Reviewerly/exHarmony huggingface dataset:
Characterization, synthesis, and optimization of quantum circuits over multiple-control <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi mathvariant="italic">Z</mml:mi></mml:math> -rotation gates: A systematic study

We conduct a systematic study of quantum circuits composed of multiple-control $Z$-rotation (mczr) gates as primitives, since they are widely used components in quantum algorithms and also have attracted much experimental interest in recent years. We establish a circuit-polynomial correspondence to characterize the functionality of quantum circuits over the mczr gate set with continuous parameters. An analytic method for exactly synthesizing such quantum circuit to implement any given diagonal unitary matrix with an optimal gate count is proposed, which also enables the circuit depth optimal for specific cases with pairs of complementary gates. Furthermore, we present a gate-exchange strategy together with a flexible iterative algorithm for effectively optimizing the depth of any mczr circuit, which can also be applied to quantum circuits over any other commuting gate set. Besides the theoretical investigation, the practical performance of our circuit synthesis and optimization techniques is further evaluated numerically on two typical examples in quantum computing, including diagonal Hermitian operators and quantum approximate optimization algorithm circuits with tens of qubits, which can demonstrate a reduction in circuit depth by 33.40% and 15.55% on average over relevant prior works, respectively. Therefore, our methods and results provide a pathway for implementing quantum circuits and algorithms on recently developed devices.
Expand-Convolute Codes for Pseudorandom Correlation Generators from LPN

The recent development of pseudorandom correlation generators (PCG) holds tremendous promise for highly efficient MPC protocols. Among other correlations, PCGs allow for the efficient generation of oblivious transfer (OT) and vector oblivious linear evaluations (VOLE) with sublinear communication and concretely good computational overhead. This type of PCG makes use of a so-called LPN-friendly error-correcting code. That is, for large dimensions the code should have very efficient encoding and have high minimum distance. We investigate existing LPN-friendly codes and find that several candidates are less secure than was believed. Beginning with the recent expand-accumulate codes, we find that for their aggressive parameters, aimed at good concrete efficiency, they achieve a smaller [pseudo] minimum distance than conjectured. This decreases the resulting security parameter of the PCG but it remains unclear by how much. We additionally show that the recently proposed and extremely efficient silver codes achieve only very small minimum distance and result in concretely efficient attacks on the resulting PCG protocol. As such, silver codes should not be used. We introduce a new LPN-friendly code which we call expand-convolute. These codes have provably high minimum distance and faster encoding time than suitable alternatives, e.g. expand-accumulate. The main contribution of these codes is the introduction of a convolution step that dramatically increases the minimum distance. This in turn allows for a more efficient parameter selection which results in improved concrete performance. In particular, we observe a 3 times improvement in running time for a comparable security level.
On the Security of Keyed Hashing Based on Public Permutations

Doubly-extendable cryptographic keyed functions (deck) generalize the concept of message authentication codes (MAC) and stream ciphers in that they support variable-length strings as input and return variable-length strings as output. A prominent example of building deck functions is Farfalle, which consists of a set of public permutations and rolling functions that are used in its compression and expansion layers. By generalizing the compression layer of Farfalle, we prove its universality in terms of the probability of differentials over the public permutation used in it. As the compression layer of Farfalle is inherently parallel, we compare it to a generalization of a serial compression function inspired by Pelican-MAC. The same public permutation may result in different universalities depending on whether the compression is done in parallel or serial. The parallel construction consistently performs better than the serial one, sometimes by a big factor. We demonstrate this effect using Xoodoo $$[3]$$ , which is a round-reduced variant of the public permutation used in the deck function Xoofff.
Multitask-Based Cluster Transmission for Few-Shot Text Classification

Few-shot text classification aims to perform class prediction by learning from a few examples on labels. Prototypical Network (ProtoNet) is often used to solve the few-shot problem, devoted to constructing a metric space between classes and samples. However, the ProtoNet-based works of building meta-learners have inadequately developed the potential of metric space for the discriminative representation of text, which lead to the deficiency of classification tasks. To improve the above problem, we propose a smoothing strategy combining averaging and prototyping based on ProtoNet. Specifically, we generate a new cluster by cluster transmission algorithm and combine it with a label vector of the pooling function to enrich the distinguishability representation. The proposed algorithm makes the features of the samples more compact and improves the learning efficiency. Then, we use the representation of basic classification features as an auxiliary task to further enhance the diversity of spatial vectors and alleviate the over-fitting problem. Experiments show that our approach further improves the performance of the few-shot text classification task.
On the Impossibility of Algebraic NIZK in Pairing-Free Groups

Non-Interactive Zero-Knowledge proofs (NIZK) allow a prover to convince a verifier that a statement is true by sending only one message and without conveying any other information. In the CRS model, many instantiations have been proposed from group-theoretic assumptions. On the one hand, some of these constructions use the group structure in a black-box way but rely on pairings, an example being the celebrated Groth-Sahai proof system. On the other hand, a recent line of research realized NIZKs from sub-exponential DDH in pairing-free groups using Correlation Intractable Hash functions, but at the price of making non black-box usage of the group. As of today no construction is known to simultaneously reduce its security to pairing-free group problems and to use the underlying group in a black-box way. This is indeed not a coincidence: in this paper, we prove that for a large class of NIZK either a pairing-free group is used non black-box by relying on element representation, or security reduces to external hardness assumptions. More specifically our impossibility applies to two incomparable cases. The first one covers Arguments of Knowledge (AoK) which proves that a preimage under a given one way function is known. The second one covers NIZK (not necessarily AoK) for hard subset problems, which captures relations such as DDH, Decision-Linear and Matrix-DDH.
Two-Round Stateless Deterministic Two-Party Schnorr Signatures from Pseudorandom Correlation Functions

Schnorr signatures are a popular choice due to their simplicity, provable security, and linear structure that enables relatively easy threshold signing protocols. The deterministic variant of Schnorr (where the nonce is derived in a stateless manner using a PRF from the message and a long term secret) is widely used in practice since it mitigates the threats of a faulty or poor randomness generator (which in Schnorr leads to catastrophic breaches of security). Unfortunately, threshold protocols for the deterministic variant of Schnorr have so far been quite inefficient, as they make non black-box use of the PRF involved in the nonce generation. In this paper, we present the first two-party threshold protocol for Schnorr signatures, where signing is stateless and deterministic, and only makes black-box use of the underlying cryptographic algorithms. We present a protocol from general assumptions which achieves covert security, and a protocol that achieves full active security under standard factoring-like assumptions. Our protocols make crucial use of recent advances within the field of pseudorandom correlation functions (PCFs). As an additional benefit, only two-rounds are needed to perform distributed signing in our protocol, connecting our work to a recent line of research on the trade-offs between round complexity and cryptographic assumptions for threshold Schnorr signatures.
Recent Progress on Text Summarisation Based on BERT and GPT

Text summarisation is one of the essential topics in natural language processing. Pre-trained language models, especially BERT and GPT, are the most advanced methods for various natural language processing tasks; thus, many researchers have tried to use BERT and GPT for text summarisation. To facilitate further research on this topic, this paper surveys its state-of-the-art. Specifically, we summarise the topic’s main research issues and BERT- and GPT-based solutions, compare these methods (especially their pros and cons), explore their applications, and discuss the challenges to future research.
Security-Preserving Distributed Samplers: How to Generate Any CRS in One Round Without Random Oracles

A distributed sampler is a way for several mutually distrusting parties to non-interactively generate a common reference string (CRS) that all parties trust. Previous work constructs distributed samplers in the random oracle model, or in the standard model with very limited security guarantees. This is no accident, as standard model distributed samplers with full security were shown impossible. In this work, we provide new definitions for distributed samplers which we show achieve meaningful security guarantees in the standard model. In particular, our notion implies that the hardness of a wide range of security games is preserved when the CRS is replaced with a distributed sampler. We also show how to realize our notion of distributed samplers. A core technical tool enabling our construction is a new notion of single-message zero knowledge.
Performance-enhanced time-delayed photonic reservoir computing system using a reflective semiconductor optical amplifier

We propose a time-delayed photonic reservoir computing (RC) architecture utilizing a reflective semiconductor optical amplifier (RSOA) as an active mirror. The performance of the proposed RC structure is investigated by two benchmark tasks, namely the Santa Fe time-series prediction task and the nonlinear channel equalization task. The simulation results show that both the prediction and equalization performance of the proposed system are significantly improved with the contribution of RSOA, with respect to the traditional RC system using a mirror. By increasing the drive current of the RSOA, the greater nonlinearity of the RSOA gain saturation is achieved, as such the prediction and equalization performance are enhanced. It is also shown that the proposed RC architecture shows a wider consistency interval and superior robustness than the traditional RC structure for most of the measured parameters such as coupling strength, injection strength, and frequency detuning. This work provides a performance-enhanced time-delayed RC structure by making use of the nonlinear transformation of the RSOA feedback.
A Multi-Task Fusion Strategy-Based Decision-Making and Planning Method for Autonomous Driving Vehicles

The autonomous driving technology based on deep reinforcement learning (DRL) has been confirmed as one of the most cutting-edge research fields worldwide. The agent is enabled to achieve the goal of making independent decisions by interacting with the environment and learning driving strategies based on the feedback from the environment. This technology has been widely used in end-to-end driving tasks. However, this field faces several challenges. First, developing real vehicles is expensive, time-consuming, and risky. To further expedite the testing, verification, and iteration of end-to-end deep reinforcement learning algorithms, a joint simulation development and validation platform was designed and implemented in this study based on VTD–CarSim and the Tensorflow deep learning framework, and research work was conducted based on this platform. Second, sparse reward signals can cause problems (e.g., a low-sample learning rate). It is imperative for the agent to be capable of navigating in an unfamiliar environment and driving safely under a wide variety of weather or lighting conditions. To address the problem of poor generalization ability of the agent to unknown scenarios, a deep deterministic policy gradient (DDPG) decision-making and planning method was proposed in this study in accordance with a multi-task fusion strategy. The main task based on DRL decision-making planning and the auxiliary task based on image semantic segmentation were cross-fused, and part of the network was shared with the main task to reduce the possibility of model overfitting and improve the generalization ability. As indicated by the experimental results, first, the joint simulation development and validation platform built in this study exhibited prominent versatility. Users were enabled to easily substitute any default module with customized algorithms and verify the effectiveness of new functions in enhancing overall performance using other default modules of the platform. Second, the deep reinforcement learning strategy based on multi-task fusion proposed in this study was competitive. Its performance was better than other DRL algorithms in certain tasks, which improved the generalization ability of the vehicle decision-making planning algorithm.
9 Applications and future trends of Artificial Intelligence and blockchain-powered Augmented Reality

Artificial Intelligence (AI) is a sophisticated computer system designed to simulate human intelligence under different circumstances. It incorporates several branches of study and utilizes existing technologies to demonstrate human behavior and autonomously take necessary actions according to any particular situation. A more recent innovation in capturing and storing data is the blockchain technology, which shares data across several nodes in a distributed network. Augmented Reality (AR) is a technology that aims to provide users with an interactive experience of their realworld environment by enhancing real-world objects through computer graphics and superimposing virtual computer-generated images on a user’s view of the real world. Implementation of AI further enhances the experience of AR by integrating its abilities and adding new features to the AR intelligence, such as text analysis, object detection, scene labeling, and much more. It enables AR to interact with the physical world using neural networks. This paper focuses on exploring the different applications of AI for AR and how fusing blockchain with them can support the entire AR ecosystem.
Chapter 9 Quantum computing: future of artificial intelligence and its applications

Quantum computing (QC) is the emerging technology for solving complex problems through the laws of quantum mechanism and optimized algorithms. To examine the possibilities of active cooperation across both disciplines of computer science, this article explains the core components of quantum computing and conveys some of the important features linked by quantum computing and Artificial Intelligence (AI). To make major progress with artificial intelligence, quantum computing is probably necessary. Quantum computing’s training and processing power can be used to improve AI and machine learning (ML) algorithms at a reduced cost. AI can provide quantum computers with crucial error-correcting algorithms. Several AI algorithms have been successfully applied to quantum computers; they are constrained by the computational power of conventional computers despite developing useful applications. Artificial intelligence may benefit from quantum computing’s computational boost in order to handle more challenging issues. This case study explains the importance of quantum computing, the future of QC in AI, and the different applications of quantum computing, such as AI and ML, cybersecurity and cryptography, and weather forecasting.
Qumode fusion for continuous-variable cluster states

The quantum fusion technique enables the bonding of two cluster states together, and therefore provides a promising method to grow large-scale cluster states. Quantum fusion was first proposed in a discrete-variable regime. In terms of the resource usage, quantum fusion can be divided into two categories, which are type-I and type-II fusions. Type-I fusion consumes one qubit in the fusion procedure, while type-II consumes two qubits. However, for continuous-variable (CV) analogs, concrete schemes for realizing quantum fusions of cluster states with arbitrary topologies have not been shown yet. In this paper, we present the CV analog of quantum fusion schemes. We show that CV type-I fusion can be realized by an ancilla and two quantum nondemolition interactions, and CV type-II fusion can be realized by an entanglement swapping scheme. With finite squeezing considered, we analyze the extra errors added by the fusion procedures in CV cluster-state quantum computation. Our schemes are universal and can be generalized to fuse cluster states with arbitrary topologies.
Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation

In accordance with the inability of various hair artefacts subjected to dermoscopic medical images, undergoing illumination challenges that include chest-Xray featuring conditions of imaging acquisi-tion situations built with clinical segmentation. The study proposed a novel deep-convolutional neural network (CNN)-integrated methodology for applying medical image segmentation upon chest-Xray and dermoscopic clinical images. The study develops a novel technique of segmenting medical images merged with CNNs with an architectural comparison that incorporates neural networks of U-net and fully convolutional networks (FCN) schemas with loss functions associated with Jaccard distance and Binary-cross entropy under optimised stochastic gradient descent + Nesterov practices. Digital image over clinical approach significantly built the diagnosis and determination of the best treatment for a patient's condition. Even though medical digital images are subjected to varied components clarified with the effect of noise, quality, disturbance, and precision depending on the enhanced version of images segmented with the optimised process. Ultimately, the threshold technique has been employed for the output reached under the pre- and post-processing stages to contrast the image technically being developed. The data source applied is well-known in PH2 Database for Melanoma lesion segmentation and chest X-ray images since it has variations in hair artefacts and illumination. Experiment outcomes outperform other U-net and FCN architectures of CNNs. The predictions produced from the model on test images were post-processed using the threshold technique to remove the blurry boundaries around the predicted lesions. Experimental results proved that the present model has better efficiency than the existing one, such as U-net and FCN, based on the image segmented in terms of sensitivity = 0.9913, accuracy = 0.9883, and dice coefficient = 0.0246.
Solar Power Forecasting Using Hybrid Model

The significance of forecasting has been increasing in power systems due to the more usage of renewable energy sources. Renewable energy sources such as solar energy, which is very intermittent in nature. Hence, to capture this nature, Deep learning (DL) based techniques are required. Because DL-based techniques learn quickly from patterns and deal with intermittent changes in load and renewable sources. This study proposes DL technique-based convolution neural network (CNN) - long short-term memory (LSTM) hybrid model to forecast solar energy. This allows the LSTM to learn features from the input data that have been learned by CNN. Further, 15 minutes of solar power generation data for one year from Visvesvaraya National Institute of Technology (VNIT), Nagpur is used as the benchmark data to evaluate the hybrid model. This study considers a novel meteorological factor and compares that with without considering a meteorological factor. Moreover, CNN and LSTM individual DL models were developed, analyzed, and tested for the same VNIT data for forecasting the 15-minute solar power generation. The proposed hybrid model is compared with individual LSTM and CNN models in terms of performance evaluation matrices i.e., The root mean squared error (RMSE), the mean squared error (MSE), the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the coefficient of determination (R-Squared) are all measures of error. The percentage improvement from the individual model and the proposed hybrid model has been evaluated with and without considering meteorological factors. The proposed model improves the efficiency by 0.45% and 21.31%, as compared to LSTM and CNN by considering metrological factors respectively. Similarly, it improves without considering metrological factors 1.01% and 21.59% for LSTM and CNN respectively.
Threshold Delegatable Anonymous Credentials with Controlled and Fine-Grained Delegation

Anonymous credential systems allow users to obtain a credential on multiple attributes from an organization and then present it to verifiers in a way that no information beyond what attributes are required to be shown is revealed. Moreover, multiple uses of the credential cannot be linked. Thus they represent an attractive tool to realize fine-grained privacy-friendly authentication and access control. In order to avoid a single point of trust and failure, decentralized AC systems have been proposed. They eliminate the need for a trusted credential issuer, e.g., by relying on a set of credential issuers that issue credentials in a threshold manner (e.g., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$t$</tex-math></inline-formula> out of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> f). In this paper, we present a novel AC system with such a threshold issuance that additionally provides credential delegation. It represents the first decentralized <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> delegatable AC system. We provide a rigorous formal framework for such threshold delegatable anonymous credentials ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {TDAC}$</tex-math></inline-formula> 's). Our concrete approach departs from previous delegatable ACs and is inspired by the concept of functional credentials. More precisely, we propose a threshold delegatable subset predicate encryption ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {TDSPE}$</tex-math></inline-formula> ) scheme and use <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {TDSPE}$</tex-math></inline-formula> to construct a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathsf {TDAC}$</tex-math></inline-formula> scheme and present a comparison with previous work and performance benchmarks based on a prototype implementation.