Reviewerly/exHarmony huggingface dataset:
6-qubit optimal Clifford circuits

Abstract Clifford group lies at the core of quantum computation—it underlies quantum error correction, its elements can be used to perform magic state distillation and they form randomized benchmarking protocols, Clifford group is used to study quantum entanglement, and more. The ability to utilize Clifford group elements in practice relies heavily on the efficiency of their circuit-level implementation. Finding short circuits is a hard problem; despite Clifford group being finite, its size grows quickly with the number of qubits n , limiting known optimal implementations to n = 4 qubits. For n = 6, the number of Clifford group elements is about 2.1 × 10 23 . In this paper, we report a set of algorithms, along with their C implementation, that implicitly synthesize optimal circuits for all 6-qubit Clifford group elements by storing a subset of the latter in a database of size 2.1TB (1kB = 1024B). We demonstrate how to extract arbitrary optimal 6-qubit Clifford circuit in 0.0009358 and 0.0006274 s using consumer- and enterprise-grade computers (hardware) respectively, while relying on this database. We use this implementation to establish a new example of quantum advantage by Clifford circuits over CNOT gate circuits and find optimal Clifford 2-designs for up to 4 qubits.
Behavior and feedback based trust computation in cloud environment

Cloud Computing has become a promising paradigm to deliver different computing resources and services over the Internet on demand. The cloud users have to rely on the third party service providers for accessing services. With an increase in available cloud services, selecting an appropriate cloud service provider to deliver the service securely will be always challenging for users. The trust measure plays an important role while selecting proper service providers to handle user’s requests in the cloud environment. Hence, evaluation of the trustworthiness of the cloud service provider before selecting it to deliver the service has become an important requirement in the cloud environment. The paper presents a method to evaluate the trustworthiness of cloud service provider on the basis of it’s behavior and feedback given by the users. The various Quality of Service attributes are considered for computing behavioral trust values. The different parameters from the service level agreement are used to maintain the feedback and compute the feedback trust value for the service provider. The trustworthiness of the cloud service provider is judged by computing the cumulative trust, which is calculated using behavioral trust and feedback trust. Also, the proposed model includes a mechanism to judge the genuineness of feedback submitted by the users. The proposed method is compared with Evidence Based Trust Model and Enhanced QoS based Model to evaluate the performance in terms of accuracy and efficiency.
IntAnti-Phish: An Intelligent Anti-Phishing Framework Using Backpropagation Neural Network

Among the cybercriminals, the popularity of phishing has been rapidly growing day by day. Therefore, phishing has become an alarming issue to solve in the field of cybersecurity. Many researchers have already proposed several anti-phishing approaches to detect phishing in terms of email, webpages, images, or links. This study also aimed to propose and implement an intelligent framework to detect phishing URLs (Uniform Resource Locator). It has been observed in this study that Backpropagation Neural Network-based systems need to tune various hyperparameters to obtain the optimized output. With a maximum of two hidden layers along with 400 epochs can reach maximum accuracy of 0.93, the minimum mean squared error of 0.27, and also a minimum error rate of 0.07 which measurements lead this study to generate an optimized model for phishing detection. The detailed process of feature extraction and optimized model generation along with the detection of unknown URLs are considered and proposed during the development of IntAnti-Phish (An Intelligent Anti-Phishing Framework).
Leveraging Regular Expressions for Flexible Scenario Detection in Recorded Driving Data

For the development of Advanced Driver Assistant Systems (ADAS) and Automated Driving Systems (ADS) large amounts of driving data are collected. Storing, processing and analyzing this data requires significant efforts. For these efforts to pay off, data must be made accessible and reusable for different development phases from specification to testing. However, raw measurement data can largely vary in format, configuration and comprehensiveness. Adding meaningful, semantic labels to the data, also called data enrichment, is a promising way to improve searchability and to compute aggregating statistics over large databases. In this work we identify challenges and define requirements for data enrichment of real world measurement data for ADAS and ADS. We suggest a framework as a general and modular processing pipeline to tackle common challenges of real world data. Within this framework we support Boolean expressions to reduce multivariate time series to states of interest. Finally regular expressions are used as a deterministic method to identify semantically meaningful sequences. We demonstrate the flexibility of this approach by detecting lane change and cut-in maneuvers. The proposed methods prove to be well suited for robust maneuver detection with varying durations.
Non-local correlation between two coupled qubits interacting nonlinearly with a two-mode cavity: Bell function, Trace norm and Bures distance quantifiers

Abstract In this paper, we study the dynamics of two coupled qubits interacting with a two-mode parametric amplifier cavity through the nonlinear interactions of the photon processes under the intrinsic decoherence. The nonlinear unitary interactions lead to generating different types of nonlocal correlations via Bell nonlocality, trace-norm measurement-induced non-locality (MIN), and Bures-distance entanglement. Based on the intrinsic decoherence, the two-qubit states have stationary amounts of the MIN-correlation and Bures distance entanglement with more stability. It is found that the qubit–qubit interaction coupling is affected as additional decoherence. It leads to more oscillations and stability. The enhancement of the amount of the generated nonlocal correlations depends on the superposition of Barut-Girardello coherent states. The phenomenon of sudden birth/death and the Bell non-locality can be enhanced when the two-mode parametric amplifier cavity is initially prepared in the coherent states.
Robust Alignment of Multi-Exposed Images With Saturated Regions

It is challenging to align multi-exposed images due to large illumination variations, especially in presence of saturated regions.In this paper, a novel image alignment algorithm is proposed to cope with the multi-exposed images with saturated regions.Specifically, the multi-exposed images are first normalized by using intensity mapping functions (IMFs) in consideration of saturated pixels.Then, the normalized images are coded by using the local binary pattern (LBP).Finally, the coded images are aligned by formulating an optimization problem by using a differentiable Hamming distance.Experimental results show that the proposed algorithm outperforms state-of-the-art alignment methods for multi-exposed images in terms of alignment accuracy and robustness to exposure values.
Reconciling Image Captioning and User’s Comments for Urban Tourism

Image captioning as a process of assigning textual description to an image has gained momentum nowadays thanks to recent advances in deep learning related architectures and availability of associated tools. In the era of digital tourism, this offers a valuable framework to reconcile the widely available tourism images and user's generated content. This paper presents a novel approach to perform such reconciliation in a way to benefit tourism industry. Especially, IMGUR online image sharing services has been employed to construct a novel database, referred Tourism48, which contains gallery tourism images from 48 countries together with their associated user's comments. Google Cloud Vision API has been employed to perform image captioning of the underlined images, while similarity analysis has been employed to match user's comments to the obtained captioning results. The outcomes can trigger the development of subsequent policy research in tourism industry and behavior analysis. Besides, the Tourism48 dataset has been made available for research community.
Intermittent Pulling with Local Compensation for Communication-Efficient Distributed Learning

As a widely used iterative algorithm, the distributed Stochastic Gradient Descent (SGD) has shown great advances in training machine learning models due to the reduced time of the gradients computation. However, the huge number of iterations of SGD usually incurs huge communication cost on pushing local gradients and pulling global model that prohibits its further improvement over performance. In this paper, to reduce the number of pulling operations, a novel approach named Pulling Reduction with Local Compensation (PRLC) is proposed, in which each worker intermittently pulls the global model from the server and uses its local update to compensate the gap between the local model and the global model. Our rigorous theoretical analysis shows that the convergence rate of PRLC preserves the same order as the classical synchronous SGD for both strongly-convex and non-convex cases with good scalability due to the linear speedup with respect to the number of training nodes. Moreover, we also show that PRLC admits lower pulling frequency than the pulling reduction method without local compensation. The extensive experiments conducted on various models exhibit that our approach achieves a significant pulling reduction over the state-of-the-art methods, e.g., requiring only half of the pulling operations of LAG.
Perceptions of Future Teachers of Audiovisual Education and Communication. Challenges in Training for a Sustainable Education

The education of future teachers is going through a crisis related to audiovisual education. Following what is known as the 2015–2030 Agenda, the world of education is being confronted with a new learning model that must respond to the proposals of the Sustainable Development Goals—SDGs. In this article, we want to emphasize the need for training future teachers to meet the new needs of media education, taking into account the increasing importance of sustainable development of children in relation to audiovisual media to maintain their welfare, skills and future prospects. For this reason, the objective of the study is to collect the perceptions of 278 future schoolteachers about the significance of audiovisual education and their own training on the subject, the socio-educational objectives of audiovisual tools, their positioning regarding audiovisual methodology and the concept of communication. Two ad-hoc questionnaires with an ex-post-facto design were used. The findings showed that a reduced percentage of students felt they had received full audiovisual training. Nevertheless, over half of the future schoolteachers considered their work in the classroom to be important for social and educational ends, both from a theoretical and a practical viewpoint. We believe it is necessary to provide audiovisual education to future schoolteachers based on students’ interests and needs.
Modeling Internet Business Optimization Processes

Is considered a number of aspects of evolution of Internet business, in particular, assessment of efficiency of this innovation technology in terms of satisfaction of requests and needs of end users is implemented. The mathematical model is synthesized and modeling of set of processes is carried out. The analysis of results of such modeling allowed authors to formulate an entity of the concept “trust level” to information telecommunication technologies and to offer a way of its assessment, by definition of the so-called “index of trust of Internet users”. This index also to some extent reflects priorities of end users and can quite serve as a peculiar indicator of drivers of growth of the Internet in general and the Internet of things in particular.
Motion and trajectory planning modeling for mobile landing mechanism systems based on improved genetic algorithm

In many traditional soft-landing missions, researchers design the lander and the rover as two separate individuals, which has its limitations. At present, research on landers mainly focuses on the performance analysis of those who cannot move, and the motion of legged mobile lander has not yet been studied. In this paper, a novel Mobile Landing Mechanism (MLM) is proposed. Firstly, the monte-Carlo method is used to solve the workspace, and the motion feasibility of the mechanism is verified. Secondly, combining with the constraints of velocity, acceleration and secondary acceleration of each driving joint of the MLM, the trajectory of its joint space is planned by using cubic spline curve. And based on the weighted coefficient method, an optimal time-jerk pedestal trajectory planning model is established. Finally, by comparing the genetic algorithm (GA) with the adaptive genetic algorithm (AGA), an optimization algorithm is proposed to solve the joint trajectory optimization problem of the MLM, which can obtain better trajectory under constraints. Simulation shows that the motion performance of the mechanism is continuous and stable, which proves the rationality and effectiveness of the foot trajectory planning method.
Graph Neural Network and Multi-view Learning Based Mobile Application Recommendation in Heterogeneous Graphs

With the popularity of smartphones, mobile applications (mobile apps) have become a necessity in people's lives and work. Massive apps provide users with a variety of choices, but also bring about the information overload problem. In reality, the number of apps that users have used is very limited, resulting in a very sparse interaction matrix between users and apps. It is not accurate enough to use a sparse interaction matrix to predict numerous unknown ratings, so that the recommended results cannot satisfy users. This paper aims to exploit the user's historical behavior data and the app's side information to make app recommendation to solve the problem of information overload. Specifically, first of all, multiple semantic meta-graphs are designed by leveraging the user information, app information, user historical usage record information, and app's side information. Then, similarity matrices between users and apps based on different semantic meta-graphs are obtained. The graph neural network with the attention mechanism is employed to learn the collaborative information between users and apps, and to selectively aggregate the feature information of the neighbors. Finally, the multi-view learning and attention mechanism are adopted to obtain users' ratings for apps from different perspectives. Comprehensive experiments with different numbers of training samples show that the proposed method outperforms other comparison methods.
Microwave quantum illumination with a digital phase-conjugated receiver

Quantum illumination is a sensing technique that employs entangled signal-idler beams to improve the detection efficiency of low-reflectivity objects in environments with large thermal noise. The advantage over classical strategies is evident at low signal brightness, a feature which could make the protocol an ideal prototype for non-invasive scanning or low-power short-range radar. Here we experimentally investigate the concept of quantum illumination at microwave frequencies, by generating entangled fields using a Josephson parametric converter which are then amplified to illuminate a room-temperature object at a distance of 1 meter. Starting from experimental data, we simulate the case of perfect idler photon number detection, which results in a quantum advantage compared to the relative classical benchmark. Our results highlight the opportunities and challenges on the way towards a first room-temperature application of microwave quantum circuits.
Measuring the Degree of Suitability of Edge Detection Operators Prior to an Application

Unlike image restoration, image enhancement techniques are found to be subjective in nature as the appearance of an output image depends upon human perception. Hence, it is very difficult to determine the appropriateness of image enhancement techniques including edge detection operators prior to an application. This paper makes use of regression models to determine the suitability of edge detection operators before operators to be executed. With the existing operators, a novel Hybrid technique is used in the evaluation. The Hybrid detector is designed by combining Canny and Sobel operators with the gradient of texton image. This approach estimates a model as an objective function to determine the degree of proximity or suitability of edge detection operators under regression constraints on two publicly available databases, viz. the BSDS300 and the Multi-cue. The experimental results exhibit that the Hybrid edge detector outperforms other operators for measuring the proximity for appropriateness.
Joint Online Route Planning and Power Allocation for Multitarget Tracking in Airborne Radar Systems

Reasonable route planning and power allocation strategy for the airborne radar systems can sufficiently utilize the limited power and improve the multi-target tracking (MTT) performance. However, in the existing strategies, the route planning and power allocation are usually considered separately and the practical online requirements of route planning have not been addressed. In view of it, we propose a joint online route planning and power allocation (JORPPA) scheme for the task of the MTT in the airborne radar systems in this paper. The posterior Cramér-Rao lower bound (PCRLB) for the moving airborne system is derived and utilized as a constituent of the MTT performance metric. Then, the JORPPA algorithm is modeled as an optimization problem by using a scaled accuracy-based utility function as the objective function. The formulated problem is non-convex. We then propose a partition-based approach to solve it effectively. Numerical results verify the effectiveness of the proposed solution for MTT.
A hierarchical design for shared-control wheelchair navigation in dynamic environments

For people who have a mobility impairment and find standard wheelchairs unsuitable, a shared-controlled approach could provide a potential mobility solution. However, state-of-the-art research on shared control wheelchairs mainly focus on static environments. In this paper, we present a hierarchical design for our shared-controlled wheelchair using a velocity-based approach together with probabilistic shared control (PSC). By modifying the collision avoidance element and model the robot-pedestrian interaction based on their physical distance, we extended the implementation of PSC to dynamic environments. Our approach was tested in a Unity3D based simulator with human participants. It achieved least number of collisions while obtaining relatively low computational cost and high user agreement comparing with other state-of-the-art methods.
Robust underwater image enhancement method based on natural light and reflectivity

The poor visibility of underwater images is caused not only by scattering and absorption effects but is also related to light conditions. To improve robustness, a novel underwater image enhancement method based on natural light and reflectivity is proposed. Aiming at the scattering effects of reflectivity, a dehazing process based on the non-correlation of a foreground scene and background light is first conducted. Then, a more precise reflectivity can be estimated by substituting the captured image with the dehazed image. Moreover, classical methods often regard the dehazed image as the final result, but ignore the fact that attenuated natural light and nonuniform artificial light, which lead to insufficient brightness and halo effects, are included in the dehazed image, and are not robust to all scenes. This phenomenon enables us to remove the artificial light disturbance by introducing the dehazed image in the Lambertian model, and compensate for the loss of natural light energy by exploiting the light attenuation ratio map. Thus, the least-attenuated natural light can be further derived. Experimental results demonstrate that our method is satisfactory in producing more pleasing results under various circumstances.
High equilibrium optimizer for global optimization

With the advent of the information age, people have higher requirements for basic algorithms. Meta-heuristic algorithms have received wide attention as a high-level strategy to study and generate fully optimized solutions to data-driven optimization problems. Using the advantage of equilibrium optimizer (EO) with better balance mode, combined with the strategy of memetic algorithm, different proportion of temperature is introduced in different stages. That is, EO and thermal exchange optimization (TEO) are fused to obtain a new highly balanced optimizer (HEO). While keeping the guiding strategy and memory mode unchanged of EO, the accuracy of optimization is greatly improved. 14 well-known benchmark functions and 7 selective algorithms were used for HEO evaluation comparison experiments. On the basis of the fitness function curve, the optimal solution and other experimental data are tested statistically. The experimental results show that the improved algorithm has high accuracy and stability, but at the cost of running a little more time. Application testing of complex engineering problems is also one of the main purposes of algorithm design. In this paper, three typical engineering design problems (three truss, welded beam and rolling bearing design) are tested and the experimental results show that this algorithm has certain competitiveness and superiority in classical engineering design.
High-Resolution Cranial Implant Prediction via Patch-Wise Training

In this study, we proposed two methods for AutoImplant ( https://autoimplant.grand-challenge.org/ ) - the cranial implant design challenge. The shape of the implant is predicted based on the inputted defective skull. This task can be accomplished either by directly predicting the implant with the defective skull, or indirectly rebuilding the complete skull and then taking the difference between the defective and complete skulls. In our work, a deep learning model is applied to automatically predict the implant. In order to solve the problem that high resolution images can often not be directly inputted to the deep learning model, two proposed methods of resize and patch-based are examined. On the test set, the proposed resize method achieves an average dice similarity score (DSC) of 0.7350 and a Hausdorff distance (HD) of 7.2425 mm, while the proposed patch-based method achieves an average DSC of 0.8887 and a HD of 5.5339 mm.