Reviewerly/exHarmony huggingface dataset:
The Model Framework of College Students’ Entrepreneurial Team Formation Based on Relevance Algorithm

Reasonable realization of the task relevance of the entrepreneurial team formation model is a research hotspot in the field of cluster computing. Based on the theoretical basis of the correlation algorithm, this article constructs a parallel computing framework for college students’ entrepreneurial formation model and realizes the application of the team formation model in the entrepreneurial model. This article replaces the traditional structure with the multi-entrepreneurial formation mode structure to avoid premature convergence and initializes the entrepreneurial formation mode with an algorithm, which solves the problem of the first-come, first-served entrepreneurial association degree algorithm of the entrepreneurial formation mode. During the simulation process, the multilevel entrepreneurial relevance algorithm based on the relevance algorithm was experimentally verified and performance tested. In the performance comparison test, the optimal solution search efficiency increased to 89.7%, the heterogeneous fault tolerance rate of resources reached 0.713, and the task set scheduling time was reduced to 0.221s. The experimental results show that the multilevel entrepreneurial relevance model based on the relevance algorithm can effectively reduce the task set scheduling time and relevance cost, and can take into account the load balance of the system, effectively promoting the diversity and dynamics of resource requirements.
User Personalized Recommendation Algorithm Based on GRU Network Model in Social Networks

Efficient and accurate personalized recommendation algorithms can effectively improve user experience satisfaction, in order to improve the performance of user personalized recommendation algorithm, this study proposes a user personalized recommendation algorithm based on deep learning network. The algorithm uses Gate Recurrent Unit (GRU) network to build the main model of personalized recommendation algorithm to reduce the influence of over fitting of multi-layer network; The attention mechanism is introduced into GRU network, so that the recommendation model can obtain the feature information of user data more accurately and reduce the influence of irrelevant data on the model; At the same time, due to the introduction of variable length mini-batch allocation method, the model training data is more complete and reliable, which can effectively improve the accuracy of user personalized recommendation. The simulation experiment is based on Amazon dataset and MovieLens dataset. The experimental results show that the proposed method has good personalized recommendation ability.
Error Concealment in the Density Field of a Spatiotemporal Image Sequence

One of the most difficult challenges of multimedia transmission during the last two decades has been the retrieval of degraded or missing regions of images and videos while maintaining satisfactory perceptual accuracy. The objective is to retrieve lost data by using the similarity between frames. Usually, error concealment (EC) schemes depend on replacing incorrect data with data that are identical to the initial. This is possible because video contains a high degree of self-similarity. This research focuses on applying an EC approach in transform-domain video sequences. To conduct EC on films, they must first be translated to frames and then transformed using one of the available transformations into frequency-domain images. Using successive frames, it is possible to recover lost or incorrect data from images. Intra-coded frames (I-frames) may be used to recreate lost knowledge in predictive (P-frames) and bidirectional predictive frames (B-frames). I-frame knowledge that has been lost may be restored using previous intra-coded frames. The use of wavelet error concealment generated more precise results than the other techniques. In this study, it was discovered that covering faults in the density sector with wavelets produces more reliable results than the other techniques.
Autonomous Car based on Neural Network

Autonomous vehicles will frame the foundation of future cutting edge in transportation giving travel relaxation, road protection, alongside various worth added services. Such a change which will be fueled by boosts in technologies for machine learning and wireless communication will empower a future vehicular biological system that is better highlighted and more proficient. These days, endeavors are being made to give insignificant control to man over the vehicle. The point is to decrease fender benders and give individuals an agreeable climate which is the justification for turning into an autonomous vehicle. The benefits of autonomous vehicles, like less car accidents, expanded dependability, expanded street limit, diminished gridlock just as decrease of traffic police and care protection, despite the fact that we need to beat the issues of network safety, software dependability, and loss of driver related jobs. Our system is focused on a vehicle that is agreeable and affordable for people. Our autonomous car will navigate itself through the track with the help of computer vision technique. User will be capable to observe the stay view from the webcam so that it will be connected to the car. Lane detection algorithm is used to provide the required control to the car that is built through neural networks. People are free to concentrate on other work or to relaxation all through their trips.
DR-STRaNGe: End-to-End System Design for DRAM-based True Random Number Generators

Random number generation is an important task in a wide variety of critical applications including cryptographic algorithms, scientific simulations, and industrial testing tools. True Random Number Generators (TRNGs) produce cryptographically-secure truly random data by sampling a physical entropy source that typically requires custom hardware and suffers from long latency. To enable high-bandwidth and low-latency TRNGs on widely-available commodity devices, recent works propose hardware TRNGs that generate random numbers using commodity DRAM as an entropy source. Although prior works demonstrate promising TRNG mechanisms using DRAM, practical integration of such mechanisms into real systems poses various challenges.We identify three key challenges for using DRAM-based TRNGs in current systems: (1) generating random numbers with DRAM-based TRNGs can degrade overall system performance by slowing down concurrently-running applications due to the interference between RNG and regular memory operations in the memory controller (i.e., RNG interference), (2) this RNG interference can degrade system fairness by causing unfair prioritization of applications that intensively use random numbers (i.e., RNG applications), and (3) RNG applications can experience significant slowdown due to the high latency of DRAM-based TRNGs.To address these challenges, we propose DR-STRaNGe, an end-to-end system design for DRAM-based TRNGs that (1) reduces the RNG interference by separating RNG requests from regular memory requests in the memory controller, (2) improves fairness across applications with an RNG-aware memory request scheduler, and (3) hides the large TRNG latencies using a random number buffering mechanism combined with a new DRAM idleness predictor that accurately identifies idle DRAM periods.We evaluate DR-STRaNGe using a comprehensive set of 186 multi-programmed workloads. Compared to an RNG-oblivious baseline system, DR-STRaNGe improves the performance of non-RNG and RNG applications on average by 17.9% and 25.1%, respectively. DR-STRaNGe improves system fairness by 32.1% on average when generating random numbers at a 5 Gb/s throughput. DR-STRaNGe reduces energy consumption by 21% compared to the RNG-oblivious baseline design by reducing the time spent for RNG and non-RNG memory accesses by 15.8%.
Wavelet Sparse Coding-Based Lightweight Networks for Image Superresolution

Image superresolution (ISR) is a hot topic. With the success of deep learning, the convolutional neural network-based ISR makes great progress recently. However, most state-of-the-art networks contain millions of parameters and hundreds of layers. It is difficult to apply models realistically. To solve this problem, we propose a Wavelet Sparse Coding-based Lightweight Network for Image Superresolution (WLSR). Our contributions include four aspects. Firstly, to improve the ISR performance, the WLSR utilizes the superiorities of wavelet sparse coding on ISR. Secondly, we take advantage of the dilated convolution to expand receptive fields. In this case, the filters in WLSR can acquire more information than common convolutional filters from input images. Thirdly, to deal with sparse code efficiently, we employ deformable convolution networks to obtain the convolutional kernels that concentrate on nonzero elements. Fourthly, to make WLSR uint8 quantization robust, we take advantage of the Clipped ReLU activation in the end of WLSR and balance the SR performance with running time. Experimental results indicate that, compared with state-of-the-art lightweight models, the WLSR can achieve exceptional performance with few parameters. Moreover, the WLSR contains 30 times fewer parameters than VDSR, but it works better than VDSR on the validation set.
Generative and discriminative infinite restricted Boltzmann machine training

As one of the essential deep learning models, a restricted Boltzmann machine (RBM) is a commonly used generative training model. By adaptively growing the size of the hidden units, infinite RBM (IRBM) is obtained, which possesses an excellent property of automatically choosing the hidden layer size depending on a specific task. An IRBM presents a competitive generative capability with the traditional RBM. First, a generative model called Gaussian IRBM (GIRBM) is proposed to deal with practical scenarios from the perspective of data discretization. Subsequently, a discriminative IRBM (DIRBM) and a discriminative GIRBM (DGIRBM) are established to solve classification problems by attaching extra-label units next to the input layer. They are motivated by the fact that a discriminative variant of an RBM can complete an individual framework for classification with better performance than some standard classifiers. Remarkably, the proposed models retain both generative and discriminative properties synchronously, that is, they can reconstruct data effectively and be established in considerable self-contained classifiers. The experimental results on image classification (both large and small), text identification, and facial recognition (both clean and noisy) reflect that a DIRBM and a DGIRBM are superior to some state-of-the-art RBM models in terms of the reconstruction error and the classification accuracy. Intuitively, they require models to avoid utilizing more hidden units than needed when confronted with various sizes of data, prioritizing smaller networks. In addition, the proposed models behave more robustly than other classic classifiers when dealing with noisy facial recognition.
A Note on Blockchain Authentication Methods for Mobile Devices in Healthcare

The past couple of decades witnessed a tremendous expansion in the IoT world that gathers now billions of devices, sensors, users and transactions.The aspirations of ubiquitous computing have drastically changed the computing world, from a parallel point of view, to distributed, then grid and cloud computing -all these just to keep up with the proliferation of devices and the users' expectations.Alongside with this fast development, many issues appeared, especially in terms of scalability and security.Regardless of protocols, devices, applications or technologies used, critical data will be involved and, therefore, vulnerabilities that can affect the performance of the system or allows them to be maliciously exploited.The higher the number of devices, the more constraints appear and along with these constraints the existing models and technologies become overwhelmed and simply not enough.The size of the IoT and its autonomous character make it impossible to sustain and implement a centralized authentication system.Therefore, to allow reliable peer authentication and to approach a trust level management, we propose discussing a model based on blockchain technology.Blockchain is a revolutionary technology, modeled by a linear sequence of blocks, considered to be the future of wireless networks security (Li et al., 2018).We rely on this new data structure to address two major components of security in mobile networks: authentication and trust.
Practical Connectionist Temporal Classification for accurate and efficient training

In the field of speech recognition based on Recurrent Neural Networks (RNNs), a speech signal is transcribed into alphabets and words. Because RNNs are good at sequence learning, they are deployed for speech to language transcription tasks. The challenging and difficult requirement of pre-segmentation prior to labelling of training data while training RNNs for such tasks was overcome by the method of Connectionist Temporal Classification (CTC) which enabled training with unsegmented acoustic signals. The CTC algorithm is compute-intensive and is prone to accuracy issues unless extreme care is taken. In this paper, we present the practical aspects which influence the accuracy of CTC algorithm during training. We also propose variation to the algorithm which is robust to the accuracy and precision issues. The proposed method also demonstrates computational advantage with respect to the existing practical implementation. Simulation results depict the accuracy and computational advantage of the proposed method over existing practical implementation of CTC algorithm.
Small target detection based on image enhancement in remote sensing images

At present, the target detection algorithms represented by deep learning, such as: You only look once (YOLO), Single Shot MultiBox Detector (SSD) and other single-stage target detection algorithms have demonstrated high detection rate and stable detection effect However, when this type of detection algorithm detects certain types of targets in remote sensing images, because the relative and absolute sizes of these targets are very small, the problems of low detection rate and high false detection rate will occur. Traditional detection methods cannot meet the requirements of remote sensing. Image inspection needs. To solve this problem, this paper proposes a small target detection method based on image enhancement method. Firstly, the image overlap and block method are used to increase the relative size of the small target and retain the information of the small target. Secondly, the image after each block is The block uses an image enhancement method based on SRGAN to improve the resolution of the small target, which is used to improve the sub-table rate of the small target and enhance its feature and context information. Then based on the YOLOv4 method, the enhanced image block is detected. Finally, based on the NWPU VHR-10 data set, the effectiveness of the method is verified in the experimental results of remote sensing image based on image enhancement of small target detection.
Perceptions of linguistic parity on social media: a qualitative comparison between primary Cymraeg and English-medium secondary school pupils

This thematic analytic qualitative study used participants' oral narratives to examine primary Cymraeg, which is the Welsh language, and English-speaking adolescents' perceptions regarding Cymraeg-speakers' opportunities of using their primary language on social media. Twenty-three participants were interviewed (two Welsh/Bilingual-medium schools: males = 5, females = 6; two English-medium schools: males = 6, females = 6). All of the Welsh/Bilingual-medium participants were primary Cymraeg-speakers. Operating at the broad thematic level, both groups provided similar responses. However, inter-group variance might be inferred at the sub-thematic level where, for instance, Cymraeg-speakers identified a greater array of technical limitations preventing Cymraeg-speakers using Cymraeg within the social media domain. The potential consequences of an actual or perceptual linguistic imbalance on social media are discussed.
Cloud – buzzword or business plan?

Computer Fraud & SecurityVol. 2022, No. 5 The SandboxCloud – buzzword or business plan?Patrick WraggPatrick WraggIntegrity360Search for more papers by this authorPatrick WraggPublished Online:18 May 2022https://doi.org/10.12968/S1361-3723(22)70575-1AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 May 2022Volume 2022Issue 5ISSN (print): 1361-3723ISSN (online): 1873-7056 Metrics History Published online 18 May 2022 Published in print 1 May 2022 Information© MA Business Ltd.
Deep learning for image super-resolution and its application in urban security

Improving the image quality of surveillance cameras is highly demanded in urban security, which could greatly ease the workload of policies in identifying criminals. Deep learning, especially for generative adversarial networks (GAN), had been used in increasing image quality by translating low-resolution images into high-resolution images. The technology that converting low-resolution images into high-resolution images is called image super-resolution. GAN consists of a generator and a discriminator in which the discriminator is to discriminate real and fake high-resolution images while the generator is to convert low-resolution images into fake high-resolution images that can not be differentiated by the discriminator. The generator and discriminator work together in a competitive manner that tries to improve each other. This paper aims to explore GAN-based methods for image super-resolution, which is important for urban security. We first provide readers with theories about GAN. Moreover, we introduce four popular methods based on GAN for image super-resolution including super-resolution GAN, enhanced super-resolution GAN, residual channel attention GAN, and super-resolution GAN with ranker. We also list some challenges about applying image super-resolution to urban security and provide the corresponding solution.
Combining Global and Local Representations of Source Code for Method Naming

Code is a kind of complex data. Recent models learn code representation using global or local aggregation. Global encoding allows all tokens of code to be connected directly and neglects the graph structure. Local encoding focuses on the neighbor nodes when capturing the graph structure but fails to capture long dependencies. In this work, we gather both encoding strategies and investigate different models that combine both global and local representations of code in order to learn code representation better. Specifically, we modify the layer structure based on the sequence-to-sequence model to incorporate a structured model in the encoder and decoder parts, respectively. To further consider different integration ways, we propose four models for method naming. In an extensive evaluation, we demonstrate that our models have a significant improvement on a well-studied dataset of method naming, achieving ROUGE-1 score of 54.1, ROUGE-2 score of 26.7, and ROUGE-L score of 54.3, outperforming state-of-the-art models by 2.7, 1.7, and 4.3 points, respectively. Our data and code are available at https://github.com/zc-work/CGLNaming.
Crime Detection on Social Networks Using AI and ML Techniques

Data mining (DM) and machine learning techniques have been used to identify specific patterns and similarities in existing cybercrimes to predict the cyberattacks. Since dealing with big data is a complex task as it requires computationally accurate and efficient techniques, difficulty arises in the analysis of irregular activities on cyberworld. DM techniques offer predictive solutions against cybercrimes and modus operandi. DM methods include classification, association, and clustering while different methods related to machine learning are supervised, semi-supervised, and unsupervised. AI can serve as a smart tool for criminal usage for carrying out malicious activities either by enhancing or changing the existing threats or posing new threats altogether. The expansion of existing threats such as drug trafficking improve frequency of smuggling activities by turning to unmanned underwater vehicles. Also using cheap quadcopter and facial recognition software together could increase the terrorist attacks on civilians.
Biaxial structured illumination microscopy with high measurement accuracy based on product processing

• In this paper, a BPSIM method with enhanced measurement accuracy is proposed. • Two CCDs, one located in the focal plane and the other placed before or after the focal plane, are used to detect signals. • By conducting a multiplication operation on these two signals, we obtain a product CDR, whose FWHM is smaller than CDR. • Since the FWHM of PCDR is smaller than that of conventional CDR, an enhanced measurement accuracy can be achieved. • The proposed BPSIM can find potential applications in precision measurement. Structured illumination microscopy (SIM) has been established for non-fluorescent three-dimensional (3D) measurement with nanometer resolution. In SIM, a narrower full width at half maximum (FWHM) of contrast depth response (CDR) curve indicates a higher measurement resolution and accuracy. In this paper, we propose a biaxial structured illumination microscopy based on product processing (BPSIM) to achieve a high resolution and accurate reconstruction. In this method, two CCDs placed in biaxial optical paths are utilized to capture signals. After that, through conducting a multiplication operation on these two signals, a product CDR (PCDR) with narrow FWHM is achieved. Further, the peak position of PCDR for each pixel is extracted to realize 3D measurement. Owing to narrow FWHM of PCDR, an enhanced measurement accuracy and resolution can be obtained. The measurement error of step sample achieved by SIM and BPSIM is 14.5 nm and 8.7 nm respectively, justifying the proposed method can realize an improved accuracy. The simulations are also performed to demonstrate the feasibility of proposed method, indicating the potential to be applied in high precision measurement.
<scp>NeuralConflict</scp>: Using neural networks to identify norm conflicts in normative documents

Abstract A large number of norms, which express constraints on people's behaviour within a specific range, are contained in normative documents. In writing and revising normative documents, conflicts between two norms often arise. The task Norm Conflict Identification (NCI) aims to identify such conflicts. The existing NCI methods based on statistical learning are all pipelines, which cause errors to accumulate, and cannot sufficiently extract helpful information. According to the characteristics of NCI, we propose a neural network model called NeuralConflict. This end‐to‐end model can avoid the accumulation of errors and makes it easier to obtain the optimal global solution. The model sets up an auxiliary task to predict whether two norms are semantically related and shares part of the information with the task NCI. In addition, the model uses a convolutional neural network with differently sized convolution kernels to extract local semantic information from the norms. Finally, the model inputs the shared and local semantic information into a fully connected neural network to predict whether the two norms conflict. We construct a Chinese dataset and use it with an existing English dataset for the experiments. Experimental results show that NeuralConflict achieves optimum results on both datasets.