Reviewerly/exHarmony huggingface dataset:
Design and Test of an Integrated Random Number Generator with All-Digital Entropy Source

In the cybersecurity field, the generation of random numbers is extremely important because they are employed in different applications such as the generation/derivation of cryptographic keys, nonces, and initialization vectors. The more unpredictable the random sequence, the higher its quality and the lower the probability of recovering the value of those random numbers for an adversary. Cryptographically Secure Pseudo-Random Number Generators (CSPRNGs) are random number generators (RNGs) with specific properties and whose output sequence has such a degree of randomness that it cannot be distinguished from an ideal random sequence. In this work, we designed an all-digital RNG, which includes a Deterministic Random Bit Generator (DRBG) that meets the security requirements for cryptographic applications as CSPRNG, plus an entropy source that showed high portability and a high level of entropy. The proposed design has been intensively tested against both NIST and BSI suites to assess its entropy and randomness, and it is ready to be integrated into the European Processor Initiative (EPI) chip.
Evaluation Method of IT English Blended Teaching Quality Based on the Data Mining Algorithm

As the mixed education model gradually becomes widespread in various universities in Japan, the evaluation of the quality of IT English mixed education has become a very important issue, and it is worth considering the corresponding evaluation method. In this paper, we use a data mining algorithm to implement an evaluation method for the interconversion of quantitative data and qualitative concepts and use the IT English mixed teaching model to evaluate and analyze the teaching quality of the course. The evaluation method is feasible and provides a mixing method. Evaluation of the quality of education. Reference method.
Syntax and models of Cartesian cubical type theory

Abstract We present a cubical type theory based on the Cartesian cube category (faces, degeneracies, symmetries, diagonals, but no connections or reversal) with univalent universes, each containing Π, Σ, path, identity, natural number, boolean, suspension, and glue (equivalence extension) types. The type theory includes a syntactic description of a uniform Kan operation, along with judgmental equality rules defining the Kan operation on each type. The Kan operation uses both a different set of generating trivial cofibrations and a different set of generating cofibrations than the Cohen, Coquand, Huber, and Mörtberg (CCHM) model. Next, we describe a constructive model of this type theory in Cartesian cubical sets. We give a mechanized proof, using Agda as the internal language of cubical sets in the style introduced by Orton and Pitts, that glue, Π, Σ, path, identity, boolean, natural number, suspension types, and the universe itself are Kan in this model, and that the universe is univalent. An advantage of this formal approach is that our construction can also be interpreted in a range of other models, including cubical sets on the connections cube category and the De Morgan cube category, as used in the CCHM model, and bicubical sets, as used in directed type theory.
Hybrid collaborative filtering model for consumer dynamic service recommendation based on mobile cloud information system

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service recommendation systems mainly addresses two problems: prediction and completion of sparse QoS data, and the user's personalized recommendation. To address the issue of high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, this study presents a hybrid collaborative filtering model for consumer service recommendation based on mobile cloud by introducing user preferences. The example verified that the service recommendation based on the model can effectively reduce the data sparsity and increase the accuracy of the prediction.
Empirically developed framework for building trust in distributed agile teams

Organizations are adopting agile practices in distributed software development in order to develop quality software in less time. Using agile software development in distributed set up has its own set of challenges pertaining to face to face interactions, collaboration, time zone and cultural differences. A strong presence of trust helps to overcome these challenges. A relatively lesser number of empirical studies on multidimensional perspective of trust in distributed agile software development has motivated this study. This study aims to develop a comprehensive framework to build trust in distributed agile teams. This study is based on Grounded Theory research methodology which involves 40 agile practitioners from diverse domains belonging to 19 different software organizations located across seven different countries. Besides, observations in two different software organizations were also performed to gather data. Data has been gathered in the form of semi-structured interviews and field notes. Qualitative data analysis resulted into five different contributing categories for building trust amongst distributed agile teams. These categories represent multidimensional perspectives that influence trust building amongst agile team members working across different parts of the world. This study culminates into a framework for building trust in distributed agile teams. The proposed framework has been developed empirically and has five components that influence trust building. These components are related to working environment, leadership, organizational, personal and socio cultural perspectives. The multidimensional perspective of trust was investigated from an agile practitioners view through their real-life project experiences. Organizations and software practitioners may utilize the results of this study to create a hospitable environment for building trust while practicing agile in a distributed environment.
A Blockchain Based User Subscription Data Management and Access Control Scheme in Mobile Communication Networks

In mobile communication networks, when a user roams to and accesses a foreign network, the foreign operator needs to request the user’s subscription data from a centralized authentication server, which is managed by the user’s home operator. However, centralized authentication introduces single point of failure. Meanwhile, the real-time participation of the home operator and the trust relationship between the foreign operator and the home operator are difficult to guarantee. In this paper, by adopting blockchain and smart contracts, we propose a secure and efficient access control scheme of user subscription data in roaming scenarios. We further design a flexible user authentication scheme, which utilizes derivable tokens based on the proposed access control scheme. By using blockchain to store and manage user subscription data, access control can be decentralized without any trusted third party. Besides, by implementing automatic verification of access privilege through smart contracts, the limitation of the home operators’ real-time participation is eliminated. In addition, to further improve security and reduce the on-chain storage overhead, we optimize the data encryption and storage scheme utilizing threshold secret sharing. Our security and performance analysis show that the proposed user subscription data access control scheme for roaming service provides high-level security while causing acceptable time and storage overhead.
Secure and Efficient Certificate-Based Proxy Signature Schemes for Industrial Internet of Things

To improve the security of industrial Internet of things (IIoT), several concrete constructions of certificate-based proxy signature (CBPS) scheme without bilinear pairing were proposed in the last few years. However, many previous constructions were found impractical, either fail to meet the claimed security properties or contain design flaws. In particular, in some instances, a malicious proxy signer can claim to be the original signer by forwarding the messages from original signer to any proxy signer, and the receiver cannot determine whether the delegation of signing power has been reused. In this article, we first demonstrate some security issues and design flaws in the previous proposals of CBPS scheme. As follows, to further address the above deficiencies, three new constructions of CBPS scheme with improved security are introduced. In the first two proposals, the delegation validity can be verified by the designated verifier (proxy signer), and the delegation of signing power for the last construct is publicly verifiable (any signer). Furthermore, formal security proofs are given using forking lemma in the random oracle, assuming that the discrete logarithm problem is hard. Compared with the previous CBPS schemes, our constructions are efficient with respect to computation and communication. Finally, we discuss the two necessary conditions for constructing secure CBPS schemes, to avoid security flaws in future research.
Visual Recognition of Traffic Signs in Natural Scenes Based on Improved RetinaNet

Aiming at recognizing small proportion, blurred and complex traffic sign in natural scenes, a traffic sign detection method based on RetinaNet-NeXt is proposed. First, to ensure the quality of dataset, the data were cleaned and enhanced to denoise. Secondly, a novel backbone network ResNeXt was employed to improve the detection accuracy and effection of RetinaNet. Finally, transfer learning and group normalization were adopted to accelerate our network training. Experimental results show that the precision, recall and mAP of our method, compared with the original RetinaNet, are improved by 9.08%, 9.09% and 7.32%, respectively. Our method can be effectively applied to traffic sign detection.
Global Perception Network for Salient Object Detection in Remote Sensing Images

Despite recent works that have achieved remarkable progress on salient object detection for natural scene images, to detect various types and scales of objects, complex backgrounds in remote sensing images are still challenging. In this study, a novel global perception network (GPNet) is constructed for the salient object detection of remote sensing images. The proposed GPNet includes a global perception module (GPM), an axial attention block (AAB), and a feature distillation structure (FDS). The GPM is used to preserve the relationships of the entire dataset, the AAB is designed to capture the dependencies between the space and channel, the FDS is introduced to enable the helpful multilevel information flow into deep layers to enhance feature generation, and the global and the local attention information are mutually fused to enhance the network mode. Extensive experiments on three public datasets demonstrate that the proposed method outperforms other compared state-of-the-art methods both qualitatively and quantitatively (<uri>https://github.com/liuyu1002/GPnet</uri>).
Hyperspectral Image Restoration With Self-Supervised Learning: A Two-Stage Training Approach

Hyperspectral image (HSI) denoising is a crucial preprocessing task to improve the performance of the subsequent HSI interpretation and applications. With recent progress in deep learning, HSI denoising methods based on deep neural networks have attracted increasing interest and achieved the state-of-the-art performance. Nevertheless, most of these methods are based on network structures originally developed for grayscale and color images and require a change of network structure to be applicable to HSIs. The new network architectures often lead to complicated models and limited flexibility, which, in turn, result in difficulty in learning and demand of a large number of training samples. In this article, we propose an innovative two-stage learning method including pretraining and fine-tuning procedures. In the first stage, a denoising convolutional neural network can be pretrained with pairs of corrupted and clean images. In the second stage, the pretrained network is fine-tuned via a self-supervised learning strategy to capture the spectral correlation in HSIs. The training pairs in the second stage are constructed from the neighboring band images in the target noisy HSI, leading to a novel idea of embedding spectral information into denoiser through the target image, rather than the change of the network architecture. This model has strong adaptability such that many image denoising networks can be easily adopted for HSIs, while the external hyperspectral training set is optional but not mandatory. Experimental results show that our method has competitive performance compared with the state-of-the-art approaches.
Temporal‐enhanced graph convolution network for skeleton‐based action recognition

Graph convolution networks (GCNs) have drawn attention for skeleton-based action recognition. They have achieved remarkable performance by adaptively learning spatial features of human action dynamics. However, the existing methods are limited in temporal sequence modelling of human actions. To give adequate consideration to temporal factors in action modelling, a novel temporal-enhanced graph convolution network is presented. First, a Causal Convolution layer is introduced to ensure no future information leakage at each time step for keeping ordering information of inputs. Second, a novel cross-spatial-temporal graph convolution layer that extends an adaptive graph from the spatial to the temporal domain to capture local cross-spatial-temporal dependencies among joints is presented. Third, a temporal attention layer is designed to enhance the modelling capability of long-range temporal dependencies, helping the network to directly focus on important time steps. Experimental results on three large-scale datasets, NTU-RGB + D, Kinetics-Skeleton, and UAV-Human, indicate that the authors’ network achieves accuracy improvement with better generalisation capability over previous methods. The authors’ code and data are available at https://github.com/xieyulai/TE-GCN.
Privacy Preservation in Patient Information Exchange Systems Based on Blockchain: System Design Study

With the increasing sophistication of the medical industry, various advanced medical services such as medical artificial intelligence, telemedicine, and personalized health care services have emerged. The demand for medical data is also rapidly increasing today because advanced medical services use medical data such as user data and electronic medical records (EMRs) to provide services. As a result, health care institutions and medical practitioners are researching various mechanisms and tools to feed medical data into their systems seamlessly. However, medical data contain sensitive personal information of patients. Therefore, ensuring security while meeting the demand for medical data is a very important problem in the information age for which a solution is required.Our goal is to design a blockchain-based decentralized patient information exchange (PIE) system that can safely and efficiently share EMRs. The proposed system preserves patients' privacy in the EMRs through a medical information exchange process that includes data encryption and access control.We propose a blockchain-based EMR-sharing system that allows patients to manage their EMRs scattered across multiple hospitals and share them with other users. Our PIE system protects the patient's EMR from security threats such as counterfeiting and privacy attacks during data sharing. In addition, it provides scalability by using distributed data-sharing methods to quickly share an EMR, regardless of its size or type. We implemented simulation models using Hyperledger Fabric, an open source blockchain framework.We performed a simulation of the EMR-sharing process and compared it with previous works on blockchain-based medical systems to check the proposed system's performance. During the simulation, we found that it takes an average of 0.01014 (SD 0.0028) seconds to download 1 MB of EMR in our proposed PIE system. Moreover, it has been confirmed that data can be freely shared with other users regardless of the size or format of the data to be transmitted through the distributed data-sharing technique using the InterPlanetary File System. We conducted a security analysis to check whether the proposed security mechanism can effectively protect users of the EMR-sharing system from security threats such as data forgery or unauthorized access, and we found that the distributed ledger structure and re-encryption-based data encryption method can effectively protect users' EMRs from forgery and privacy leak threats and provide data integrity.Blockchain is a distributed ledger technology that provides data integrity to enable patient-centered health information exchange and access control. PIE systems integrate and manage fragmented patient EMRs through blockchain and protect users from security threats during the data exchange process among users. To increase safety and efficiency in the EMR-sharing process, we used access control using security levels, data encryption based on re-encryption, and a distributed data-sharing scheme.
More is Less: Domain-Specific Speech Recognition Microprocessor Using One-Dimensional Convolutional Recurrent Neural Network

Low-power keywords recognition has been a focus of acoustic signal processing for several decades. This work investigates the domain-specific speech recognition microprocessor based on optimized one-dimensional convolutional recurrent neural network (1D-CRNN). Compared to previous DNN based frameworks, the proposed 1D-CRNN can process both the feature extraction and keywords classification, and achieve high recognition accuracy with reduced computation operations under wide range background noise SNRs. An energy-efficient 1D-CRNN accelerator is implemented to dynamically reconfigure and process the different layers. This accelerator has the characteristics of “More is Less” in three aspects: 1) the hybrid network with more complex layers is much more compact and requires less computation; 2) although the weight width quantized to 8 bits requires more memory size and multiplication energy cost, the required network neurons can be reduced and hardware utilization can be improved; 3) an energy-aware self-compensation tensor multiplication unit with dual power supply based on approximation design method can be utilized for 1D-CRNN computing. Compared to the state-of-the-art architectures, the novel more-is-less architecture can achieve a much lower power consumption of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1.4~\mu \text{W}\sim 2.1~\mu \text{W}$ </tex-math></inline-formula> (over 80% reduced) under an industry 22nm technology, while maintaining higher system adaptability (support SNRs: −5dB~Clean) for 1~5 real-time keywords recognition.
GS-QRNN: A High-Efficiency Automatic Modulation Classifier for Cognitive Radio IoT

Cognitive radio (CR) has been introduced into the Internet of Things (IoT) domain as a potential solution to the critical spectrum resource shortage caused by a dramatic increase of IoT solutions within a limited operational spectrum. One technique of use within traditional CR processing for sensing communication activity across a spectrum of interest is automatic modulation classification (AMC). However, utilizing AMC within resource-limited cognitive radio-enabled IoT (CR-IoT) devices poses a significant challenge. In this article, we present a high-efficiency automatic modulation classifier architecture whose core is built upon the implementation of stacking quasirecurrent neural network (S-QRNN) layers acting as a feature extraction stage. This architecture utilizes the low-latency feature extraction capability of convolutional layers, and a minimalist recurrent pooling function that mimics recurrent-layer operations to aggregate the extracted features over time steps for higher classification accuracy. Additionally, implementing dense layers between two consecutive QRNN layers helps keep the network growth rate low. Therefore, the proposed S-QRNN classifier overall exhibits higher efficiency, fitting well within the limitations of CR-IoT devices. In order to demonstrate higher performance efficiency of our classifier, we conduct a comprehensive evaluation of our approach against the state-of-the-art AMC classifiers. Our analysis metrics of the classifier performance focuses on trainability, classification accuracy, execution latency, and resulting overall efficiency. Our results demonstrate that our proposed S-QRNN classifier exhibits higher trainability and, on average, a 75.83% higher efficiency, while it has, on average, a 26.54% lower execution latency. We then expand upon S-QRNN’s foundation by introducing gated recurrent units into our classifier to initially extract temporal features of the received constellations. The resulting GS-QRNN classifier demonstrates an efficiency increase by an average of 191% compared to the gated-enhanced recurrent-neural-network-based AMC classifier, with our GS-QRNN classifier average execution latency being 59.31% lower.
Salient keypoint-based copy–move image forgery detection

Copy–move forgery is one of the most common image forgeries in digital images. In copy–move forgery, some object or region of an object is copied and duplicated in other parts of the same image. Hence, there is a need to develop an accurate and robust forgery detection approach for various image forensics applications. In this article, an improved salient keypoint selection approach for copy–move forgery detection has been proposed. Scale-Invariant Feature Transform (SIFT) and KAZE keypoint features have been extracted from the input image and salient keypoints have been selected for improving the robustness of the proposed algorithm. Salient keypoint selection reduces the feature descriptor matching time for finding region duplication in the given image. To improve the detection accuracy of the proposed approach, selective search-based region proposals have been introduced to create a bounding box on the input image. Feature descriptor matching is performed between keypoints which are located inside two different bounding boxes. The proposed approach has been evaluated on two benchmark datasets, CoMoFoD and MICC-F220 and detection results outperformed state-of-the-art techniques under different geometric transformations and post-processing operations.