Reviewerly/exHarmony huggingface dataset:
Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments

Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding. Scientists have used AI techniques to rediscover previously known concepts. So far, no examples of that kind have been reported that are applied to open problems for getting new scientific concepts and ideas. Here, we present Theseus, an algorithm that can provide new conceptual understanding, and we demonstrate its applications in the field of experimental quantum optics. To do so, we make four crucial contributions. (i) We introduce a graph-based representation of quantum optical experiments that can be interpreted and used algorithmically. (ii) We develop an automated design approach for new quantum experiments, which is orders of magnitude faster than the best previous algorithms at concrete design tasks for experimental configuration. (iii) We solve several crucial open questions in experimental quantum optics which involve practical blueprints of resource states in photonic quantum technology and quantum states and transformations that allow for new foundational quantum experiments. Finally, and most importantly, (iv) the interpretable representation and enormous speed-up allow us to produce solutions that a human scientist can interpret and gain new scientific concepts from outright. We anticipate that Theseus will become an essential tool in quantum optics for developing new experiments and photonic hardware. It can further be generalized to answer open questions and provide new concepts in a large number of other quantum physical questions beyond quantum optical experiments. Theseus is a demonstration of explainable AI (XAI) in physics that shows how AI algorithms can contribute to science on a conceptual level.3 MoreReceived 23 November 2020Revised 1 July 2021Accepted 1 July 2021DOI:https://doi.org/10.1103/PhysRevX.11.031044Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement manipulationQuantum opticsQuantum state engineeringQuantum states of lightQuantum InformationAtomic, Molecular & Optical
Identity and Aggregate Signature-Based Authentication Protocol for IoD Deployment Military Drone

With the rapid miniaturization in sensor technology, ruddervator, arduino, and multi-rotor system, drone technology catches much attention of the researchers.It can be controlled remotely by a seated operator sitting to a powerful intelligence computer system (PICS) or an airborne control and command platform (AC2P).The two types of drones (reconnaissance and attacking) can communicate with each other and with the PICS or AC2P through wireless network channels referred to as Flying Ad Hoc Network or Unmanned Aerial Vehicular Network (FANET or UAVN).When the line of sight is broken, communication is mainly carried out through satellite using GPS (Global Positioning System) signals.Both GPS and UAVN/FANET use open network channels for data broadcasting, exposed to several threats, and its security is challenging for the researchers.Monitoring data transmission traffic, espionage, troop movement, border surveillance, searching and warfare battlefield phenomenon, etc., can auspiciously be achieved by developing a robust authentication scheme for IoD deployment military drone.Therefore, this research illustrates the designing of two separate protocols based on aggregate signature, identity, pairing cryptography, and Computational Diffie-Hellman Problem (CDHP) to guarantee data integrity, authorization, and confidentiality among drones and AC2P/PICS.The outdated data transmission flaw has also been tackled, which is frequently noted in prior protocols.The security of these protocols will formally be verified using a random oracle model (ROM), a real-or-random (ROR) model, and informally using pragmatic illustration and mathematical lemmas.Nonetheless, the performance analysis section will be executed using the algorithmic big-O notation.The results show that these protocols are verifiably protected in the Random Oracle Model (ROM) and Real-Or-Random (ROR) model using the CDHP.
Acceptance determinants of 5G services

5G is a revolutionary development in network technologies which is gradually becoming very common among people contributing significantly in different fields such as education, industry, agriculture, health, tourism and military. Currently, 5G is an outbreak change as opposed to the traditional service of the Internet since it offers better quality, ultra-fast connection, low-cost, reduced latency, energy saving, which makes its great impact even greater in people’s life. The present study examines various factors that have a significant impact on the Use of 5G in the Gulf area. The study extended the TAM (Technology Acceptance Model) to include factors such as Perceived Enjoyment, Perceived Resources and Perceived Skills Readiness. The present research has adopted a hybrid model that incorporates TAM determinants with other external factors which have a direct relation with 5G as internet service. Previous studies have focused on the importance of 5G in different environments and countries. However, this study focuses on the newly spread Use of 5G in the gulf area by adopting a hybrid conceptual model. The findings suggest that 5G may help in promoting the usage of internet service more effectively with its low-cost, faster data transfer and better quality. Moreover, the findings indicate a positive effect of the gender as a mediator between the variables: Perceived Skills Readiness, Perceived Ease of use, and Perceived Resources.
Adaptive Unscented Kalman Filter for Target Tacking with Time-Varying Noise Covariance Based on Multi-Sensor Information Fusion

In this paper, an innovative optimal information fusion methodology based on adaptive and robust unscented Kalman filter (UKF) for multi-sensor nonlinear stochastic systems is proposed. Based on the linear minimum variance criterion, this multi-sensor information fusion method has a two-layer architecture: at the first layer, a new adaptive UKF scheme for the time-varying noise covariance is developed and serves as a local filter to improve the adaptability together with the estimated measurement noise covariance by applying the redundant measurement noise covariance estimation, which is isolated from the state estimation; the second layer is the fusion structure to calculate the optimal matrix weights and gives the final optimal state estimations. Based on the hypothesis testing theory with the Mahalanobis distance, the new adaptive UKF scheme utilizes both the innovation and the residual sequences to adapt the process noise covariance timely. The results of the target tracking simulations indicate that the proposed method is effective under the condition of time-varying process-error and measurement noise covariance.
Densely-Populated Traffic Detection Using YOLOv5 and Non-maximum Suppression Ensembling

Vehicular object detection is the heart of any intelligent traffic system. It is essential for urban traffic management. Recent state-of-the-art methods apply R-CNN, Fast R-CNN, Faster R-CNN, and YOLO for this task. However, region-based CNN methods have the problem of higher inference time which makes them unrealistic to use the model in real-time. YOLO on the other hand struggles to detect small objects that appear in groups. In this paper, we propose a method that can locate and classify vehicular objects from a given densely crowded image using YOLOv5. We apply non-maximum suppression ensembling of 4 different models of YOLOv5 trained on different setups. The performance of our proposed model was measured on the Dhaka AI dataset which contains densely crowded vehicular images taken from both top view and side view of the street in both day and night settings. Our experiments show that our model achieved mAP@0.5 of 0.458 with an inference time of 0.75s outperforming other state-of-the-art models on performance. Hence, the model can be implemented in the street for real-world traffic detection which can be used for traffic control and data collection.
Varying PRNG to improve image cryptography implementation

Securing information became essential to exchange multimedia information safely. These exchanged data need to be transformed in a well-managed, secure, and reliable manner. This paper focuses on securing multimedia images via cryptography during transmission among users using an effective selection from several Pseudorandom Number Generators (PRNG). This paper implements several PRNG techniques involved within consecutive cryptoprocesses of substitution and transposition that have proven a secure process. In the study, different PRNGs are tested to encrypt images in forms of grayscale and colored RGB images compared to current similar approaches. The work experimentation is aiming at investigating and identifying suitability and reliability through security measures standard parameters. The research is showing proper PRNG selection as an attractive, significant work worth remarking for image cryptography.
Reachability Deficits in Quantum Approximate Optimization of Graph Problems

The quantum approximate optimization algorithm (QAOA) has become a cornerstone of contemporary quantum applications development. Here we show that the \emph{density} of problem constraints versus problem variables acts as a performance indicator. Density is found to correlate strongly with approximation inefficiency for fixed depth QAOA applied to random graph minimization problem instances. Further, the required depth for accurate QAOA solution to graph problem instances scales critically with density. Motivated by Google's recent experimental realization of QAOA, we preform a reanalysis of the reported data reproduced in an ideal noiseless setting. We found that the reported capabilities of instances addressed experimentally by Google, approach a rapid fall-off region in approximation quality experienced beyond intermediate-density. Our findings offer new insight into performance analysis of contemporary quantum optimization algorithms and contradict recent speculation regarding low-depth QAOA performance benefits.
Dealing with Typos for BERT-based Passage Retrieval and Ranking

Passage retrieval and ranking is a key task in open-domain question answering and information retrieval.Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers.These methods have been shown to effectively model the semantic matching between queries and passages, also in presence of keyword mismatch, i.e. passages that are relevant to a query but do not contain important query keywords.In this paper we consider the Dense Retriever (DR), a passage retrieval method, and the BERT re-ranker, a popular passage re-ranking method.In this context, we formally investigate how these models respond and adapt to a specific type of keyword mismatch -that caused by keyword typos occurring in queries.Through empirical investigation, we find that typos can lead to a significant drop in retrieval and ranking effectiveness.We then propose a simple typos-aware training framework for DR and BERT re-ranker to address this issue.Our experimental results on the MS MARCO passage ranking dataset show that, with our proposed typos-aware training, DR and BERT re-ranker can become robust to typos in queries, resulting in significantly improved effectiveness compared to models trained without appropriately accounting for typos.
Efficient and Privacy-Preserving Multi-Party Skyline Queries Over Encrypted Data

One existing challenge associated with large scale skyline queries on cloud services, particularly when dealing with private information such as biomedical data, is supporting multi-party queries with curious-but-honest parties on encrypted data. In addition, existing solutions designed for performing secure skyline queries incur significant communication and computation costs due to ciphertext calculation. Thus, in this paper, we demonstrate the potential of supporting privacy-preserving multi-party skyline queries on encrypted data using additive homomorphic and proxy re-encryption cryptosystems. However, the secure computation based on these cryptosystems will further slow down query efficiency. To improve the efficiency of comparison on encrypted data, we redesign two lightweight secure comparison protocols. Meanwhile, we present an efficient method named “blind-reading” to securely obtain the skyline point. We also propose a novel method, Privacy Matrix, designed to reduce the scale of the dataset so that the computational cost is significantly decreased without privacy leakage. Then, we construct our secure skyline query protocol by integrating lightweight secure comparison protocols, “blind-reading” and Privacy Matrix techniques. Finally, we evaluate the security of our protocol, where we show it is secure without leaking information. The performance evaluation also shows that our proposed approach significantly improves the efficiency (at least ×4.5 faster) compared to the-state-of-art and has the scalability of query processing under large datasets.
Building and Evaluating Open-Domain Dialogue Corpora with Clarifying Questions

Enabling open-domain dialogue systems to ask clarifying questions when appropriate is an important direction for improving the quality of the system response.Namely, for cases when a user request is not specific enough for a conversation system to provide an answer right away, it is desirable to ask a clarifying question to increase the chances of retrieving a satisfying answer.To address the problem of 'asking clarifying questions in opendomain dialogues': (1) we collect and release a new dataset focused on open-domain singleand multi-turn conversations, (2) we benchmark several state-of-the-art neural baselines, and (3) we propose a pipeline consisting of offline and online steps for evaluating the quality of clarifying questions in various dialogues.These contributions are suitable as a foundation for further research.
Quantum Probes for the Characterization of Nonlinear Media

Active optical media leading to interaction Hamiltonians of the form H=λ˜(a+a†)ζ represent a crucial resource for quantum optical technology. In this paper, we address the characterization of those nonlinear media using quantum probes, as opposed to semiclassical ones. In particular, we investigate how squeezed probes may improve individual and joint estimation of the nonlinear coupling λ˜ and of the nonlinearity order ζ. Upon using tools from quantum estimation, we show that: (i) the two parameters are compatible, i.e., the may be jointly estimated without additional quantum noise; (ii) the use of squeezed probes improves precision at fixed overall energy of the probe; (iii) for low energy probes, squeezed vacuum represent the most convenient choice, whereas for increasing energy an optimal squeezing fraction may be determined; (iv) using optimized quantum probes, the scaling of the corresponding precision with energy improves, both for individual and joint estimation of the two parameters, compared to semiclassical coherent probes. We conclude that quantum probes represent a resource to enhance precision in the characterization of nonlinear media, and foresee potential applications with current technology.
Reproducibility in Evolutionary Computation

Experimental studies are prevalent in Evolutionary Computation ( EC ), and concerns about the reproducibility and replicability of such studies have increased in recent times, reflecting similar concerns in other scientific fields. In this article, we discuss, within the context of EC, the different types of reproducibility and suggest a classification that refines the badge system of the Association of Computing Machinery ( ACM ) adopted by ACM Transactions on Evolutionary Learning and Optimization ( TELO ). We identify cultural and technical obstacles to reproducibility in the EC field. Finally, we provide guidelines and suggest tools that may help to overcome some of these reproducibility obstacles.
E-SFD: Explainable Sensor Fault Detection in the ICS Anomaly Detection System

Industrial Control Systems (ICS) are evolving into smart environments with increased interconnectivity by being connected to the Internet.These changes increase the likelihood of security vulnerabilities and accidents.As the risk of cyberattacks on ICS has increased, various anomaly detection studies are being conducted to detect abnormal situations in industrial processes.However, anomaly detection in ICS suffers from numerous false alarms.When false alarms occur, multiple sensors need to be checked, which is impractical.In this study, when an anomaly is detected, sensors displaying abnormal behavior are visually presented through XAI-based analysis to support quick practical actions and operations.Anomaly Detection has designed and applied better anomaly detection technology than the first prize at HAICon2020, an ICS security threat detection AI contest hosted by the National Security Research Institute last year, and explains the anomalies detected in its model.To the best of our knowledge, our work is at the forefront of explainable anomaly detection research in ICS.Therefore, it is expected to increase the utilization of anomaly detection technology in ICS.
Characterization of control in a superconducting qutrit using randomized benchmarking

We characterize control of a qutrit implemented in the lowest three energy levels of a capacitively shunted flux-biased superconducting circuit. Randomized benchmarking over the qutrit Clifford group yields an average fidelity of 98.89±0.05%. For a selected subset of the Clifford group, we perform quantum process tomography and observe the behavior of repeated gate sequences. Each qutrit gate is generated using only two-state rotations via a method applicable to any unitary. We find that errors are due primarily to decoherence and have a significant contribution from level shifts. This work demonstrates high-fidelity qutrit control and outlines avenues for future work on the optimal control of superconducting qudits.Received 6 September 2020Revised 10 September 2021Accepted 14 September 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.L042007Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum benchmarkingQuantum computationQuantum controlQuantum gatesQuantum tomographyPhysical SystemsJosephson junctionsSuperconducting qubitsTechniquesNumerical approximation & analysisCondensed Matter, Materials & Applied PhysicsQuantum Information
Texture Memory-Augmented Deep Patch-Based Image Inpainting

Patch-based methods and deep networks have been employed to tackle image inpainting problem, with their own strengths and weaknesses. Patch-based methods are capable of restoring a missing region with high-quality texture through searching nearest neighbor patches from the unmasked regions. However, these methods bring problematic contents when recovering large missing regions. Deep networks, on the other hand, show promising results in completing large regions. Nonetheless, the results often lack faithful and sharp details that resemble the surrounding area. By bringing together the best of both paradigms, we propose a new deep inpainting framework where texture generation is guided by a texture memory of patch samples extracted from unmasked regions. The framework has a novel design that allows texture memory retrieval to be trained end-to-end with the deep inpainting network. In addition, we introduce a patch distribution loss to encourage high-quality patch synthesis. The proposed method shows superior performance both qualitatively and quantitatively on three challenging image benchmarks, i.e., Places, CelebA-HQ, and Paris Street-View datasets (Code will be made publicly available in https://github.com/open-mmlab/mmediting).
Negative Quasiprobabilities Enhance Phase Estimation in Quantum-Optics Experiment

Operator noncommutation, a hallmark of quantum theory, limits measurement precision, according to uncertainty principles. Wielded correctly, though, noncommutation can boost precision. A recent foundational result relates a metrological advantage with negative quasiprobabilities-quantum extensions of probabilities-engendered by noncommuting operators. We crystallize the relationship in an equation that we prove theoretically and observe experimentally. Our proof-of-principle optical experiment features a filtering technique that we term partially postselected amplification (PPA). Using PPA, we measure a wave plate's birefringent phase. PPA amplifies, by over two orders of magnitude, the information obtained about the phase per detected photon. In principle, PPA can boost the information obtained from the average filtered photon by an arbitrarily large factor. The filter's amplification of systematic errors, we find, bounds the theoretically unlimited advantage in practice. PPA can facilitate any phase measurement and mitigates challenges that scale with trial number, such as proportional noise and detector saturation. By quantifying PPA's metrological advantage with quasiprobabilities, we reveal deep connections between quantum foundations and precision measurement.
Model-free inference of unseen attractors: Reconstructing phase space features from a single noisy trajectory using reservoir computing

Reservoir computers are powerful tools for chaotic time series prediction. They can be trained to approximate phase space flows and can thus both predict future values to a high accuracy and reconstruct the general properties of a chaotic attractor without requiring a model. In this work, we show that the ability to learn the dynamics of a complex system can be extended to systems with multiple co-existing attractors, here a four-dimensional extension of the well-known Lorenz chaotic system. We demonstrate that a reservoir computer can infer entirely unexplored parts of the phase space; a properly trained reservoir computer can predict the existence of attractors that were never approached during training and, therefore, are labeled as unseen. We provide examples where attractor inference is achieved after training solely on a single noisy trajectory.
IES: Hyper-chaotic plain image encryption scheme using improved shuffled confusion-diffusion

Achieving a secured encrypted image is challenging in sensitive data communications. An accurate and secure encryption scheme for colour images requires the generation of intricate secret keys. To attain this goal, we proposed an improved shuffled confusion-diffusion based colour Image Encryption Scheme (IES) using hyper-chaotic plain images. First, five different sequences of random numbers were generated. Two of these sequences were used to mix image pixels and bits, while the remaining three were used to XOR the values of image pixels. The performance of the developed IES was evaluated in terms of various measures such as key space size, correlation coefficient, entropy, mean squared deviation (MSD), Peak Signal-to-Noise Ratio (PSNR) and differential analysis. The values of the correlation coefficient (0.000732), entropy (7.9997), PSNR (7.61), and MSE (11,258) were determined to be better compared to with the art of technologies that exist. The proposed IES outperformed the other methods in terms of computational efficiency and other performance metrics when compared. The proposed may be beneficial for encrypting big data sets on parallel machines.