Reviewerly/exHarmony huggingface dataset:
Research on promoting factors of “Internet Plus Innovation and Entrepreneurship” based on the perspective of cloud analysis and fuzz analysis method

With the rapid development of “Internet plus” in our country, the scope of its influence is also constantly expanding. Especially in recent years, China puts forward “mass entrepreneurship and innovation”, which makes the “cloud analysis” and “Internet plus” and “innovation” to start cross-border integration. Starting with the background, development stage, environment and other aspects of Internet-plus entrepreneurship and innovation under the cloud analysis, this paper analyzes the influence of various factors in the process of Internet plus entrepreneurship and innovation and the influence degree of various factors on entrepreneurship and innovation in depth, constructs the entrepreneurship and innovation evaluation index system based on the “Internet plus”, and finally, combined with an example to verify the index system and fusion analysis method. The verification results show that the “Internet plus” entrepreneurship and innovation based on the perspective of cloud analysis in this paper can not only directly demonstrate the promoting factors of “entrepreneurship and innovation “under Internet plus”, but also be scientific and effective when evaluating the “Internet plus” entrepreneurship and innovation activities from the perspective of cloud analysis.
Research on Agricultural Internet of Things Data Sharing System Based on Blockchain

The rapid development of technology in the field of Internet of Things has ushered in new changes in traditional agriculture. Blockchain has attracted the attention of various industries due to its unique characteristics of decentralized trust and distribution. The emergence of blockchain provides new possibilities for breaking down the barriers to data sharing and realizing the trusted interconnection of data. This article introduces the data quantification and isomerization problems presented by the progress in the agricultural IoT industry and the solutions provided by the blockchain technology. Designed a blockchain-based agricultural data sharing model and system architecture. The key mechanisms of system process design and data sharing were studied.
MSDPN: Monocular Depth Prediction with Partial Laser Observation using Multi-stage Neural Networks

In this study, a deep-learning-based multi-stage network architecture called Multi-Stage Depth Prediction Network (MSDPN) is proposed to predict a dense depth map using a 2D LiDAR and a monocular camera. Our proposed network consists of a multi-stage encoder-decoder architecture and Cross Stage Feature Aggregation (CSFA). The proposed multi-stage encoder-decoder architecture alleviates the partial observation problem caused by the characteristics of a 2D LiDAR, and CSFA prevents the multi-stage network from diluting the features and allows the network to learn the interspatial relationship between features better. Previous works use sub-sampled data from the ground truth as an input rather than actual 2D LiDAR data. In contrast, our approach trains the model and conducts experiments with a physically-collected 2D LiDAR dataset. To this end, we acquired our own dataset called KAIST RGBD-scan dataset and validated the effectiveness and the robustness of MSDPN under realistic conditions. As verified experimentally, our network yields promising performance against state-of-the-art methods. Additionally, we analyzed the performance of different input methods and confirmed that the reference depth map is robust in untrained scenarios.
The Innovation Research and Practice of the Hybrid Teaching Mode in Colleges and Universities Based on Computer Technology

Abstract With the rapid development of science and technology, online education resources have become an important way of education. Therefore, we must combine computer technology and give full play to high-quality open educational resources, which will guide us to innovate the classroom teaching mode of colleges and universities. Through hybrid teaching, colleges and universities can improve the quality of classroom teaching. Through the research of the new hybrid teaching mode, students’ interest in learning can be stimulated, thereby improving students’ learning effects. First, this article analyzes the concept and advantages of blended teaching. Then, the shortcomings of hybrid teaching practice are put forward. Finally, some suggestions were made [1-2] .
Learning explanations that are hard to vary

In this paper, we investigate the principle that good explanations are hard to vary in the context of deep learning. We show that averaging gradients across examples -- akin to a logical OR of patterns -- can favor memorization and `patchwork' solutions that sew together different strategies, instead of identifying invariances. To inspect this, we first formalize a notion of consistency for minima of the loss surface, which measures to what extent a minimum appears only when examples are pooled. We then propose and experimentally validate a simple alternative algorithm based on a logical AND, that focuses on invariances and prevents memorization in a set of real-world tasks. Finally, using a synthetic dataset with a clear distinction between invariant and spurious mechanisms, we dissect learning signals and compare this approach to well-established regularizers.
DBCACF: A Multidimensional Method for Tourist Recommendation Based on Users’ Demographic, Context and Feedback

By the advent of some applications in the web 2.0 such as social networks which allow the users to share media, many opportunities have been provided for the tourists to recognize and visit attractive and unfamiliar Areas-of-Interest (AOIs). However, finding the appropriate areas based on user’s preferences is very difficult due to some issues such as huge amount of tourist areas, the limitation of the visiting time, and etc. In addition, the available methods have yet failed to provide accurate tourist’s recommendations based on geo-tagged media because of some problems such as data sparsity, cold start problem, considering two users with different habits as the same (symmetric similarity), and ignoring user’s personal and context information. Therefore, in this paper, a method called “Demographic-Based Context-Aware Collaborative Filtering” (DBCACF) is proposed to investigate the mentioned problems and to develop the Collaborative Filtering (CF) method with providing personalized tourist’s recommendations without users’ explicit requests. DBCACF considers demographic and contextual information in combination with the users' historical visits to overcome the limitations of CF methods in dealing with multi- dimensional data. In addition, a new asymmetric similarity measure is proposed in order to overcome the limitations of symmetric similarity methods. The experimental results on Flickr dataset indicated that the use of demographic and contextual information and the addition of proposed asymmetric scheme to the similarity measure could significantly improve the obtained results compared to other methods which used only user-item ratings and symmetric measures.
The rise of MSP &amp; CSP vulnerabilities: storehouses for secure data

Managed and cloud service providers (MSPs and CSPs) serve an invaluable role in operating and securing companies' digital assets. 1 As gatekeepers for a broad range of critical systems, MSPs and CSPs are tasked with laying the security groundwork essential to keeping businesses safe from hackers and malware.
Image Classification Using Machine Learning Techniques for Traffic Signal

This paper discusses the application of image classification that can be used in controlling a car, unmanned automatically, to pause, move, take deviation as and when any obstacles come on its way, also follow traffic signals. Our work will focus on making cars to avoid accidents, unlike the manual ones that are prone to accidents. The reasons may be many lapses on the part of the driver physically, mentally or sudden medical emergencies, etc. As part of this work, CNN classifiers have been used to perform image classification. Using CNN there was accuracy of 90%. Further, this accuracy may be increased to over 95%. This work involves the use of OpenCV that controls the hardware such as Raspberry Pi, four DC motors, Raspberry Pi cam, Arduino Uno.
Implementation of pre-captured videos for remote cytological evaluation of salivary gland lesions

The objective of this study was to examine the feasibility of implementing videos captured by static telecytological applications for remote cytological evaluation of fine needle aspiration specimens from salivary gland lesions.The current study was carried out on 102 fine needle aspiration specimens from salivary gland lesions with histological confirmation (benign lesions, 11; benign neoplasms, 68; malignant neoplasms, 23), retrospectively selected from the department's registry. Videos were transferred via file transfer protocol to password-protected accounts for remote review by three independent cytopathologists. In addition to diagnosis, reviewers commented on overall digital video quality. Contributor's and reviewer's diagnoses were collected, recorded and statistically evaluated.Statistical evaluation of cytological diagnoses detected no significant difference in diagnostic accuracy between the diagnoses proffered on the basis of pre-captured videos and conventional slides. The overall interobserver agreement was ranging from substantial to almost perfect with κ values of 0.71-0.89.Video production by static telecytology applications can be used as an alternative method for telecytological diagnosis of salivary gland fine needle aspirations. Videos of salivary gland fine needle aspirations can be used for rapid and accurate diagnosis, by diminishing turn-around times and improving the quality indices of small cytology departments. They can also be used for archiving, teleconsultation, educational and second opinion purposes, improving the performance of the already existing static telecytology stations.
Task-based evaluation of deep image super-resolution in medical imaging

In medical imaging, it is sometimes desirable to acquire high resolution images that reveal anatomical and physiological information to support clinical practice such as diagnosis and image-guided therapies. However, for certain imaging modalities (i.e., magnetic resonance imaging (MRI)), acquiring high resolution images can be a very time-consuming and resource-intensive process. One popular solution recently developed is to create a high resolution version of the acquired low-resolution image by use of deep image super-resolution (DL-SR) methods. It has been demonstrated in literature that deep super-resolution networks can improve the image quality measured by traditional physical metrics such as mean square error (MSE), structural similarity index metric (SSIM) and peak signal-to-noise ratio (PSNR). However, it is not clear how well these metrics quantify the diagnostic value of the generated SR images. Here, a task-based super-resolution (SR) image quality assessment is conducted to quantitatively evaluate the efficiency and performance of DL-SR methods. A Rayleigh task is designed to investigate the impact of signal length and super-resolution network complexity on s binary detection performance. Numerical observers (NOs) including the regularized Hotelling Observer (RHO), the anthropomorphic Gabor channelized observers (Gabor CHO) and the ResNet-approximated ideal observer (ResNet-IO) are implemented to assess the Rayleigh task performance. For the datasets considered in this study, little to no improvement in task performance of the considered NOs due to the considered DL-SR SR networks, despite substantial improvement in traditional IQ metrics.
Assessment of a tumour growth model for virtual clinical trials of breast cancer screening

Image-based analysis of breast tumour growth rate may help optimize breast cancer screening and diagnosis. It may improve the identification of aggressive tumours and suggest optimal screening intervals. Virtual clinical trial (VCT) is a simulation-based method used to evaluate and optimize medical imaging systems and design clinical trials. Our work is motivated by desire to simulate multiple screening rounds with growing tumours. We have developed a model to simulate tumours with various growth rates; this study aims at evaluating the model. We used clinical data on tumour volume doubling times (TVDT) from our previous study, to fit a probability distribution (“clinical fit”). Growing tumours were inserted into 30 virtual breasts (“simulated cohort”). Based on the clinical fit we simulated two successive screening rounds for each virtual breast. TVDT from clinical and simulated images were compared. Tumour size was measured from simulated mammograms by a radiologist in three repeated sessions, to estimate TVDT (“estimated TVDT”). Reproducibility of measured sizes decreased slightly for small tumours. The mean TVDT from the clinical fit was 297±169 days, whereas the simulated cohort had 322±217 days, and the average estimated TVDT 340 ± 287 days. The median difference between the simulated and estimated TVDT was 12 days (4% of the mean clinical TVDT). Comparisons between other data sets suggest no significant difference (p&gt;0.5). The proposed tumour growth model suggested close agreement with clinical results, supporting potential use in VCTs of temporal breast imaging.
A Survey on metaheuristic nature inspired computations used for Mining of Association Rule, Frequent Itemset and High Utility Itemset

Abstract Metaheuristics are dilemma-independent methods that are generalizedin a variety of problems. In the real world, various problems are solved using generalized dilemma-independent methods called Metaheuristics Computation. Metaheuristic Nature Inspired Computing (MNIC) is a generalized approach to solve NP-hard problems by taking inspirations from the behavior of mother biological nature and their characteristics. Mining of Association rule, Frequent Itemset and High Utility Itemset are strongly interrelated and developing in the field of Data Mining. Metaheuristic nature inspired computation was widely used for the mining association rules of frequentitemsets and high utility itemsets to address the high computation time and optimal solutions. While various articles have been written, there is no systematic review of contemporary metaheuristic nature inspired approaches used in Association Rule Mining (ARM), Frequent Itemset Mining (FIM) and High Utility Itemset Mining (HUIM). This paper explores recent literature on various metaheuristics nature inspired approaches used for ARM, FIM and HUIM.
Teaching and learning post pandemic

This article aims to offer one perspective on ways that Lancaster University supported its staff in the rapid shift to online teaching and learning in the midst of a global pandemic. The approach centred around the upskilling of staff, with mixed engagement across the suite of support tools and resources, which can be compared to similar situations in the wider Higher Education (HE) sector. A focus on the future of curriculum design and the associated requirements at an institutional- and sector-wide level is addressed in relation to the opportunities and challenges with which we are faced.
PARAMETRIC FLATTEN-T SWISH: AN ADAPTIVE NONLINEAR ACTIVATION FUNCTION FOR DEEP LEARNING

Activation function is a key component in deep learning that performs non-linear mappings between the inputs and outputs. Rectified Linear Unit (ReLU) has been the most popular activation function across the deep learning community. However, ReLU contains several shortcomings that can result in inefficient training of the deep neural networks, these are: 1) the negative cancellation property of ReLU tends to treat negative inputs as unimportant information for the learning, resulting in performance degradation; 2) the inherent predefined nature of ReLU is unlikely to promote additional flexibility, expressivity, and robustness to the networks; 3) the mean activation of ReLU is highly positive and leads to bias shift effect in network layers; and 4) the multilinear structure of ReLU restricts the non-linear approximation power of the networks. To tackle these shortcomings, this paper introduced Parametric Flatten-T Swish (PFTS) as an alternative to ReLU. By taking ReLU as a baseline method, the experiments showed that PFTS improved classification accuracy on SVHN dataset by 0.31%, 0.98%, 2.16%, 17.72%, 1.35%, 0.97%, 39.99%, and 71.83% on DNN-3A, DNN-3B, DNN-4, DNN-5A, DNN-5B, DNN-5C, DNN-6, and DNN-7, respectively. Besides, PFTS also achieved the highest mean rank among the comparison methods. The proposed PFTS manifested higher non-linear approximation power during training and thereby improved the predictive performance of the networks.
Modifying u-net for small dataset: a simplified u-net version for liver parenchyma segmentation

Automation in the field of medical image segmentation is critical in helping the oncologists and surgeons for the accurate analysis of several pathological conditions by saving time. The ability to automatically segment the liver fast and accurately enables clinicians to understand the anatomical structure of the organ, and helps in the decision making process of diagnosis, surgery planning and as an anatomical map during surgical navigation especially important when using intraoperative image modalities . This work aims to develop an automatic liver parenchyma segmentation network which is based on U-Net architecture, a widely used architecture for medical image segmentation. This modified U-Net architecture includes reduced convolutional layers and using dropout layers as well as pre-processing the dataset to overcome the constraints of a small sample set. Reduced architecture complexity and introducing dropout regularization, addresses the problem of overfitting. We experimented with a callback for observing the training failure where it follows the early stopping policy and selecting the best model. Adding Gaussian noise to data can help the model to generalise well. For choosing the appropriate loss function we tested four different loss functions; Dice, binary cross entropy, Tversky and focal Tversky and concluded that Dice performs better. The network has been trained and validated using publicly available 3D-IRCADb dataset with images from 20 patients and achieved an overall Dice score of 94.5%. The overall objective of this work is to construct a network from a small sample set without the problem of overfitting or under-fitting, but delivering an acceptable Dice score.
Separation and Concentration in Deep Networks

Numerical experiments demonstrate that deep neural network classifiers progressively separate class distributions around their mean, achieving linear separability on the training set, and increasing the Fisher discriminant ratio. We explain this mechanism with two types of operators. We prove that a rectifier without biases applied to sign-invariant tight frames can separate class means and increase Fisher ratios. On the opposite, a soft-thresholding on tight frames can reduce within-class variabilities while preserving class means. Variance reduction bounds are proved for Gaussian mixture models. For image classification, we show that separation of class means can be achieved with rectified wavelet tight frames that are not learned. It defines a scattering transform. Learning 1×1 convolutional tight frames along scattering channels and applying a soft-thresholding reduces within-class variabilities. The resulting scattering network reaches the classification accuracy of ResNet-18 on CIFAR-10 and ImageNet, with fewer layers and no learned biases.
B-DCT based Watermarking Algorithm for Patient Data Protection in IoMT

Internet of Medical Things (IoMT) is the connection between medical devices and information systems to share, collect, process, store, and integrate patient and health data using network technologies. X-Rays, MR, MRI, and CT scans are the most frequently used patient medical image data. These images usually include patient information in one of the corners of the image. In this research work, to protect patient information, a new robust and secure watermarking algorithm developed for a selected region of interest (ROI) of medical images. First ROI selected from the medical image, then selected part divided equal blocks and applied Discrete Cosine Transformation (DCT) algorithm to embed a watermark into the selected coefficients. Several geometric and removal attacks are applied to the watermarked multimedia element such as lossy image compression, the addition of Gaussian noise, denoising, filtering, median filtering, sharpening, contrast enhancement, JPEG compression, and rotation. Experimental results show very promising results in PSNR and similarity ratio (SR) values after blocked DCT (B-DCT) based embedding algorithm against the Discrete Wavelet Transformation (DWT), Least Significant Bits (LSB) and DCT algorithms.
Perception-Aware Path Finding and Following of Snake Robot in Unknown Environment

In this paper, we investigate the perception-aware path finding, planning and following for a class of snake robots autonomously serpentining in an unmodeled and unknown environment. In the work, the onboard LiDAR sensor mounted on the head of the snake robot is utilized to reconstruct the local environment, by which and the modified rapidly-exploring random tree method, a feasible path from the current position of the robot to a local selected target position can be obtained. Next, the parametric cubic spline interpolation path-planning method and potential functions are applied to make the path more smooth so as to prevent the multi-link and elongated robot body from hitting obstacles. To steer, a time-varying line-of-sight control law is designed to ensure that the robot moves to the local target position along the generated path by the perception-aware method. The robot will repeatedly perform the above search-find-move strategy until it reaches the final predefined target point. Simulation and experimental results demonstrate a good performance of the proposed perception-aware approach, that is, the elongated and underactuated snake robot is capable of autonomously navigating in an unknown environment.
Recent Advances in Fully Dynamic Graph Algorithms.

In recent years, significant advances have been made in the design and analysis of fully dynamic algorithms. However, these theoretical results have received very little attention from the practical perspective. Few of the algorithms are implemented and tested on real datasets, and their practical potential is far from understood. Here, we present a quick reference guide to recent engineering and theory results in the area of fully dynamic graph algorithms.
Computational model of tumor growth for in silico trials

Digital breast tomosynthesis (DBT) can improve the detectability of breast cancer by eliminating overlapping breast tissues that affects the performance of digital mammography (DM) systems. It is yet to be established if DBT can detect lesions at earlier progression stages than DM. To pursue this investigation using in-silico methods, it is necessary to develop computational models that mimic the growth of cancerous lesions. We report on a novel computational model that mimics the progression of breast tumors based on underlying biological and physiological phenomena. Our model includes anisotropic growth and irregularly shaped lesions commonly seen in breast cancer. Our method relies on the assumption that tumor shape is ultimately determined by pressure fields given by surrounding anatomical structures causing the lesion to preferentially proliferate in certain directions. By varying the direction of tumor growth via pressure maps, we simulated various anisotropic lesions seen in clinical cases. We used the open-source, freely available VICTRE imaging pipeline to obtain DM images of growing lesions within breast models and depict several time points in the growth of the tumor as seen by this imaging modality.