labeled datasets, these methods may suffer from under/overfitting problems and may exhibit unstable output responses in the classification process. To deal with the issue we have suggested an automatic pornographic image detection method by utilizing transfer learning (TL) and feature fusion. The novelty of our proposed work is TL based feature fusion process (FFP) which enables the removal of hyper-parameter tuning, improves model performance, and lowers the computational burden of the desired model. FFP fuses low-level and mid-level features of the outperforming pre-trained models followed by transferring the learned knowledge to control the classification process. Key contributions of our proposed method are i) generation of a well-labeled obscene image dataset GGOI via Pix-2-Pix GAN architecture for the training of deep learning models ii) modification of model architectures by integrating batch normalization and mixed pooling strategy to obtain training stability (iii) selection of outperforming models to be integrated with the FFP by performing end-to-end detection of obscene images and iv) design of TL based obscene image detection method by retraining the last layer of the fused model. Extensive experimental analyses are performed on benchmark datasets i.e., NPDI, Pornography 2k, and generated GGOI dataset. The proposed TL model with fused MobileNet V2 + DenseNet169 network performs as the state-of-the-art model compared to existing methods and provides average classification accuracy, sensitivity, and F1 score of 98.50%, 98.46% and 98.49% respectively.
On the effectiveness of compact biomedical transformers

Abstract Motivation Language models pre-trained on biomedical corpora, such as BioBERT, have recently shown promising results on downstream biomedical tasks. Many existing pre-trained models, on the other hand, are resource-intensive and computationally heavy owing to factors such as embedding size, hidden dimension and number of layers. The natural language processing community has developed numerous strategies to compress these models utilizing techniques such as pruning, quantization and knowledge distillation, resulting in models that are considerably faster, smaller and subsequently easier to use in practice. By the same token, in this article, we introduce six lightweight models, namely, BioDistilBERT, BioTinyBERT, BioMobileBERT, DistilBioBERT, TinyBioBERT and CompactBioBERT which are obtained either by knowledge distillation from a biomedical teacher or continual learning on the Pubmed dataset. We evaluate all of our models on three biomedical tasks and compare them with BioBERT-v1.1 to create the best efficient lightweight models that perform on par with their larger counterparts. Results We trained six different models in total, with the largest model having 65 million in parameters and the smallest having 15 million; a far lower range of parameters compared with BioBERT’s 110M. Based on our experiments on three different biomedical tasks, we found that models distilled from a biomedical teacher and models that have been additionally pre-trained on the PubMed dataset can retain up to 98.8% and 98.6% of the performance of the BioBERT-v1.1, respectively. Overall, our best model below 30 M parameters is BioMobileBERT, while our best models over 30 M parameters are DistilBioBERT and CompactBioBERT, which can keep up to 98.2% and 98.8% of the performance of the BioBERT-v1.1, respectively. Availability and implementation Codes are available at: https://github.com/nlpie-research/Compact-Biomedical-Transformers. Trained models can be accessed at: https://huggingface.co/nlpie.
Optical Flow-Based Algorithm Analysis to Detect Human Emotion from Eye Movement-Image Data

One of the popular methods for the recognition of human emotions such as happiness, sadness and shock is based on the movement of facial features. Motion vectors that show these movements can be calculated by using optical flow algorithms. In this method, for detecting emotions, the resulted set of motion vectors is compared with a standard facial movement template caused by human emotional changes. In this paper, a new method is introduced to compute the quantity of likeness towards a particular emotion to make decisions based on the importance of obtained vectors from an optical flow approach. The current study uses a feature point tracking technique separately applied to the five facial image regions (eyebrows, eyes, and mouth) to identify basic emotions. Primarily, this research will be focusing on eye movement regions. For finding the vectors, one of the efficient optical flow methods is using the pre-experiment as explained further below.
A Message Recovery Attack on LWE/LWR-Based PKE/KEMs Using Amplitude-Modulated EM Emanations

Creating a good deep learning model is an art which requires expertise in deep learning and a large set of labeled data for training neural networks. Neither is readily available. In this paper, we introduce a method that enables us to recover messages of LWE/LWR-based PKE/KEMs using simple multilayer perceptron (MLP) models trained on a small dataset. The core idea is to extend the attack dataset so that at least one of its traces has the ground truth label to which the models are biased towards. We demonstrate the effectiveness of the presented method on the examples of CRYSTALS-Kyber and Saber algorithms implemented in ARM Cortex-M4 CPU on nRF52832 system-on-chip supporting Bluetooth 5.2. We use amplitude-modulated EM emanations which are typically weaker and noisier than power or near-field EM side channels, and thus more difficult to exploit.
Abstract Layer for LeakyReLU for Neural Network Verification Based on Abstract Interpretation

Deep neural networks have been widely used in several complex tasks such as robotics, self-driving cars, medicine, etc. However, they have recently shown to be vulnerable in uncertain environments where inputs are noisy. As a consequence, the robustness of neural networks has become an essential property for their application in critical systems. Robustness is the capacity to take the same decision even when inputs are disturbed under different types of perturbations, including adversarial attacks. The great difficulty today is providing a formal guarantee of robustness, which is the context of this paper. To do so, abstract interpretation, a popular state-of-the-art method, consisting of converting the layers of the neural network into abstract layers, has been recently proposed. An abstract layer can act on a geometric abstract object or shape comprising implicitly an infinite number of inputs rather than an individual input. In this paper, we propose a new mathematical formulation of an abstract transformer to convert a LeakyReLU activation layer to an abstract layer. Moreover, we implement and integrate our transformer into the ERAN tool. For validation, we assess the performance of our transformer according to the LeakyReLU hyperparameter, and we study the robustness of the neural network according to the input perturbation intensity. Our approach is evaluated on three different datasets: MNIST, Fashion and a robotic dataset. The obtained results demonstrate the efficacy of our abstract transformer in terms of mathematical formulation and implementation.
Simultaneous transmission of hyper-entanglement in three degrees of freedom through a multicore fiber

Abstract Entanglement distribution is at the heart of most quantum communication protocols. Inevitable loss of photons along quantum channels is a major obstacle for distributing entangled photons over long distances, as the no-cloning theorem forbids the information to simply be amplified along the way as is done in classical communication. It is therefore desirable for every successfully transmitted photon pair to carry as much entanglement as possible. Spontaneous parametric down-conversion (SPDC) creates photons entangled in multiple high-dimensional degrees of freedom simultaneously, often referred to as hyper-entanglement. In this work, we use a multicore fiber (MCF) to show that energy-time and polarization degrees of freedom can simultaneously be transmitted in multiple fiber cores, even maintaining path entanglement across the cores. We verify a fidelity to the ideal Bell state of at least 95% in all degrees of freedom. Furthermore, because the entangled photons are created with a center wavelength of 1560 nm, our approach can readily be integrated into modern telecommunication infrastructure, thus paving the way for high-rate quantum key distribution and many other entanglement-based quantum communication protocols.
Experimental upstream transmission of continuous variable quantum key distribution access network

Continuous variable quantum key distribution that can be implemented using only low-cost and off-the-shelf components reveals great potential in practical large-scale realization. Access networks, as a modern network necessity, connect many end-users to the network backbone. In this work, we first demonstrate upstream transmission quantum access networks using continuous variable quantum key distribution. A two-end-user quantum access network is then experimentally realized. Through phase compensation, data synchronization, and other technical upgrades, we achieve a secret key rate of the total network of 390 kbits/s. In addition, we extend the case of a two-end-user quantum access network to the case of a multiplicity of users, and analyze the network capacity in the case of a multiplicity of users by measuring the additive excess noise from different time slots.
A Chinese nested named entity recognition approach using sequence labeling

Purpose This study aims to introduce an innovative approach that uses a decoder with multiple layers to accurately identify Chinese nested entities across various nesting depths. To address potential human intervention, an advanced optimization algorithm is used to fine-tune the decoder based on the depth of nested entities present in the data set. With this approach, this study achieves remarkable performance in recognizing Chinese nested entities. Design/methodology/approach This study provides a framework for Chinese nested named entity recognition (NER) based on sequence labeling methods. Similar to existing approaches, the framework uses an advanced pre-training model as the backbone to extract semantic features from the text. Then a decoder comprising multiple conditional random field (CRF) algorithms is used to learn the associations between granularity labels. To minimize the need for manual intervention, the Jaya algorithm is used to optimize the number of CRF layers. Experimental results validate the effectiveness of the proposed approach, demonstrating its superior performance on both Chinese nested NER and flat NER tasks. Findings The experimental findings illustrate that the proposed methodology can achieve a remarkable 4.32% advancement in nested NER performance on the People’s Daily corpus compared to existing models. Originality/value This study explores a Chinese NER methodology based on the sequence labeling ideology for recognizing sophisticated Chinese nested entities with remarkable accuracy.
Technology Enhanced Learning Through Learning Management System and Virtual Reality Googles: A Critical Review

Learning Management System (LMS) is not a new concept, yet it has gained much more focus during the fast technological advancement. Especially the development of virtual reality (VR) and augmented reality (AR), students enhance their educational experiences in LMS. This article also highlights and addresses the LMS facilitated by the further integration of VR, specifically Goggles, that provide a 3D and 4D classroom environment. The researchers adopted an applied descriptive approach to review and discuss the relevant phenomenon. From the epistemological perspective, a current chapter is a pragmatic approach leading researchers to realistic conclusions. Thus, it is found that incorporating VR Goggles in eLearning positively influences the students’ expectations to improve learning experiences. Consequently, they feel motivated, leading to the wider acceptance of the LMS. This article further concludes that VR Goggles in the LMS lead the students to visualize their creativity, attain emphatic experiences, and enjoy arenas offering visualization of abstract concepts, leading to even positive impacts on their critical thinking abilities and boosting their morale in a ritual learning environment. Further, the study limitations are discussed accordingly.
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity

Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts.As most of these spelling errors are caused by phonetic similarity, effectively modeling the pronunciation of Chinese characters is a key factor for CSC.In this paper, we consider introducing an auxiliary task of Chinese pronunciation prediction (CPP) to improve CSC, and, for the first time, systematically discuss the adaptivity and granularity of this auxiliary task.We propose SCOPE which builds on top of a shared encoder two parallel decoders, one for the primary CSC task and the other for a fine-grained auxiliary CPP task, with a novel adaptive weighting scheme to balance the two tasks.In addition, we design a delicate iterative correction strategy for further improvements during inference.Empirical evaluation shows that SCOPE achieves new state-of-theart on three CSC benchmarks, demonstrating the effectiveness and superiority of the auxiliary CPP task.Comprehensive ablation studies further verify the positive effects of adaptivity and granularity of the task.
Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI

Background Deep learning (DL) reconstructions can enhance image quality while decreasing MRI acquisition time. However, DL reconstruction methods combined with compressed sensing for prostate MRI have not been well studied. Purpose To use an industry-developed DL algorithm to reconstruct low-resolution T2-weighted turbo spin-echo (TSE) prostate MRI scans and compare these with standard sequences. Materials and Methods In this prospective study, participants with suspected prostate cancer underwent prostate MRI with a Cartesian standard-resolution T2-weighted TSE sequence (T2C) and non-Cartesian standard-resolution T2-weighted TSE sequence (T2NC) between August and November 2022. Additionally, a low-resolution Cartesian DL-reconstructed T2-weighted TSE sequence (T2DL) with compressed sensing DL denoising and resolution upscaling reconstruction was acquired. Image sharpness was assessed qualitatively by two readers using a five-point Likert scale (from 1 = nondiagnostic to 5 = excellent) and quantitatively by calculating edge rise distance. The Friedman test and one-way analysis of variance with post hoc Bonferroni and Tukey tests, respectively, were used for group comparisons. Prostate Imaging Reporting and Data System (PI-RADS) score agreement between sequences was compared by using Cohen κ. Results This study included 109 male participants (mean age, 68 years ± 8 [SD]). Acquisition time of T2DL was 36% and 29% lower compared with that of T2C and T2NC (mean duration, 164 seconds ± 20 vs 257 seconds ± 32 and 230 seconds ± 28; P < .001 for both). T2DL showed improved image sharpness compared with standard sequences using both qualitative (median score, 5 [IQR, 4–5] vs 4 [IQR, 3–4] for T2C and 4 [IQR, 3–4] for T2NC; P < .001 for both) and quantitative (mean edge rise distance, 0.75 mm ± 0.39 vs 1.15 mm ± 0.68 for T2C and 0.98 mm ± 0.65 for T2NC; P < .001 and P = .01) methods. PI-RADS score agreement between T2NC and T2DL was excellent (κ range, 0.92–0.94 [95% CI: 0.87, 0.98]). Conclusion DL reconstruction of low-resolution T2-weighted TSE sequences enabled accelerated acquisition times and improved image quality compared with standard acquisitions while showing excellent agreement with conventional sequences for PI-RADS ratings. Clinical trial registration no. NCT05820113 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Turkbey in this issue.
Going Incognito in the Metaverse: Achieving Theoretically Optimal Privacy-Usability Tradeoffs in VR

Virtual reality (VR) telepresence applications and the so-called "metaverse" promise to be the next major medium of human-computer interaction. However, with recent studies demonstrating the ease at which VR users can be profiled and deanonymized, metaverse platforms carry many of the privacy risks of the conventional internet (and more) while at present offering few of the defensive utilities that users are accustomed to having access to. To remedy this, we present the first known method of implementing an "incognito mode" for VR. Our technique leverages local ε-differential privacy to quantifiably obscure sensitive user data attributes, with a focus on intelligently adding noise when and where it is needed most to maximize privacy while minimizing usability impact. Our system is capable of flexibly adapting to the unique needs of each VR application to further optimize this trade-off. We implement our solution as a universal Unity (C#) plugin that we then evaluate using several popular VR applications. Upon faithfully replicating the most well-known VR privacy attack studies, we show a significant degradation of attacker capabilities when using our solution.
Realizing a deep reinforcement learning agent for real-time quantum feedback

Realizing the full potential of quantum technologies requires precise real-time control on time scales much shorter than the coherence time. Model-free reinforcement learning promises to discover efficient feedback strategies from scratch without relying on a description of the quantum system. However, developing and training a reinforcement learning agent able to operate in real-time using feedback has been an open challenge. Here, we have implemented such an agent for a single qubit as a sub-microsecond-latency neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit and train the agent based solely on measurements. Our work is a first step towards adoption of reinforcement learning for the control of quantum devices and more generally any physical device requiring low-latency feedback.
Universal Cost Bound of Quantum Error Mitigation Based on Quantum Estimation Theory

We present a unified approach to analyzing the cost of various quantum error mitigation methods on the basis of quantum estimation theory. By analyzing the quantum Fisher information matrix of a virtual quantum circuit that effectively represents the operations of quantum error mitigation methods, we derive for a generic layered quantum circuit under a wide class of Markovian noise that, unbiased estimation of an observable encounters an exponential growth with the circuit depth in the lower bound on the measurement cost. Under the global depolarizing noise, we in particular find that the bound can be asymptotically saturated by merely rescaling the measurement results. Moreover, we prove for random circuits with local noise that the cost grows exponentially also with the qubit count. Our numerical simulations support the observation that, even if the circuit has only linear connectivity, such as the brick-wall structure, each noise channel converges to the global depolarizing channel with its strength growing exponentially with the qubit count. This not only implies the exponential growth of cost both with the depth and qubit count, but also validates the rescaling technique for sufficiently deep quantum circuits. Our results contribute to the understanding of the physical limitations of quantum error mitigation and offer a new criterion for evaluating the performance of quantum error mitigation techniques.