Reviewerly/exHarmony huggingface dataset:
Benchmarking universal quantum gates via channel spectrum

Noise remains the major obstacle to scalable quantum computation. Quantum benchmarking provides key information on noise properties and is an important step for developing more advanced quantum processors. However, current benchmarking methods are either limited to a specific subset of quantum gates or cannot directly describe the performance of the individual target gate. To overcome these limitations, we propose channel spectrum benchmarking (CSB), a method to infer the noise properties of the target gate, including process fidelity, stochastic fidelity, and some unitary parameters, from the eigenvalues of its noisy channel. Our CSB method is insensitive to state-preparation and measurement errors, and importantly, can benchmark universal gates and is scalable to many-qubit systems. Unlike standard randomized schemes, CSB can provide direct noise information for both target native gates and circuit fragments, allowing benchmarking and calibration of global entangling gates and frequently used modules in quantum algorithms like Trotterized Hamiltonian evolution operator in quantum simulation.
Voice-Face Homogeneity Tells Deepfake

Detecting forgery videos is highly desirable due to the abuse of deepfake. Existing detection approaches contribute to exploring the specific artifacts in deepfake videos and fit well on certain data. However, the growing technique on these artifacts keeps challenging the robustness of traditional deepfake detectors. As a result, the development of these approaches has reached a blockage. In this article, we propose to perform deepfake detection from an unexplored voice-face matching view. Our approach is founded on two supporting points: first, there is a high degree of homogeneity between the voice and face of an individual (i.e., they are highly correlated), and second, deepfake videos often involve mismatched identities between the voice and face due to face-swapping techniques. To this end, we develop a voice-face matching method that measures the matching degree between these two modalities to identify deepfake videos. Nevertheless, training on specific deepfake datasets makes the model overfit certain traits of deepfake algorithms. We instead advocate a method that quickly adapts to untapped forgery, with a pre-training then fine-tuning paradigm. Specifically, we first pre-train the model on a generic audio-visual dataset, followed by the fine-tuning on downstream deepfake data. We conduct extensive experiments over three widely exploited deepfake datasets: DFDC, FakeAVCeleb, and DeepfakeTIMIT. Our method obtains significant performance gains as compared to other state-of-the-art competitors. For instance, our method outperforms the baselines by nearly 2%, achieving an AUC of 86.11% on FakeAVCeleb. It is also worth noting that our method already achieves competitive results when fine-tuned on limited deepfake data.
YOLO v7-ECA-PConv-NWD Detects Defective Insulators on Transmission Lines

This paper proposes an enhanced YOLO v7-based method for detecting insulator defects in transmission lines, addressing the challenges of low accuracy and high leakage rates caused by complex backgrounds and electric poles alongside varying sizes of insulator targets in the image. Firstly, to address the issue of background interference and improve the importance of insulator features, a lightweight attention mechanism named Efficient Channel Attention (ECA) was introduced. With the incorporation of ECA, this model could effectively suppress background noise and provide more focus to insulator regions, thus enhancing its ability to detect insulator defects accurately. Secondly, a partial convolution (PConv) approach was employed in the backbone network instead of conventional convolution, which learned some important channels. This substitution improved both the network model’s accuracy and the training speed. Finally, the Normalized Wasserstein Distance (NWD) prevented insulator features from being lost during pre-feature extraction, which reduced the leakage rate and improved the detection accuracy of small target insulators and defective insulators. The experimental results demonstrate that the improved YOLO v7 network model achieved an average detection accuracy (mAP) of 98.1%, recall of 93.7%, and precision of 96.8% on the TISLTR dataset. On the FISLTR dataset, the average detection accuracy (mAP) for flashover insulators was 93%, with a recall of 92.3% and precision of 87.1%. The average detection accuracy (mAP) for broken insulators was 92.2%, with a recall of 90.3% and a precision of 95.2%. These metrics demonstrate significant improvements in both datasets, highlighting the proposed algorithms’ strong generalization capability and practicable potential to detect insulator targets.
Investigation into the Influence of Socio-Cultural Factors on Attitudes toward Artificial Intelligence

Abstract In this study, the influence of socio-cultural factors on attitudes toward artificial intelligence (AI) was investigated. In total, 1,677 Korean middle school students were selected to participate, and a test tool was used to measure the attitude toward AI. As a result, according to socio-cultural factors, middle school students’ attitudes toward AI were affected differently by gender- or AI-related experiences. In particular, students experiencing difficulties because of socio-cultural factors showed a more positive attitude toward AI if they had an AI education. On the other hand, interest toward AI and programming experience had a significant effect on attitudes toward AI and was not affected by socio-cultural factors. In particular, students with high interest toward AI or experience with block- and text-based programming languages showed significantly positive attitudes toward AI. Hence, the disparity in middle school students’ attitude toward AI according to socio-cultural factors was found.
Digital competences in chilean Year-1 university students from technical-vocational secondary education (TVSE) and scientific-humanistic secondary education (SHSE).

Abstract Digital competences are widely regarded as the key competences as defined by various international organizations, such as the European Commission (2018) and the OECD (2022). Given their paramount importance across all facets of contemporary society, a thorough examination of their developmental aspects becomes imperative. The study was conducted at a Chilean state-run university, which stands as the second largest institution in terms of student enrollment. Utilizing a quantitative survey-based methodology, this research study seeks to characterize the level of digital competences among Year-1 students pursuing undergraduate programs. Furthermore, it aims to compare the competence levels of two prominent student groups, distinguished by their distinct secondary schooling pathways, namely, technical-vocational education (TVSE) and scientific-humanistic education (SHSE). For the purpose of data collection, the CompDig-Ped test, aligned with the European framework for digital competences (DigComp), has been employed as the research instrument. The key findings of this study underscore significant disparities in digital competence levels between students originating from SHSE and TVSE secondary school trajectories, with TVSE students demonstrating comparatively lower levels of digital competence. These outcomes are congruent with existing research, which highlights the challenges encountered by technical-vocational secondary education in Chile over an extended period of time.
A Real-World Benchmark for Sentinel-2 Multi-Image Super-Resolution

Insufficient image spatial resolution is a serious limitation in many practical scenarios, especially when acquiring images at a finer scale is infeasible or brings higher costs. This is inherent to remote sensing, including Sentinel-2 satellite images that are available free of charge at a high revisit frequency, but whose spatial resolution is limited to 10m ground sampling distance. The resolution can be increased with super-resolution algorithms, in particular when performed from multiple images captured at subsequent revisits of a satellite, taking advantage of information fusion that leads to enhanced reconstruction accuracy. One of the obstacles in multi-image super-resolution consists in the scarcity of real-world benchmarks-commonly, simulated data are exploited which do not fully reflect the operating conditions. In this paper, we introduce a new benchmark (named MuS2) for super-resolving multiple Sentinel-2 images, with WorldView-2 imagery used as the high-resolution reference. Within MuS2, we publish the first end-to-end evaluation procedure for this problem which we expect to help the researchers in advancing the state of the art in multi-image super-resolution.
Contrastive Knowledge Amalgamation for Unsupervised Image Classification

Knowledge amalgamation (KA) aims to learn a compact student model to handle the joint objective from multiple teacher models that are specialized for their own tasks respectively. Current methods focus on coarsely aligning teachers and students in the common representation space, making it difficult for the student to learn the proper decision boundaries from a set of heterogeneous teachers. Besides, the KL divergence in previous works only minimizes the probability distribution difference between teachers and the student, ignoring the intrinsic characteristics of teachers. Therefore, we propose a novel Contrastive Knowledge Amalgamation (CKA) framework, which introduces contrastive losses and an alignment loss to achieve intra-class cohesion and inter-class separation. Contrastive losses intra- and inter- models are designed to widen the distance between representations of different classes. The alignment loss is introduced to minimize the sample-level distribution differences of teacher-student models in the common representation space. Furthermore, the student learns heterogeneous unsupervised classification tasks through soft targets efficiently and flexibly in the task-level amalgamation. Extensive experiments on benchmarks demonstrate the generalization capability of CKA in the amalgamation of specific task as well as multiple tasks. Comprehensive ablation studies provide a further insight into our CKA.
An assisted multi-frame approach for super-resolution in hyperspectral images of rock samples

Imaging spectroscopy is decisive for accurately characterizing rock samples for geological and mineral applications, since slight differences in mineral composition can be easily recognized in spectral signatures. Although, instrument limitations prevent high spatial resolution pixels scanned along hundreds of spectral channels, which considerably reduces the data capabilities. Multi frame Super-Resolution (SR) techniques can overcome this issue by retrieving high-quality images using purely computational methods. However, these approaches still have important drawbacks because of inherent uncertainties when estimating the spatial variation between frames, which strongly penalize the spectral information, essential for lithology. In this paper we propose an assisted framework to provide fully controlled motion parameters of the imaging process, skipping the intricate registration task. Essentially, we use a stepping device to successively change the position of rock samples for each new frame acquisition. With accurate knowledge about motion parameters, we were able to overcome the uncertainties regarding spatial registration between frames. Therefore, a reasonable number of equations connecting the low-resolution frames and the super resolved image could be used to solve the optimization problem. Extensive experiments proved that the proposed assisted method achieved best performances (both qualitatively and quantitatively) compared with similar approaches. Moreover, detailed spectral analysis showed almost absolute consistency between the original low-resolution image and the super-resolved result, which is crucial to guarantee reliable mineral characterization.
Etemadi reliability-based multi-layer perceptrons for classification and forecasting

Multi-layer perceptrons (MLPs) rank among the most popular and widely employed intelligent approaches for approximating the relationships between dependent and independent variables, demonstrating a wide range of successful applications. MLPs are flexible techniques capable of universally modeling and analyzing real-world problems in forecasting and classification domains with a desirable level of accuracy. In conventional MLPs, cost functions are formulated based on the error term. Subsequently, the learning process aims to estimate the unknown parameters to minimize the error-based cost function. The logic of the procedure is founded on the assumption that maximum generalization will be achieved from models displaying the highest accuracy within the training sample. While this learning process is rational and beneficial, a model's generalization capability depends simultaneously on the model's accuracy and the reliability level of that accuracy. In this manner, reliability is not taken into consideration when formulating the conventional cost function for MLPs. This paper introduces a reliability-based cost function for estimating the unknown weights and biases of MLP models during the learning process. The proposed cost function is designed to calculate the variation of the performances in dissimilar data situations. In the learning process of the proposed reliability-based multi-layer perceptron, in contrast to traditionally developed models, the goal is to minimize variation rather than error, or equivalently, to maximize reliability instead of accuracy. The generalizability of the proposed reliability-based MLP (EMLP) model in forecasting and classification domains is comprehensively evaluated using 30 benchmark data sets in each domain. Empirical results in the forecasting area indicate that, from a general perspective, the proposed EMLP model exhibits superior generalizability in 23 cases (76.67%) compared to traditional models. Furthermore, numerical results in the classification area reveal that the proposed model outperforms or matches the performance of the conventional MLP in 26 cases (86.67%). These outcomes clearly underscore the significance of reliability, a factor not considered in any of the conventional MLP modeling procedures. Therefore, the proposed MLP model represents a suitable alternative in modeling, especially when greater generalizability is desired.
Harnessing the power of virtual reality: Enhancing telepresence and inspiring sustainable travel intentions in the tourism industry

This study investigates how virtual reality (VR) can enhance telepresence and stimulate VR use and travel intentions in the sustainable tourism industry. 512 local and international tourists in Vietnam participated in an online survey after experiencing a VR experiment. They interacted with an app presenting a sustainable tourist destination based on nature, with the treatment group using VR sets and the control group viewing it on a 2D screen. Results indicate that VR-based tours facilitate greater telepresence than 2D presentations. VR is not a substitute for actual travel, with higher intentions reported to use VR in the future and to visit the tourist destination in real life due to their greater telepresence and perceived enjoyment from VR. This has implications for nature-based sustainable tourism, with further research needed for other types of tourism, including extreme activities.
Beyond Deep Fakes

A conceptual framework and research agenda for neural rendering of realistic digital faces.
Enforcing Privacy in Distributed Learning With Performance Guarantees

We study the privatization of distributed learning and optimization strategies. We focus on differential privacy schemes and study their effect on performance. We show that the popular additive random perturbation scheme degrades performance because it is not well-tuned to the graph structure. For this reason, we exploit two alternative graph-homomorphic constructions and show that they improve performance while guaranteeing privacy. Moreover, contrary to most earlier studies, the gradient of the risks is not assumed to be bounded (a condition that rarely holds in practice; e.g., quadratic risk). We avoid this condition and still devise a differentially private scheme with high probability. We examine optimization and learning scenarios and illustrate the theoretical findings through simulations.
Classifying and Benchmarking Quantum Annealing Algorithms Based on Quadratic Unconstrained Binary Optimization for Solving NP-Hard Problems

Quantum annealing has the potential to outperform classical transistor-based computer technologies in tackling intricate combinatorial optimization problems. However, ongoing scientific debates cast doubts on whether quantum annealing devices (or quantum annealers) can genuinely provide better problem-solving capabilities than classical computers. The question of whether quantum annealing algorithms (QAAs) running on quantum annealers have computational advantages over classical algorithms (CAs) running on classical computers still remains unclear. This paper aims to clarify the question by classifying and benchmarking QAAs that utilize quadratic unconstrained binary optimization (QUBO) formulas to solve NP-hard problems. It proposes a four-class classification of QUBO formulas and exemplifies each class by QUBO formulas used by QAAs for solving specific NP-hard problems, such as the subset sum, maximum cut, vertex cover, 0/1 knapsack, graph coloring, Hamiltonian cycle, traveling salesperson, and job shop scheduling problems. The classification is based on the following two criteria: (i) Does the number of QUBO variables scale linearly with the problem input size? (ii) Does the QUBO formula have both the constraint term and the optimization term? QAAs are implemented and run on a D-Wave quantum annealer for benchmarking. They are benchmarked against related classical algorithms (CAs) running on classical computers in terms of the quality of the solution and the time to the solution. The benchmarking results reveal which classes of QUBO formulas are likely to provide advantages to QAAs over CAs. Furthermore, based on the benchmarking results, observations and suggestions are given for each class of QUBO formulas, facilitating the adoption of appropriate actions to improve the performance of QAAs.
A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

There are many tricky optimization problems in real life, and metaheuristic algorithms are the most effective way to solve optimization problems at a lower cost. The dung beetle optimization algorithm (DBO) is a more innovative algorithm proposed in 2022, which is affected by the action of dung beetles such as ball rolling, foraging, and reproduction. Therefore, A dung beetle optimization algorithm is proposed based on quasi-oppositional learning and Q-learning (QOLDBO). First, the quantum state update idea is cleverly integrated into quasi-oppositional learning to increase the randomness of the generated population. And the best behavior pattern is selected by adding Q-learning in the rolling stage to improve the search effect. In addition, the variable spiral local domain method is proposed to make up for the shortage of developing only around the neighborhood optimum. For the optimal solution of each iteration, the dimensional adaptive Gaussian variation is selected and the optimal solution is retained. Experimental performance tests show that QOLDBO performs well in both benchmark test functions and CEC 2017. Simultaneously, the validity of the algorithm is verified on several classical practical application engineering problems.
En-MinWhale: An Ensemble Approach Based on MRMR and Whale Optimization for Cancer Diagnosis

According to the WHO, Cancer is a prominent cause of mortality worldwide, accounting for ~ 10 million fatalities at the end of 2020. The most common types of cancers include Lung, Breast, CNS, Leukemia, Colon, and Cervical Cancer. Early detection of cancer can decrease the death toll. According to the study, if the cancer is identified at its early stage, the death rate can be reduced to ~85%. In order to reduce the death toll, machine learning (ML) emerges as a significant solution. When it comes to cancer research with ML, biopsy and microarray data come into the front. The biopsy data is less useful as it excludes a patient’s genetic information. However, due to the genetic information, the microarray data emerges as a solution to detecting cancer disease. Dealing with microarray data also has some consequences, and high dimensionality is one of them. This article reports an ML-based ensemble model to tackle microarray data issues and provide an effective model for cancer detection. The reported model uses Minimum Redundancy Maximum Relevance (MRMR) as the feature selection algorithm. The Whale Optimization Algorithm (WOA) is implemented for the featured dataset to select the optimistic number of features without affecting microarray data relevance. Then, four ML-based classification models, including Support Vector Machine, Decision Tree, Multi-Layer Perceptron, and Random Forest, are applied as the base learners to make initial predictions. Finally, the voting ensemble technique is applied to the initial prediction to develop an ensemble model for cancer prediction. The proposed En-MinWhale model is evaluated over six different types of cancer microarray datasets, including Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer. Finally, the performance of the proposed model is evaluated using 11 various evaluative parameters, including accuracy, precision, specificity, sensitivity, F-β score, etc. The En-MinWhale model shows 94.09%, 95.83%, 94.86%, 95.00%, 94.85%, and 96.77% accuracy for Lung, Leukemia, Ovarian, CNS, Breast, and Colon Cancer datasets, respectively, that outperforms other considered hybrid models and can help out the physicians in Cancer diagnosis.
A Blockchain-based Architecture for National Population Census

The national population census plays a crucial role in providing a comprehensive understanding of a country's development and aids in policymaking and planning. However, any shortcomings in the census system can have adverse effects on the country. Our previous research has highlighted several flaws in the current census systems, such as inadequate population coverage, racial and ethnic biases, data privacy and security issues, and problems with data distribution. These issues can be mitigated through the implementation of Blockchain technology. By using a Blockchain-based system for conducting the national population census, transparency, privacy, and authenticity can be ensured while maintaining data integrity, security, efficiency, and trustworthiness. This paper builds upon our ongoing research that identifies the primary challenges the current centralized census systems face through a systematic literature review and structured interviews conducted with Pakistan's national census bureau. In this paper, we present a Blockchain-based system architecture that addresses the identified challenges and meets the requirements of Pakistan's national census bureau.