Reviewerly/exHarmony huggingface dataset:
SIDH Proof of Knowledge

We show that the soundness proof for the De Feo–Jao–Plût identification scheme (the basis for supersingular isogeny Diffie–Hellman (SIDH) signatures) contains an invalid assumption, and we provide a counterexample for this assumption—thus showing the proof of soundness is invalid. As this proof was repeated in a number of works by various authors, multiple pieces of literature are affected by this result. Due to the importance of being able to prove knowledge of an SIDH key (for example, to prevent adaptive attacks), soundness is a vital property. Surprisingly, the problem of proving knowledge of a specific isogeny turns out to be considerably more difficult than was perhaps anticipated. The main results of this paper are a sigma protocol to prove knowledge of a walk of specified length in a supersingular isogeny graph, and a second one to additionally prove that the isogeny maps some torsion points to some other torsion points (as seen in SIDH public keys). Our scheme also avoids the SIDH identification scheme soundness issue raised by Ghantous, Pintore and Veroni. In particular, our protocol provides a non-interactive way of verifying correctness of SIDH public keys, and related statements, as protection against adaptive attacks. Post-scriptum: Some months after this work was completed and made public, the SIDH assumption was broken in a series of papers by several authors. Hence, in the standard SIDH setting, some of the statements studied here now have trivial polynomial time non-interactive proofs. Nevertheless our first sigma protocol is unaffected by the attacks, and our second protocol may still be useful in present and future variants of SIDH that escape the attacks.
Visiting museums via augmented reality: an experience fast-tracking the digital transformation of the tourism industry

Purpose Augmented reality (AR) is an emergent technology that has been impacting and beneficial to various industries. This is especially the case in the tourism industry since the COVID-19 crisis. To enable and enhance the museum experience, AR technology can be used as a tool to manage visitors' experiences and adapt to the digital transformation of industries. This research work aims to explore the acceptance of museum visitors for the adoption of this technology. For this purpose, the technology acceptance model (TAM) is used as the research lens. Design/methodology/approach The authors collected 316 responses from a survey handed out to prior visitors of immersive exhibitions shown via AR at the Atelier des Lumières in Paris and analyzed them through a structural equation model approach. Findings The study’s findings explain the behavior of visitors when attending immersive exhibitions according to the TAM and highlight two new constructs that need to be considered when using the TAM with AR technology: user satisfaction and social mimetism. Originality/value The study’s findings support that implementing AR technology in the museum industry provides great opportunities for the tourism industry to recover from the economic crisis that followed from the COVID-19 health crisis. The authors identified new thriving concepts in the 21st century with the rise of social media, such as social mimetism, that propels visitors to have a positive attitude and intention to attend immersive exhibitions.
Phase-Shifting Temporal Phase Unwrapping Algorithm for High-Speed Fringe Projection Profilometry

Fringe projection profilometry based on temporal phase unwrapping (TPU) without error propagation has been widely used to measure the three-dimensional (3-D) shape of the complex scene. This paper develops a phase-shifting temporal unwrapping (PS-TPU) algorithm to ensure the measuring robustness and speed at the same time. In this method, a series of phase shifts are set as the cue of fringe order, and then embedded into the different periods of the single quasi-cosine pattern. High-quality wrapped phase and coded phase obtained from three conventional phase-shifting patterns and the single coded pattern can be utilized to generate fringe order pixel-by-pixel using the parallel and symmetrical relationship. Compared with the classical TPU algorithms, the number of projecting extra patterns is minimized to 1, while guaranteeing high noise immunity and less computational complexity. The static and dynamic experiments verify that the proposed PS-TPU is powerful for reconstructing 3-D shapes in our daily life.
Time-Efficient Finite Field Microarchitecture Design for Curve448 and Ed448 on Cortex-M4

The elliptic curve family of schemes has the lowest computational latency, memory use, energy consumption, and bandwidth requirements, making it the most preferred public key method for adoption into network protocols. Being suitable for embedded devices and applicable for key exchange and authentication, ECC is assuming a prominent position in the field of IoT cryptography. The attractive properties of the relatively new curve Curve448 contribute to its inclusion in the TLS1.3 protocol and pique the interest of academics and engineers aiming at studying and optimizing the schemes. When addressing low-end IoT devices, however, the literature indicates little work on these curves. In this paper, we present an efficient design for both protocols based on Montgomery curve Curve448 and its birationally equivalent Edwards curve Ed448 used for key agreement and digital signature algorithm, specifically the X448 function and the Ed448 DSA, relying on efficient low-level arithmetic operations targeting the ARM-based Cortex-M4 platform. Our design performs point multiplication, the base of the Elliptic Curve Diffie-Hellman (ECDH), in 3,2KCCs, resulting in more than 48% improvement compared to the best previous work based on Curve448, and performs sign and verify, the main operations of the Edwards-curves Digital Signature Algorithm (EdDSA), in 6,038KCCs and 7,404KCCs, showing a speedup of around $$11\%$$ compared to the counterparts. We present novel modular multiplication and squaring architectures reaching $$\sim $$ 25% and $$\sim $$ 35% faster runtime than the previous best-reported results, respectively, based on Curve448 key exchange counterparts, and $$\sim $$ 13% and $$\sim $$ 25% better latency results than the Ed448-based digital signature counterparts targeting Cortex-M4 platform.
Deep Reinforcement Learning

Similar to humans, RL agents use interactive learning to successfully obtain satisfactory decision strategies. In many examples, it is desirable to learn directly from measurements of raw video or image data without any hand-engineered features or domain heuristics. This kind of task requires the so-called deep reinforcement learning (DRL), which is an indepth combination of artificial neural network (ANN) and reinforcement learning (RL). The “deep” portion of DRL refers to the multiple layers of neural network that replicate the structure of a human brain. Its application often requires large amounts of interactive data and significant computing power. In recent years, computing power has dramatically increased, which has enabled successful DRL applications. Many researchers became aware of the importance of DRL after a series of well-publicized defeats of Go grandmasters by DeepMind’s AlphaGo. In addition to successfully playing Go, DRL has achieved human-level performance in several competitive video games like Atari and Starcraft.
A comprehensive framework towards segmenting and classifying breast cancer patients using deep learning and Aquila optimizer

Abstract Breast cancer is among the major frequent types of cancer worldwide, causing a significant death rate every year. It is the second most prevalent malignancy in Egypt. With the increasing number of new cases, it is vital to diagnose breast cancer in its early phases to avoid serious complications and deaths. Therefore, routine screening is important. With the current evolution of deep learning, medical imaging became one of the interesting fields. The purpose of the current work is to suggest a hybrid framework for both the classification and segmentation of breast scans. The framework consists of two phases, namely the classification phase and the segmentation phase. In the classification phase, five different CNN architectures via transfer learning, namely MobileNet, MobileNetV2, NasNetMobile, VGG16, and VGG19, are applied. Aquila optimizer is used for the calculation of the optimal hyperparameters of the different TL architectures. Four different datasets representing four different modalities (i.e., MRI, Mammographic, Ultrasound images, and Histopathology slides) are used for training purposes. The framework can perform both binary- and multi-class classification. In the segmentation phase, five different structures, namely U-Net, Swin U-Net, Attention U-Net, U-Net++, and V-Net, are applied to identify the region of interest in the ultrasound breast images. The reported results prove the efficiency of the suggested framework against current state-of-the-art studies.
Real-Time Object Detection

Object detection is a pivot and prime process in various applications and procedures such as surveillance, classification, recognition and prediction including image retrieval, computer visioning, video streams and many more.Real-time object detection requires identification of different kinds of objects specified in images, videos or live feed streams.It is basic and important to maintain the level of accuracy along with quick inference.This paper proposes an algorithm to perform the real-time object detection typically leverage machine learning, deep learning to produce effective results.The goal is to power machines to identify defined objects in a live feed, videos or images and achieve desired outcomes.The algorithm used is YOLO version3 (YOLOv3).It presents a fast and accurate object detection method with higher performance.To create reliable applications for resolving practical problems, computer vision techniques like tracking and counting are combined.It is the improved proposal to many machine learning algorithms like CNN, RNN, YOLO v1 and v2.Some of the applications are traffic surveillance, vehicle detection, face detection and recognition, number-plate detection, overspeed tracking and more.The factors included are segmentation, accuracy, precision, fastness, performance and efficiency.It is useful to meet the needs of growing technology.
Waste Classification from Digital Images Using ConvNeXt

In this paper, ConvNeXt is selected as a model for waste classification from digital images. ConvNeXt is a CNN-based backbone network that has been proposed to further improve the performance of models for visual tasks, following the various types of research work that have been generated based on Transformer. In this paper, we take ConvNeXt as the backbone to obtain an efficient waste classification model. In our experiments, we categorized waste into four classes based on predefined classification criteria. We have collected 1,660 labeled images for model training. By using ConvNeXt, we observed that the best experimental result in this paper was from ConvNeXt, which has achieved an accuracy 79.88% in the waste classification. In order to evaluate the model, we consider AP and mAP for waste classification. Our experimental results show that using Mask R-CNN network with ConvNeXt as the backbone outperforms the existing methods for waste classification.
Transport of Multispecies Ion Crystals through a Junction in a Radio-Frequency Paul Trap

We report on the first demonstration of transport of a multispecies ion crystal through a junction in a rf Paul trap. The trap is a two-dimensional surface-electrode trap with an X junction and segmented control electrodes to which time-varying voltages are applied to control the shape and position of potential wells above the trap surface. We transport either a single ^{171}Yb^{+} ion or a crystal composed of a ^{138}Ba^{+} ion cotrapped with the ^{171}Yb^{+} ion to any port of the junction. We characterize the motional excitation by performing multiple round-trips through the junction and back to the initial well position without cooling. The final excitation is then measured using sideband asymmetry. For a single ^{171}Yb^{+} ion, transport with a 4 m/s average speed induces between 0.013±0.001 and 0.014±0.001 quanta of excitation per round-trip, depending on the exit port. For a Ba-Yb crystal, transport at the same speed induces between 0.013±0.001 and 0.030±0.002 quanta per round-trip of excitation to the in-phase axial mode. Excitation in the out-of-phase axial mode ranges from 0.005±0.001 to 0.021±0.001 quanta per round-trip.
An overview on Meta-learning approaches for Few-shot Weakly-supervised Segmentation

Semantic segmentation is a difficult task in computer vision that have applications in many scenarios, often as a preprocessing step for a tool. Current solutions are based on Deep Neural Networks, which often require a large amount of data for learning a task. Aiming to alleviate the strenuous data-collecting/annotating labor, research fields have emerged in recent years. One of them is Meta-Learning, which tries to improve the generability of models to learn in a restricted amount of data. In this work, we extend a previous paper conducting a more extensive overview of the still under-explored problem of Few-Shot Weakly-supervised Semantic Segmentation. We refined the previous taxonomy and included the review of additional methods, including Few-Shot Segmentation methods that could be adapted to the weak supervision. The goal is to provide a simple organization of literature and highlight aspects observed in the current moment, and be a starting point to foment research on this problem with applications in areas like medical imaging, remote sensing, video segmentation, and others.
Robust Temporal Smoothness in Multi-Task Learning

Multi-task learning models based on temporal smoothness assumption, in which each time point of a sequence of time points concerns a task of prediction, assume the adjacent tasks are similar to each other. However, the effect of outliers is not taken into account. In this paper, we show that even only one outlier task will destroy the performance of the entire model. To solve this problem, we propose two Robust Temporal Smoothness (RoTS) frameworks. Compared with the existing models based on temporal relation, our methods not only chase the temporal smoothness information but identify outlier tasks, however, without increasing the computational complexity. Detailed theoretical analyses are presented to evaluate the performance of our methods. Experimental results on synthetic and real-life datasets demonstrate the effectiveness of our frameworks. We also discuss several potential specific applications and extensions of our RoTS frameworks.
Joint Multimodal Entity-Relation Extraction Based on Edge-Enhanced Graph Alignment Network and Word-Pair Relation Tagging

Multimodal named entity recognition (MNER) and multimodal relation extraction (MRE) are two fundamental subtasks in the multimodal knowledge graph construction task. However, the existing methods usually handle two tasks independently, which ignores the bidirectional interaction between them. This paper is the first to propose jointly performing MNER and MRE as a joint multimodal entity-relation extraction (JMERE) task . Besides, the current MNER and MRE models only consider aligning the visual objects with textual entities in visual and textual graphs but ignore the entity-entity relationships and object-object relationships. To address the above challenges, we propose an edge-enhanced graph alignment network and a word-pair relation tagging (EEGA) for the JMERE task. Specifically, we first design a word-pair relation tagging to exploit the bidirectional interaction between MNER and MRE and avoid error propagation. Then, we propose an edge-enhanced graph alignment network to enhance the JMERE task by aligning nodes and edges in the cross-graph. Compared with previous methods, the proposed method can leverage the edge information to auxiliary alignment between objects and entities and find the correlations between entity-entity relationships and object-object relationships. Experiments are conducted to show the effectiveness of our model.
Cost analysis of archives in the pathology laboratories: from safety to management

Context Despite the reluctance to invest and the challenging estimation of necessary supporting costs, optimising the archives seems to be one of the hottest topics in the future management of the pathology laboratories. Historically, archives were only partially designed to securely store and organise tissue specimens, and tracking systems were often flawed, posing significant risks to patients’ health and legal ramifications for pathologists. Objective The current review explores the available data from the literature on archives’ management in pathology, including comprehensive business plans, structure setup, outfit, inventories, ongoing conservation and functional charges. Data sources Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about the archiving process and storage were included and analysed to extract information. Prepublication servers were ignored. Italian Institutional Regional databases for public competitive bidding processes were queried too. Conclusions A new emergent feeling in the pathology laboratory is growing for archives management; the digital pathology era is a great opportunity to apply innovation to tracking systems and samples preservation. The main aim is a critical evaluation of the return of investment in developing automatic and tracked archiving processes for improving not only quality, efficacy and efficiency of the labs but also patients’ healthcare.
Iteratively Prompt Pre-trained Language Models for Chain of Thought

While Pre-trained Language Models (PLMs) internalize a great amount of world knowledge, they have been shown incapable of recalling these knowledge to solve tasks requiring complex & multi-step reasoning.Similar to how humans develop a "chain of thought" for these tasks, how can we equip PLMs with such abilities?In this work, we explore an iterative prompting framework, a new prompting paradigm which progressively elicits relevant knowledge from PLMs for multi-step inference.We identify key limitations of existing prompting methods, namely they are either restricted to queries with a single identifiable relation/predicate, or being agnostic to input contexts, which makes it difficult to capture variabilities across different inference steps.We propose an iterative context-aware prompter, which addresses these limitations by learning to dynamically synthesize prompts conditioned on the current step's contexts.Experiments on three datasets involving multi-step reasoning show the effectiveness of the iterative scheme and the context-aware prompter design. 1
Improving Passage Retrieval with Zero-Shot Question Generation

We propose a simple and effective re-ranking method for improving passage retrieval in open question answering.The re-ranker re-scores retrieved passages with a zero-shot question generation model, which uses a pre-trained language model to compute the probability of the input question conditioned on a retrieved passage.This approach can be applied on top of any retrieval method (e.g.neural or keywordbased), does not require any domain-or taskspecific training (and therefore is expected to generalize better to data distribution shifts), and provides rich cross-attention between query and passage (i.e. it must explain every token in the question).When evaluated on a number of open-domain retrieval datasets, our re-ranker improves strong unsupervised retrieval models by 6%-18% absolute and strong supervised models by up to 12% in terms of top-20 passage retrieval accuracy.We also obtain new stateof-the-art results on full open-domain question answering by simply adding the new re-ranker to existing models with no further changes. 1
Revisiting DocRED - Addressing the False Negative Problem in Relation Extraction

The DocRED dataset is one of the most popular and widely used benchmarks for documentlevel relation extraction (RE).It adopts a recommend-revise annotation scheme so as to have a large-scale annotated dataset.However, we find that the annotation of DocRED is incomplete, i.e., false negative samples are prevalent.We analyze the causes and effects of the overwhelming false negative problem in the DocRED dataset.To address the shortcoming, we re-annotate 4,053 documents in the DocRED dataset by adding the missed relation triples back to the original DocRED.We name our revised DocRED dataset Re-DocRED.We conduct extensive experiments with state-ofthe-art neural models on both datasets, and the experimental results show that the models trained and evaluated on our Re-DocRED achieve performance improvements of around 13 F1 points.Moreover, we conduct a comprehensive analysis to identify the potential areas for further improvement. 1
Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks

The wide adoption and application of Masked language models (MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities.Prior attempts at measuring leakage of MLMs via membership inference attacks have been inconclusive, implying potential robustness of MLMs to privacy attacks.In this work, we posit that prior attempts were inconclusive because they based their attack solely on the MLM's model score.We devise a stronger membership inference attack based on likelihood ratio hypothesis testing that involves an additional reference MLM to more accurately quantify the privacy risks of memorization in MLMs.We show that masked language models are indeed susceptible to likelihood ratio membership inference attacks: Our empirical results, on models trained on medical notes, show that our attack improves the AUC of prior membership inference attacks from 0.66 to an alarmingly high 0.90 level.
ClidSum: A Benchmark Dataset for Cross-Lingual Dialogue Summarization

We present CLIDSUM, a benchmark dataset towards building cross-lingual summarization systems on dialogue documents.It consists of 67k+ dialogue documents and 112k+ annotated summaries in different target languages.Based on the proposed CLIDSUM, we introduce two benchmark settings for supervised and semi-supervised scenarios, respectively.We then build various baseline systems in different paradigms (pipeline and end-to-end) and conduct extensive experiments on CLIDSUM to provide deeper analyses.Furthermore, we propose mDIALBART which extends mBART via further pre-training, where the multiple objectives help the pre-trained model capture the structural characteristics as well as key content in dialogues and the transformation from source to the target language.Experimental results show the superiority of mDIALBART, as an end-to-end model, outperforms strong pipeline models on CLIDSUM.Finally, we discuss specific challenges that current approaches faced with this task and give multiple promising directions for future research.We have released the dataset and code at https:// github.com/krystalan/ClidSum.