Reviewerly/exHarmony huggingface dataset:
Mapping Quantum Circuits to Modular Architectures with QUBO

Modular quantum computing architectures are a promising alternative to monolithic QPU (Quantum Processing Unit) designs for scaling up quantum devices. They refer to a set of interconnected QPUs or cores consisting of tightly coupled quantum bits that can communicate via quantum-coherent and classical links. In multi-core architectures, it is crucial to minimize the amount of communication between cores when executing an algorithm. Therefore, mapping a quantum circuit onto a modular architecture involves finding an optimal assignment of logical qubits (qubits in the quantum circuit) to different cores with the aim to minimize the number of expensive inter-core operations while adhering to given hardware constraints. In this paper, we propose for the first time a Quadratic Unconstrained Binary Optimization (QUBO) technique to encode the problem and the solution for both qubit allocation and inter-core communication costs in binary decision variables. To this end, the quantum circuit is split into slices, and qubit assignment is formulated as a graph partitioning problem for each circuit slice. The costly inter-core communication is reduced by penalizing inter-core qubit communications. The final solution is obtained by minimizing the overall cost across all circuit slices. To evaluate the effectiveness of our approach, we conduct a detailed analysis using a representative set of benchmarks having a high number of qubits on two different multi-core architectures. Our method showed promising results and performed exceptionally well with very dense and highly-parallelized circuits that require on average 0.78 inter-core communications per two-qubit gate.
Full-Stack Quantum Software in Practice: Ecosystem, Stakeholders and Challenges

The emergence of quantum computing has introduced a revolutionary paradigm capable of transforming numerous scientific and industrial sectors. Nevertheless, realizing the practical utilization of quantum software in real-world applications presents significant challenges. Factors such as variations in hardware implementations, the intricacy of quantum algorithms, the integration of quantum and traditional software, and the absence of standardized software and communication interfaces hinder the development of a skilled workforce in this domain. This paper explores tangible approaches to establishing quantum computing software development process and addresses the concerns of various stakeholders. By addressing these challenges, we aim to pave the way for the effective utilization of quantum computing in diverse fields.
Cryptoeconomic Security for Data Availability Committees

Layer 2 systems have received increasing attention due to their potential to scale the throughput of L1 blockchains. To avoid the cost of putting data on chain, these systems increasingly turn to off-chain data availability solutions such as data availability committees (DACs). However, placing trust on DACs conflicts with the goal of obtaining an L2 architecture whose security relies solely on the L1 chain. To eliminate such trust assumptions, we propose a DAC protocol that provides financial incentives to deter the DAC nodes from adversarial behavior such as withholding data upon request. We then analyze the interaction of rational DAC nodes and clients as a dynamic game, with a Byzantine adversary that can corrupt and bribe the participants. We also define a notion of optimality for the DAC protocols, inspired by fairness and economic feasibility. Our main result shows that our protocol is optimal and guarantees security with the highest possible probability under reasonable assumptions on the adversary.
NEURAL NETWORK METHOD FOR PARAMETER ESTIMATION OF FRACTIONAL DISCRETE-TIME UNIFIED SYSTEMS

Data-driven learning of the fractional discrete-time unified system is studied in this paper. A neural network method is suggested in the parameter estimation of fractional discrete-time chaotic systems. An optimization problem is obtained and the famous Adam algorithm is employed to train the neural network’s weights and parameters. The parameter estimation result is compared with that of the stepwise response sensitivity approach (SRSA). This paper provides a high accuracy method for parameter inverse problems. The method also can be applied to data-driven learning of other fractional chaotic systems.
Executing and Proving Over Dirty Ledgers

Scaling blockchain protocols to perform on par with the expected needs of Web3.0 has been proven to be a challenging task with almost a decade of research. In the forefront of the current solution is the idea of separating the execution of the updates encoded in a block from the ordering of blocks. In order to achieve this, a new class of protocols called rollups has emerged. Rollups have as input a total ordering of valid and invalid transactions and as output a new valid state-transition. If we study rollups from a distributed computing perspective, we uncover that rollups take as input the output of a Byzantine Atomic Broadcast (BAB) protocol and convert it to a State Machine Replication (SMR) protocol. BAB and SMR, however, are considered equivalent as far as distributed computing is concerned and a solution to one can easily be retrofitted to solve the other simply by adding/removing an execution step before the validation of the input. This “easy” step of retrofitting an atomic broadcast solution to implement an SMR has, however, been overlooked in practice. In this paper, we formalize the problem and show that after BAB is solved, traditional impossibility results for consensus no longer apply towards an SMR. Leveraging this we propose a distributed execution protocol that allows reduced execution and storage cost per executor ( $$O(\frac{log^2n}{n})$$ ) without relaxing the network assumptions of the underlying BAB protocol and providing censorship-resistance. Finally, we propose efficient non-interactive light client constructions that leverage our efficient execution protocols and do not require any synchrony assumptions or expensive ZK-proofs.
More Reliable Neighborhood Contrastive Learning for Novel Class Discovery in Sensor-Based Human Activity Recognition

Human Activity Recognition (HAR) systems have made significant progress in recognizing and classifying human activities using sensor data from a variety of sensors. Nevertheless, they have struggled to automatically discover novel activity classes within massive amounts of unlabeled sensor data without external supervision. This restricts their ability to classify new activities of unlabeled sensor data in real-world deployments where fully supervised settings are not applicable. To address this limitation, this paper presents the Novel Class Discovery (NCD) problem, which aims to classify new class activities of unlabeled sensor data by fully utilizing existing activities of labeled data. To address this problem, we propose a new end-to-end framework called More Reliable Neighborhood Contrastive Learning (MRNCL), which is a variant of the Neighborhood Contrastive Learning (NCL) framework commonly used in visual domain. Compared to NCL, our proposed MRNCL framework is more lightweight and introduces an effective similarity measure that can find more reliable k-nearest neighbors of an unlabeled query sample in the embedding space. These neighbors contribute to contrastive learning to facilitate the model. Extensive experiments on three public sensor datasets demonstrate that the proposed model outperforms existing methods in the NCD task in sensor-based HAR, as indicated by the fact that our model performs better in clustering performance of new activity class instances.
The Criticality of Implementing Principled Design When Using AI Technologies in Test Development

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).
Recognizing sports activities from video frames using deformable convolution and adaptive multiscale features

Abstract Automated techniques for evaluating sports activities inside dynamic frames are highly dependent on advanced sports analysis by smart machines. The monitoring of individuals and the discerning of athletic pursuits has several potential applications. Monitoring individuals, detecting unusual behavior, identifying medical issues, and tracking patients within healthcare facilities are examples of these applications. An assessment of the feasibility of integrating smart real-time monitoring systems across a variety of athletic environments is provided in this study. Motion and activity detection for recording sporting events has advanced due to the need for a large amount of both real-time and offline data. Through the use of deformable learning approaches, we extend conventional deep learning models to accurately detect and analyze human behavior in sports. Due to its robustness, efficiency, and statistical analysis, the system is a highly suitable option for advanced sports recording detection frameworks. It is essential for sports identification and administration to have a comprehensive understanding of action recognition. An accurate classification of human activities and athletic events can be achieved through the use of a hybrid deep learning framework presented in this study. Using innovative methodologies, we conduct cutting-edge research on action recognition that prioritizes users’ preferences and needs. It is possible to reduce the error rate to less than 3% by using the recommended structure and the three datasets mentioned above. It is 97.84% accurate for UCF-Sport, 97.75% accurate for UCF50, and 98.91% accurate for YouTube. The recommended optimized networks have been tested extensively compared to other models for recognizing athletic actions.
Analysis of Blockchain Security: Classic Attacks, Cybercrime and Penetration Testing

Blockchain is an innovative technology that gives built-in security to any software or application. There is a wide range of applications for blockchain, from risk management to financial services, crypto-currencies and the Internet of Things (IoT). This innovation is based on transparency, immutability, security, efficiency and decentralization. It is a trending topic since cryptocurrencies are a hot topic in the market. Blockchain is a combination of mathematics, cryptography, algorithms and models. In this paper, we present a general overview of the security aspects of blockchain technology.
Blockchain and Artificial Intelligence (AI) integration for revolutionizing security and transparency in finance

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
First‐in‐human real‐time AI‐assisted instrument deocclusion during augmented reality robotic surgery

The integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre-operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real-time de-occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to the authors' best knowledge, the first-in-human on edge deployment of a real-time binary segmentation pipeline during three robot-assisted surgeries: partial nephrectomy, migrated endovascular stent removal, and liver metastasectomy. To this end, a state-of-the-art real-time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real-time binary segmentation of 37 non-organic surgical items, which are never occluded during AR. The application features real-time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics, and acceptance of AR in minimally invasive surgery.
Postdigital Citizen Science: Mapping the Field

Abstract This paper provides a brief overview of citizen science, attending to its tensions and possibilities. We acknowledge the creative potential of citizen science for expanding and diversifying public participation in knowledge production and dissemination, and we also draw attention to its contradictions. We point to emerging postdigital tensions as new technologies and vast public databases are increasingly becoming cornerstones of citizen science. We discuss how postdigital citizen science operates in the context of knowledge capitalism while aiming at its transformation and highlight three key challenges for postdigital citizen science: the challenge of technology, the challenge of political economy, and the challenge of participation. Different postdigital challenges cannot be separated from each other, so we call for a deep reimagination and reconfiguration of citizen science in and for the postdigital condition. We start this reimagination by asking three questions: What is postdigital citizen science? Who (or what!) is the postdigital citizen scientist? How to conduct postdigital citizen science?
survex: an R package for explaining machine learning survival models

Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.survex is available under the GPL3 public license at https://github.com/modeloriented/survex and on CRAN with documentation available at https://modeloriented.github.io/survex.
Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions

Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user's perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flossing habits to a chatbot that either, (1-None): did not explicitly reference previous user utterances, (2-Verbatim): referenced previous utterances verbatim, or (3-Paraphrase): used paraphrases to reference previous utterances. Participants perceived Verbatim and Paraphrase chatbots as more intelligent and engaging. However, the Verbatim chatbot also raised privacy concerns with participants. To gain insights as to why people prefer certain conditions or had privacy concerns, we conducted semi-structured interviews with 15 participants. We discuss implications from our findings that can help designers choose an appropriate format to reference previous user utterances and inform in the design of longitudinal dialogue scripting.
AI and Plagiarism: Opinion from Teachers, Administrators and Policymakers

The emergence of AI applications like ChatGPT is becoming a big concern in recent times. In the field of education, AI promises to bring important breakthroughs to improve teaching and learning efficiency, but it also raises great concerns. Teachers fear that learners may turn to ChatGPT or other AI applications to generate ideas, correct mistakes, or even write complete essays. The educational forums have also revolved around how to accurately assess learners' competence when they currently have too many support tools from AI applications. Teachers, administrators, and policymakers themselves have divergent views on whether the use of AI applications in academic writing is plagiarism. This study focuses on understanding the views of teachers, administrators, and policymakers on the issue of AI and plagiarism. The research instrument is a questionnaire designed to clarify their acceptance or disapproval of learners' use of AI applications in academic writing and the extent of acceptable use. The research results show that AI applications have become increasingly popular in teaching and learning practices, and participants are quite open to this trend. Meanwhile, most participants are aware of the negative impacts that such applications may bring about. The research data also reveal that surveyed teachers and administrators/ policymakers have varied or even opposing perceptions of the use of AI applications in academic writing. These research findings should be considered for developing or adjusting legal documents in the field of education to keep these documents updated with the actual situation.
Self-Calibrating Dynamic Projection Mapping System for Dynamic, Deformable Surfaces with Jitter Correction and Occlusion Handling

Dynamic projection mapping (DPM) is becoming increasingly popular, enabling viewers to visualize information on moving and deformable surfaces. Examples include large data visualization on the moving walls of tents deployed in austere remote locations during emergency management or defense operations. A DPM system typically comprises a RGB-D camera and a projector. In this paper, we present the first fully functional DPM system that auto-calibrates (without any physical props like planar checkerboard or rigid 3D objects) and creates a comprehensible display in the presence of large and fast movements by managing jitter and occlusion by passing objects.Prior DPM systems need specific calibration props, manual inputs and in order to deliver sub-pixel calibration accuracy. Recalibration in the face of movement or change in system setup becomes a time consuming process where the calibration prop needs to be brought back. When rendering content using DPM, errors in calibration are exacerbated and the noise in the depth camera leads to jitter, making the projection unreadable or incomprehensible. Occlusion may disrupt operations completely by jumbling up even the unoccluded parts of the display.In this paper we propose key hardware-agnostic methods for DPM calibration and rendering to make DPM systems easily deployable, stable and legible. First, we present a novel projector-camera calibration that does not need synchronization of the devices and leverages the moving surface itself, a counter-intuitive proposition. We project ArUCo markers on the moving surface and use corresponding detected features of these markers in the RGB and depth camera over multiple frames to accurately estimate the intrinsics and extrinsics of both the projector and the RGB-D camera. Second, we present a DPM rendering method that uses Kalman filtering models to reduce jitter and predict the surface shape in the presence of short term occlusions by other static objects. This results in the first DPM system, to the best of our knowledge, that can auto-calibrate in minutes and can render high resolution content like high-resolution text or images comprehensible even in the presence of fast movements, deformations and occlusions. We compare and evaluate the accuracy with prior methods and analyze the effect of surface movement on the calibration accuracy.
A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

The rapid growth of e-commerce has significantly increased the demand for advanced techniques to address specific tasks in the e-commerce field. In this paper, we present a brief survey of machine learning and deep learning techniques in the context of e-commerce, focusing on the years 2018–2023 in a Google Scholar search, with the aim of identifying state-of-the-art approaches, main topics, and potential challenges in the field. We first introduce the applied machine learning and deep learning techniques, spanning from support vector machines, decision trees, and random forests to conventional neural networks, recurrent neural networks, generative adversarial networks, and beyond. Next, we summarize the main topics, including sentiment analysis, recommendation systems, fake review detection, fraud detection, customer churn prediction, customer purchase behavior prediction, prediction of sales, product classification, and image recognition. Finally, we discuss the main challenges and trends, which are related to imbalanced data, over-fitting and generalization, multi-modal learning, interpretability, personalization, chatbots, and virtual assistance. This survey offers a concise overview of the current state and future directions regarding the use of machine learning and deep learning techniques in the context of e-commerce. Further research and development will be necessary to address the evolving challenges and opportunities presented by the dynamic e-commerce landscape.
Understanding the interplay of entanglement and nonlocality: motivating and developing a new branch of entanglement theory

A standard approach to quantifying resources is to determine which operations on the resources are freely available, and to deduce the partial order over resources that is induced by the relation of convertibility under the free operations. If the resource of interest is the nonclassicality of the correlations embodied in a quantum state, i.e., <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mi>g</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi></mml:math>, then the common assumption is that the appropriate choice of free operations is Local Operations and Classical Communication (LOCC). We here advocate for the study of a different choice of free operations, namely, Local Operations and Shared Randomness (LOSR), and demonstrate its utility in understanding the interplay between the entanglement of states and the nonlocality of the correlations in Bell experiments. Specifically, we show that the LOSR paradigm (i) provides a resolution of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow class="MJX-TeXAtom-ORD"><mml:mtext class="MJX-tex-mathit" mathvariant="italic">anomalies of nonlocality</mml:mtext></mml:mrow></mml:math>, wherein partially entangled states exhibit more nonlocality than maximally entangled states, (ii) entails new notions of genuine multipartite entanglement and nonlocality that are free of the pathological features of the conventional notions, and (iii) makes possible a resource-theoretic account of the self-testing of entangled states which generalizes and simplifies prior results. Along the way, we derive some fundamental results concerning the necessary and sufficient conditions for convertibility between pure entangled states under LOSR and highlight some of their consequences, such as the impossibility of catalysis for bipartite pure states. The resource-theoretic perspective also clarifies why it is neither surprising nor problematic that there are mixed entangled states which do not violate any Bell inequality. Our results motivate the study of LOSR-entanglement as a new branch of entanglement theory.