Reviewerly/exHarmony huggingface dataset:
CPSSDS: Conformal prediction for semi-supervised classification on data streams

In this study, we focus on semi-supervised data stream classification tasks. With the advent of applications that generate vast streams of data, data stream mining algorithms are required to handle the intrinsic issues of learning patterns from this type of non-stationary data. The main challenges are fast arriving, infinite length, limited labeled data, concept drift, and concept evolution. Although many algorithms have been used to overcome these issues, there are still many challenges that need to be addressed and discussed in practical domains. This paper mainly focuses on the limited labeled data and concept drift detection issues. Therefore, a semi-supervised framework called CPSSDS is proposed, which uses an incremental classifier as a base learner and a self-training framework to handle the scarcity of labeled samples. The proposed approach utilizes a form of Conformal Predictors to discover a set of informative unlabeled data instances to add to the original training set in each training procedure which is the main challenge in the standard self-training approach. In terms of this framework, the Kolmogorov-Smirnov test is adopted to detect concept drift by comparing the outputs of conformal prediction for two sequences of data chunks. Finally, experimental results on several synthetic and real datasets demonstrate that the proposed learning framework is comparable to the state-of-the-art semi-supervised algorithms for data streams. The implementation source code of the CPSSDS is available at https://github.com/neginsmd/CPSSDS.git.
Photon-Limited Object Detection using Non-local Feature Matching and Knowledge Distillation

Robust object detection under photon-limited conditions is crucial for applications such as night vision, surveillance, and microscopy, where the number of photons per pixel is low due to a dark environment and/or a short integration time. While the mainstream "low-light" image enhancement methods have produced promising results that improve the image contrast between the foreground and background through advanced coloring techniques, the more challenging problem of mitigating the photon shot noise inherited from the random Poisson process remains open. In this paper, we present a photon-limited object detection framework by adding two ideas to state-of-the-art object detectors: 1) a space-time non-local module that leverages the spatial-temporal information across an image sequence in the feature space, and 2) knowledge distillation in the form of student-teacher learning to improve the robustness of the detector’s feature extractor against noise. Experiments are conducted to demonstrate the improved performance of the proposed method in comparison with state-of-the-art baselines. When integrated with the latest photon counting devices, the algorithm achieves more than 50% mean average precision at a photon level of 1 photon per pixel.
Non-rational Narain CFTs from codes over F4

We construct a map between a class of codes over $F_4$ and a family of non-rational Narain CFTs. This construction is complementary to a recently introduced relation between quantum stabilizer codes and a class of rational Narain theories. From the modular bootstrap point of view we formulate a polynomial ansatz for the partition function which reduces modular invariance to a handful of algebraic easy-to-solve constraints. For certain small values of central charge our construction yields optimal theories, i.e. those with the largest value of the spectral gap.
Attention Mechanism for Neural Machine Translation: A survey

Machine translation, one of the most basic challenges in natural language processing, aims to automated translate between human language. Neural machine translation has emerged as a very successful paradigm which can learn features directly from data and has led to remarkable breakthrough in the field of machine translation. But widely adopt encoder-decoder model architecture in neural machine translation has been faced effectiveness issue and training issue. Currently, almost all state-of-the-art neural machine translation models are based on attention mechanism. Attention models help relate input sequence units disregarding distance between them in space and time, at the same time make sequence data processing more parallelizable. Therefore, the introduction of attention greatly improves the performance of neural machine translation model. Given this period of rapid development, the goal of this paper is to provide a comprehensive summary of the state-of-art attention model. Most widely adopt attention models and their invariants are covering in this paper, including but not limited to self-attention, soft attention, hard attention, local attention, global attention, additive attention, multiplicative attention, key-value attention and their realization in various sequence-to-sequence models. I finish the survey by identifying promising directions for future research.
FALCON Down: Breaking FALCON Post-Quantum Signature Scheme through Side-Channel Attacks

This paper proposes the first side-channel attack on FALCON—a NIST Round-3 finalist for the post-quantum digital signature standard. We demonstrate a known-plaintext attack that uses the electromagnetic measurements of the device to extract the secret signing keys, which then can be used to forge signatures on arbitrary messages. The proposed attack targets the unique floating-point multiplications within FALCON’s Fast Fourier Transform through a novel extend-and-prune strategy that extracts the sign, mantissa, and exponent variables without false positives. The extracted floating-point values are then mapped back to the secret key’s coefficients. Our attack, notably, does not require pre-characterizing the power profile of the target device or crafting special inputs. Instead, the statistical differences on obtained traces are sufficient to successfully execute our proposed differential electromagnetic analysis. The results on an ARM-Cortex-M4 running the FALCON NIST’s reference software show that approximately 10k measurements are sufficient to extract the entire key.
Advances in Chip‐Scale Quantum Photonic Technologies

Abstract Quantum photonic system has made remarkable achievements in computing and communication. Serving as quantum information carriers, photons are flying qubits and robust against decoherence, emerging as a desirable platform to make quantum processor a reality. However, with system's complexity and functionality scaling up, the requirements for stability, programmability, and manufacturability will be in high demand. Integrated photonics, compatible with complementary metal‐oxide‐semiconductor fabrication, has overwhelming dominance in terms of density and performance, making it an unrivaled contender for large‐scale quantum information processing (QIP). To improve the performance of individual blocks and integrate them on a common substrate is one of the core tasks for a practical quantum processor. Here, the recent advances in components that constitute quantum photonic systems on silicon photonics platform, including sources, modulators, and detectors are reviewed. Burgeoning quantum photonics applications, such as multi‐dimensional, multi‐photon QIP and integrated quantum key distribution are highlighted.
Simplifying approach to node classification in Graph Neural Networks

Graph Neural Networks (GNNs) have become one of the indispensable tools to learn from graph-structured data, and their usefulness has been shown in wide variety of tasks. In recent years, there have been tremendous improvements in architecture design, resulting in better performance on various prediction tasks. In general, these neural architectures combine node feature aggregation and feature transformation using learnable weight matrix in the same layer. This makes it challenging to analyze the importance of node features aggregated from various hops and the expressiveness of the neural network layers. As different graph datasets show varying levels of homophily and heterophily in features and class label distribution, it becomes essential to understand which features are important for the prediction tasks without any prior information. In this work, we decouple the node feature aggregation step and depth of graph neural network, and empirically analyze how different aggregated features play a role in prediction performance. We show that not all features generated via aggregation steps are useful, and often using these less informative features can be detrimental to the performance of the GNN model. Through our experiments, we show that learning certain subsets of these features can lead to better performance on wide variety of datasets. Based on our observations, we introduce several key design strategies for graph neural networks. More specifically, we propose to use softmax as a regularizer and ”soft-selector” of features aggregated from neighbors at different hop distances; and L2-Normalization over GNN layers. Combining these techniques, we present a simple and shallow model, Feature Selection Graph Neural Network (FSGNN), and show empirically that the proposed model achieves comparable or even higher accuracy than state-of-the-art GNN models in nine benchmark datasets for the node classification task, with remarkable improvements up to 51.1%. Source code available at https://github.com/sunilkmaurya/FSGNN/.
Measurement of gear tooth profiles using incoherent line structured light

Optical measurement methods are being increasingly used in the profile measurement field, but optical high-precision measurements of gear tooth profiles have not been achieved. Therefore, a high-precision profile measurement method that uses incoherent line structured light was proposed and a corresponding measurement system was developed in this paper. To calibrate the accuracy of this system, profile measurement experiments using roughness comparison specimens were conducted. Finally, the tooth profile measurement experiments using an involute master were performed. The results indicated that the incoherent line structured light method was less affected by speckle noise and had higher measurement resolution than the line laser method. The tooth profile error for the involute master measured by the incoherent line structured light method was ±2.2 μm. This study provides a new method for the industrial application of optical high-precision measurement of gear tooth profiles.
Broadband Continuous-Variable Entanglement Generation Using a Kerr-Free Josephson Metamaterial

Generation of quantum resources like entanglement is pivotal for quantum technology. This work uses superconducting Josephson metamaterials to generate color-entangled photons from vacuum fluctuations at a high rate, showing the presented system to be a superb microwave entanglement generator with remarkable bandwidth. Successful squeezing of the light is also demonstrated. These results are quite promising for quantum computing, sensing, and communication with superconducting devices.
Multi-Party Functional Encryption

We initiate the study of multi-party functional encryption (\(\mathsf {MPFE})\) which unifies and abstracts out various notions of functional encryption which support distributed ciphertexts or secret keys, such as multi-input FE, multi-client FE, decentralized multi-client FE, multi-authority FE, dynamic decentralized FE, adhoc multi-input FE and such others. Using our framework, we identify several gaps in the literature and provide some constructions to fill these:1. Multi-Authority \({\mathsf {ABE}}\) with Inner Product Computation. The recent work of Abdalla et al. (ASIACRYPT’20) constructed a novel “composition” of Attribute Based Encryption (\({\mathsf {ABE}}\)) and Inner Product Functional Encryption (\({\mathsf { IPFE}}\)), namely functional encryption schemes that combine the access control functionality of attribute based encryption with the possibility of performing linear operations on the encrypted data. In this work, we extend the access control component to support the much more challenging multi-authority setting, i.e. “lift” the primitive of \({\mathsf {ABE}}\) in their construction to multi-authority \({\mathsf {ABE}}\) for the same class of access control policies (LSSS structures). This yields the first construction of a nontrivial multi-authority \(\mathsf {FE}\) beyond \({\mathsf {ABE}}\) from simple assumptions on pairings to the best of our knowledge.Our techniques can also be used to generalize the decentralized attribute based encryption scheme of Michalevsky and Joye (ESORICS’18) to support inner product computation on the message. While this scheme only supports inner product predicates which is less general than those supported by the Lewko-Waters (EUROCRYPT’11) construction, it supports policy hiding which the latter does not. Our extension inherits these features and is secure based on the k-linear assumption, in the random oracle model.2. Function Hiding \(\mathsf {DDFE} \). The novel primitive of dynamic decentralized functional encryption (\(\mathsf {DDFE} \)) was recently introduced by Chotard et al. (CRYPTO’20), where they also provided the first construction for inner products. However, the primitive of \(\mathsf {DDFE} \) does not support function hiding, which is a significant limitation for several applications. In this work, we provide a new construction for inner product \(\mathsf {DDFE} \) which supports function hiding. To achieve our final result, we define and construct the first function hiding multi-client functional encryption (\(\mathsf{MCFE}\)) scheme for inner products, which may be of independent interest.3. Distributed Ciphertext-Policy \({\mathsf {ABE}}\). We provide a distributed variant of the recent ciphertext-policy attribute based encryption scheme, constructed by Agrawal and Yamada (EUROCRYPT’20). Our construction supports \(\mathbf{NC}^1\) access policies, and is secure based on “Learning With Errors” and relies on the generic bilinear group model as well as the random oracle model.Our new \(\mathsf {MPFE} \) abstraction predicts meaningful new variants of functional encryption as useful targets for future work.
A Mini Review of Presence and Immersion in Virtual Reality

Virtual reality technology is constantly improving such that a virtual environment is more like a physical one. However, some research evidence suggest that certain virtual reality scenarios are less real than others to human observers (e.g., experience of falling from a high place) leading to potential limitations of using virtual reality as a research tool for certain tasks. Moreover, since the inception of VR research the terms presence and immersion have been somewhat convoluted and at times, even used interchangeably. Using a thematic content analysis based on seventeen articles, a theme for each term emerged. Presence is an experiential quality in virtual environments and immersion is associated with the technical aspects of a virtual system that aide the user in feeling a sense of presence. Several new technologies, as well as more traditional approaches are discussed as potential methods to improve of immersion, and therefore presence, in virtual reality.
A dataset and benchmark for malaria life-cycle classification in thin blood smear images

Malaria microscopy, microscopic examination of stained blood slides to detect parasite Plasmodium, is considered to be a gold standard for detecting life-threatening disease malaria. Detecting the plasmodium parasite requires a skilled examiner and may take up to 10 to 15 minutes to completely go through the whole slide. Due to a lack of skilled medical professionals in the underdeveloped or resource-deficient regions, many cases go misdiagnosed, which results in unavoidable medical complications. We propose to complement the medical professionals by creating a deep learning-based method to automatically detect (localize) the plasmodium parasites in the photograph of stained film. To handle the unbalanced nature of the dataset, we adopt a two-stage approach. Where the first stage is trained to classify cells into just healthy or infected. The second stage is trained to classify each detected cell further into the malaria life-cycle stage. To facilitate the research in machine learning-based malaria microscopy, we introduce a new large-scale microscopic image malaria dataset. Thirty-eight thousand cells are tagged from the 345 microscopic images of different Giemsa-stained slides of blood samples. Extensive experimentation is performed using different Convolutional Neural Networks on this dataset. Our experiments and analysis reveal that the two-stage approach works better than the one-stage approach for malaria detection. To ensure the usability of our approach, we have also developed a mobile app that will be used by local hospitals for investigation and educational purposes. The dataset, its annotations, and implementation codes will be released upon publication of the paper.
CASIDE: A data model for interoperable cancer survivorship information based on FHIR

Cancer survivorship has traditionally received little research attention although it is associated with a variety of long-term consequences and also many other comorbidities. There is an urgent need to increase research on this area, and the secondary use of healthcare data has the potential to provide valuable insights on survivors' health trajectories. However, cancer survivors' data is often stored in silos and collected inconsistently. In this study we present CASIDE, an interoperable data model for cancer survivorship information that aims to accelerate the secondary use of healthcare data and data sharing across institutions. It is designed to provide a holistic view of the cancer survivor, taking into account not just the clinical data but also the patient's own perspective, and is built upon the emerging Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) standard. Advantages of adopting FHIR and challenges in information modelling using this standard are discussed. CASIDE is a generalizable approach that is already being used as a support tool for the development of downstream applications to support clinical decision making and can contribute to translational collaborative research on cancer survivorship.
Geochemical gene: A promising concept in discrimination and traceability of geological materials

Geochemical gene is a new technique presented recently and its history is described and classified into three stages up to the present. To construct a geochemical gene, five steps are suggested as selection of elements, determination of reference values, spectral line and codes, calculation of similarity, and sequence of elements. The essence of the geochemical gene is similar to the quantitative description of the spider diagram or REE pattern regardless of the compositional closure effect. According to the construction steps, 7 geochemical genes have been constructed by now as the lithogenes of LG01, LG02, and LG03, the metallogenes of MGAu and MGW, and the rare earth elemental genes of REEG01 and REEG02. Lots of weathering profiles developed on different lithological rocks in China have been studied to test the gene properties of heredity and inheritance and the results indicate that the geochemical genes can keep steady during rocks' weathering according to the similar gene criterion of ≥80% on gene similarity. Moreover, the LG01 and LG03 are more stable than LG02, the LG03 may be steadier than LG01, and the REEG01 is more stable than REEG02 during rocks' weathering. The gene similarity between a sample and the ideal acidic rock is called the sample's acidic similarity which can be used as a tool to classify samples and discriminate their provenances compositionally on the LG01 and LG03 lithogenes. The gene similarity between a sample and the ideal ore is called the sample's mineralization similarity which can be viewed as an integrated index to determine a sample with or without mineralization on the field of geochemical exploration. Based on the geochemical gene database constructed from the regional geochemical survey data, the gene similarity can be used to trace a geological material where it is from. To code geochemical genes and calculate gene similarities between samples, a software named as GGC (Geochemical Gene Coding) is developed non-commercially.
A rough cut cybersecurity investment using portfolio of security controls with maximum cybersecurity value

This paper deals with optimisation of cybersecurity investment in supply chains using stochastic programming approach. A classical exponential function of breach probability and the intuitive idea of ‘the expected net benefits’, originally presented in 2002 by Gordon and Loeb, were applied to introduce the concept of cybersecurity value. The cybersecurity value of security control is defined as the value gained by implementing a single control to secure a subset of components. The cybersecurity value of a control can be seen as a measure of its efficiency in reducing vulnerability of a secured system or component. A mixed binary optimisation problem, next transformed into an unconstrained binary program is developed to maximise total cybersecurity value of control portfolio. The optimal solution to the binary program provides a simple formula to immediately obtain the portfolio of security controls with maximum total cybersecurity value and determine a rough cut cybersecurity investment. This study also shows that portfolio of security controls with maximum total cybersecurity value reduces the losses from security breaches and mitigate the impact of cyber risk.