Reviewerly/exHarmony huggingface dataset:
Determining the Opinions of University Students on the Education They Receive with Technology During the Pandemic Process

In this study, it was aimed to determine the opinions of university students studying in the Department of Electrical Engineering about the distance education process they received during the pandemic. The quantitative research method was used in the research. The research consisted of 390 volunteer university students studying at 16 universities in Russia; the universe of the research was determined as the distance education system and the sample was determined as ‘Microsoft Teams’. In order to collect the data of the research, a measurement tool called ‘technology use’ developed by the researchers was used and applied. For the measurement tool, help was received from experts in the field and have worked in these fields. The data were shared with university students via an online questionnaire and their participation was ensured and collected. When looking at the research, it was stated that university students frequently used their distance education centre infrastructure with Microsoft Teams infrastructure in the distance education process and they used Microsoft Teams elements in the activities. When the results of the research are discussed, they stated that the technology levels of the university students were high; that they learned the whiteboard and electric fields in the classroom activities; and that the topics covered and the questions solved in the lessons were high with the re-watch method.
Cavity-QED simulation of a quantum metamaterial with tunable disorder

We explore experimentally a quantum metamaterial based on a superconducting chip with 25 frequency-tunable transmon qubits coupled to a common coplanar resonator. The collective bright and dark modes are probed via the microwave response, i.e., by measuring the transmission amplitude of an external microwave signal. All qubits have individual control and readout lines. Their frequency tunability allows the number $N$ of resonantly coupled qubits to change and a disorder in their excitation frequencies to be introduced with preassigned distributions. While increasing $N$, we demonstrate the expected ${N}^{1/2}$ scaling law for the energy gap (Rabi splitting) between bright modes around the cavity frequency. By introducing a controllable disorder and averaging the transmission amplitude over a large number of realizations, we demonstrate a decay of mesoscopic fluctuations which mimics an approach towards the thermodynamic limit. The collective bright states survive in the presence of disorder when the strength of individual qubit coupling to the cavity dominates over the disorder strength.
Novel Method for Safeguarding Personal Health Record in Cloud Connection Using Deep Learning Models

It is a new online service paradigm that allows consumers to exchange their health data. Health information management software allows individuals to control and share their health data with other users and healthcare experts. Patient health records (PHR) may be intelligently examined to predict patient criticality in healthcare systems. Unauthorized access, privacy, security, key management, and increased keyword query search time all occur when personal health records (PHR) are moved to a third-party semitrusted server. This paper presents security measures for cloud-based personal health records (PHR). The cost of keeping health records on a hospital server grows. This is particularly true in healthcare. As a consequence, keeping PHRs in the cloud helps healthcare institutions save money on infrastructure. The proposed security solutions include an optimized rule-based fuzzy inference system (ORFIS) to determine the patient’s criticality. Patients are classified into three groups (sometimes known as protective rings) based on their severity: very critical, less critical, and normal. In trials using the UCI machine learning archive, the new ORFIS outperformed existing fuzzy inference approaches in detecting the criticality of PHR. Using a graph-based access policy and anonymous authentication with a NoSQL database in a private cloud environment improves data storage and retrieval efficiency, granularity of data access, and response time.
Adaptive Optimal Control of Chaotic System Using Backstepping Neural Network Concept

By the projection recurrent neural network-based adaptive backstepping control (PRNN-ABC), this paper stabilizes a class of uncertain chaotic system that includes unknown dynamics and disturbances. First, the backstepping control laws based on the nonlinear model is proposed. Second, the optimization problem studied by performance index and the control limitations. Third, the optimization problem is turned to a constrained quadratic programming (QP) in order to formulate recurrent neural network (RNN). later, the model of RNN is defined due to the Karush-Kuhn-Tucker (KKT) and the variational inequality concept. Finally, the stability of the closed-loop is investigated by Lyapunov function.
Formation of Fuzzy Patterns in Logical Analysis of Data Using a Multi-Criteria Genetic Algorithm

The formation of patterns is one of the main stages in logical data analysis. Fuzzy approaches to pattern generation in logical analysis of data allow the pattern to cover not only objects of the target class, but also a certain proportion of objects of the opposite class. In this case, pattern search is an optimization problem with the maximum coverage of the target class as an objective function, and some allowed coverage of the opposite class as a constraint. We propose a more flexible and symmetric optimization model which does not impose a strict restriction on the pattern coverage of the opposite class observations. Instead, our model converts such a restriction (purity restriction) into an additional criterion. Both, coverage of the target class and the opposite class are two objective functions of the optimization problem. The search for a balance of these criteria is the essence of the proposed optimization method. We propose a modified evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to solve this problem. The new algorithm uses pattern formation as an approximation of the Pareto set and considers the solution’s representation in logical analysis of data and the informativeness of patterns. We have tested our approach on two applied medical problems of classification under conditions of sample asymmetry: one class significantly dominated the other. The classification results were comparable and, in some cases, better than the results of commonly used machine learning algorithms in terms of accuracy, without losing the interpretability.
Object detectors involving a NAS-gate convolutional module and capsule attention module

Abstract Several state-of-the-art object detectors have demonstrated outstanding performances by optimizing feature representation through modification of the backbone architecture and exploitation of a feature pyramid. To determine the effectiveness of this approach, we explore the modification of object detectors’ backbone and feature pyramid by utilizing Neural Architecture Search (NAS) and Capsule Network. We introduce two modules, namely, NAS-gate convolutional module and Capsule Attention module. The NAS-gate convolutional module optimizes standard convolution in a backbone network based on differentiable architecture search cooperation with multiple convolution conditions to overcome object scale variation problems. The Capsule Attention module exploits the strong spatial relationship encoding ability of the capsule network to generate a spatial attention mask, which emphasizes important features and suppresses unnecessary features in the feature pyramid, in order to optimize the feature representation and localization capability of the detectors. Experimental results indicate that the NAS-gate convolutional module can alleviate the object scale variation problem and the Capsule Attention network can help to avoid inaccurate localization. Next, we introduce NASGC-CapANet, which incorporates the two modules, i.e., a NAS-gate convolutional module and capsule attention module. Results of comparisons against state-of-the-art object detectors on the MS COCO val-2017 dataset demonstrate that NASGC-CapANet-based Faster R-CNN significantly outperforms the baseline Faster R-CNN with a ResNet-50 backbone and a ResNet-101 backbone by mAPs of 2.7% and 2.0%, respectively. Furthermore, the NASGC-CapANet-based Cascade R-CNN achieves a box mAP of 43.8% on the MS COCO test-dev dataset.
Design of a Gough–Stewart Platform Based on Visual Servoing Controller

Designing a robot with the best accuracy is always an attractive research direction in the robotics community. In order to create a Gough-Stewart platform with guaranteed accuracy performance for a dedicated controller, this paper describes a novel advanced optimal design methodology: control-based design methodology. This advanced optimal design method considers the controller positioning accuracy in the design process for getting the optimal geometric parameters of the robot. In this paper, three types of visual servoing controllers are applied to control the motions of the Gough-Stewart platform: leg-direction-based visual servoing, line-based visual servoing, and image moment visual servoing. Depending on these controllers, the positioning error models considering the camera observation error together with the controller singularities are analyzed. In the next step, the optimization problems are formulated in order to get the optimal geometric parameters of the robot and the placement of the camera for the Gough-Stewart platform for each type of controller. Then, we perform co-simulations on the three optimized Gough-Stewart platforms in order to test the positioning accuracy and the robustness with respect to the manufacturing errors. It turns out that the optimal control-based design methodology helps get both the optimum design parameters of the robot and the performance of the controller {robot + dedicated controller}.
The Growth Model of Industrial Internet Platform in Industrial 4.0

The development of industrial Internet platform is an important carrier to realize the innovation of Industry 4.0. This paper analyzes the development mechanism of Chinese industrial Internet platforms from three aspects of motivation, incentives, and constraints, summarizes the influencing factors of platform development, and establishes a growth model of industrial Internet platform, to select representative factual data as observation variables to verify the effect of each factor. In the end, this paper puts forward relevant suggestions for the government, platform companies, and other related parties to drive the realization of Industry 4.0.
Intelligent detection of vulnerable functions in software through neural embedding‐based code analysis

Summary Software vulnerability is a fundamental problem in cybersecurity, which poses severe threats to the secure operation of devices and systems. In this paper, we propose a new vulnerability detection framework of employing advanced neural embedding. For example, CodeBERT is a large‐scale pre‐trained embedding model for natural language and programming language. It achieves state‐of‐the‐art performance on various natural language processing and code analysis tasks, demonstrating improved generalization ability compared with conventional models. The proposed framework encapsulates CodeBERT as a code representation generator and combines it with transfer learning to conduct cross‐project vulnerability detection. Considering the problem of lacking code embedding models on C source code, we extract the knowledge from C source code to fine‐tune the pre‐trained embedding model, so as to better facilitate the detection of function‐level vulnerabilities in C open‐source projects. To address the severe data imbalance issue in real‐world scenarios, we introduce code argumentation idea and use a large number of synthetic vulnerability data to further improve the robustness of the detection method. Experimental results show that the proposed vulnerability detection framework achieves better performance than existing methods.
The Role of Technology Integration in the Development of 21st Century Skills and Competencies in Life Sciences Teaching and Learning

Development of 21st century skills and competencies in teaching and learning remains a key strategic imperative. Coherent development of skills and competencies requires adoption of innovative pedagogical strategies. Technology integration can be harnessed to foster effective teaching and learning. The study examined the role of technology integration in the development of 21st century skills and competencies in Life Sciences teaching and learning. The empirical investigation adopted an explanatory sequential mixed method design and involved 15 purposively selected teachers from five South African suburban schools. The study is underpinned by social constructivism as the underlying theoretical framework. Quantitative data was collected through the administration of a survey questionnaire with the participants while qualitative data was collected through semi-structured interviews. Technology integration was perceived to promote the acquisition of 21st century skills and competencies in Life Sciences teaching and learning. In particular, the teachers indicated that technology integration facilitates the development of skills such as communication, critical thinking, collaboration, problem solving and computational thinking. In addition, technology integration was largely perceived to create exciting teaching and learning environment which fosters the enhancement of academic achievement and motivation of learners. Theoretical implications for technology-enhanced teaching and learning are discussed.
A Blockchain-Based Privacy Preservation Scheme in Mobile Medical

With the rapid development of mobile medical, how to establish an effective security mechanism to protect data security and privacy while users enjoy medical services has become an urgent problem to be solved. Aiming at the easy leakage of privacy in mobile medical terminals and untrustworthy data, we make use of a role-separated mechanism to generate trusted anonymous certificates. We propose a lightweight identity authentication scheme and adopt blockchain to protect the security of medical data. Meanwhile, in view of the problems of transparency and visibility of blockchain information, we adapt the searchable encryption algorithm to realize ciphertext processing in the whole life cycle. Experiments show that our scheme can reduce the cost of computation on the basis of ensuring traffic. In the process of dynamic updating of ciphertext keywords, except the keyword identifier, less information is leaked to the server, which protects privacy of users.
Toward Efficient Blockchain for the Internet of Vehicles with Hierarchical Blockchain Resource Scheduling

With the development of advanced information and communication technology, the traditional centralized cloud architecture cannot satisfy the exploding demand for data exchange in Internet of Vehicle (IoV) systems. Moreover, the traditional centralized architecture of the vehicular network has the potential risk of a single point of failure and lacks autonomy since the system highly relies on a trusted third party (TTP) to provide identity management. Fortunately, the emergence of blockchain technology provides a potential direction to address these problems. However, there are still some problems existing in the construction of an efficient blockchain system in IoV systems, such as the dynamic network topology and limited resources. In this paper, we propose a hierarchical resource scheduling scheme for blockchain-enabled IoV systems that improves the performance of the blockchain-enabled IoV system by efficiently allocating computational resources. The superiority of the proposed method is fully demonstrated by comparing it with existing baseline methods.
Coarse-to-Fine Embedded PatchMatch and Multi-Scale Dynamic Aggregation for Reference-Based Super-resolution

Reference-based super-resolution (RefSR) has made significant progress in producing realistic textures using an external reference (Ref) image. However, existing RefSR methods obtain high-quality correspondence matchings consuming quadratic computation resources with respect to the input size, limiting its application. Moreover, these approaches usually suffer from scale misalignments between the low-resolution (LR) image and Ref image. In this paper, we propose an Accelerated Multi-Scale Aggregation network (AMSA) for Reference-based Super-Resolution, including Coarse-to-Fine Embedded PatchMatch (CFE-PatchMatch) and Multi-Scale Dynamic Aggregation (MSDA) module. To improve matching efficiency, we design a novel Embedded PatchMacth scheme with random samples propagation, which involves end-to-end training with asymptotic linear computational cost to the input size. To further reduce computational cost and speed up convergence, we apply the coarse-to-fine strategy on Embedded PatchMacth constituting CFE-PatchMatch. To fully leverage reference information across multiple scales and enhance robustness to scale misalignment, we develop the MSDA module consisting of Dynamic Aggregation and Multi-Scale Aggregation. The Dynamic Aggregation corrects minor scale misalignment by dynamically aggregating features, and the Multi-Scale Aggregation brings robustness to large scale misalignment by fusing multi-scale information. Experimental results show that the proposed AMSA achieves superior performance over state-of-the-art approaches on both quantitative and qualitative evaluations.
Efficient and robust entanglement generation with deep reinforcement learning for quantum metrology

Quantum metrology exploits quantum resources and strategies to improve measurement precision of unknown parameters. One crucial issue is how to prepare a quantum entangled state suitable for high-precision measurement beyond the standard quantum limit. Here, we propose a scheme to find optimal pulse sequence to accelerate the one-axis twisting dynamics for entanglement generation with the aid of deep reinforcement learning (DRL). We consider the pulse train as a sequence of $\pi/2$ pulses along one axis or two orthogonal axes, and the operation is determined by maximizing the quantum Fisher information using DRL. Within a limited evolution time, the ultimate precision bounds of the prepared entangled states follow the Heisenberg-limited scalings. These states can also be used as the input states for Ramsey interferometry and the final measurement precisions still follow the Heisenberg-limited scalings. While the pulse train along only one axis is more simple and efficient, the scheme using pulse sequence along two orthogonal axes show better robustness against atom number deviation. Our protocol with DRL is efficient and easy to be implemented in state-of-the-art experiments.
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction

Distant supervision assumes that any sentence containing the same entity pairs reflects identical relationships.Previous works of distantly supervised relation extraction (DSRE) task generally focus on sentence-level or bag-level denoising techniques independently, neglecting the explicit interaction with cross levels.In this paper, we propose a Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.Specifically, we propose a three-level hierarchical learning framework to interact with cross levels, generating the de-noising context-aware representations via adapting the existing multihead self-attention, named Multi-Granularity Recontextualization.Meanwhile, pseudo positive samples are also provided in the specific level for contrastive learning via a dynamic gradient-based data augmentation strategy, named Dynamic Gradient Adversarial Perturbation.Experiments demonstrate that Hi-CLRE significantly outperforms strong baselines in various mainstream DSRE datasets.
Exploring Fourier Prior for Single Image Rain Removal

Deep convolutional neural networks (CNNs) have become dominant in the task of single image rain removal. Most of current CNN methods, however, suffer from the problem of overfitting on one single synthetic dataset as they neglect the intrinsic prior of the physical properties of rain streaks. To address this issue, we propose a simple but effective prior - Fourier prior to improve the generalization ability of an image rain removal model. The Fourier prior is a kind of property of rainy images. It is based on a key observation of us - replacing the Fourier amplitude of rainy images with that of clean images greatly suppresses the synthetic and real-world rain streaks. This means the amplitude contains most of the rain streak information and the phase keeps the similar structures of the background. So it is natural for single image rain removal to process the amplitude and phase information of the rainy images separately. In this paper, we develop a two-stage model where the first stage restores the amplitude of rainy images to clean rain streaks, and the second stage restores the phase information to refine fine-grained background structures. Extensive experiments on synthetic rainy data demonstrate the power of Fourier prior. Moreover, when trained on synthetic data, a robust generalization ability to real-world images can also be obtained. The code will be publicly available at https://github.com/willinglucky/ExploringFourier-Prior-for-Single-Image-Rain-Removal.
The Trusted Computing Base of the CompCert Verified Compiler

Abstract is the first realistic formally verified compiler: it provides a machine-checked mathematical proof that the code it generates matches the source code. Yet, there could be loopholes in this approach. We comprehensively analyze aspects of where errors could lead to incorrect code being generated. Possible issues range from the modeling of the source and the target languages to some techniques used to call external algorithms from within the compiler.
The USTC-Ximalaya System for the ICASSP 2022 Multi-Channel Multi-Party Meeting Transcription (M2met) Challenge

We propose two improvements to target-speaker voice activity detection (TS-VAD), the core component in our proposed speaker diarization system that was submitted to the 2022 Multi-Channel Multi-Party Meeting Transcription (M2MeT) challenge. These techniques are designed to handle multi-speaker conversations in real-world meeting scenarios with high speaker-overlap ratios and under heavy reverberant and noisy condition. First, for data preparation and augmentation in training TS-VAD models, speech data containing both real meetings and simulated indoor conversations are used. Second, in refining results obtained after TS-VAD based decoding, we perform a series of post-processing steps to improve the VAD results needed to reduce diarization error rates (DERs). Tested on the ALIMEETING corpus, the newly released Mandarin meeting dataset used in M2MeT, we demonstrate that our proposed system can decrease the DER by up to 66.55/60.59% relatively when compared with classical clustering based diarization on the Eval/Test set.