Reviewerly/exHarmony huggingface dataset:
Axial refocusing precision model with light fields

In light field imaging, axial refocusing precision corresponds to the minimum distance in the axial direction between two distinguishable refocusing planes. The refocusing precision can be essential for applications like light field microscopy. In this paper, we introduce a refocusing precision model based on a geometrical analysis of the flow of rays within the virtual camera. The model establishes the relationship between the feature separability of refocusing and different camera settings. As extending numerical aperture (NA) in classical imaging, the baseline extension of light field also gives more accurate refocusing results. To test the axial refocus precision, we conduct experiments with 1st generation Lytro camera as well as a Blender light field simulation. The results is basically consistent with our prediction. Then, we show that computationally extending the light field baseline increases the axial refocusing precision on real plenoptic camera and light field microscopy datasets.
Software-defined hardware-assisted isolation for trusted next-generation IoT systems

To mitigate1 cybersecurity threats at the edge of the network in Internet-of Things (IoT) domains, recently, the use of networking technologies such as SDN-NFV has been proposed. Intelligent and dynamic security policy enforcement methodologies become increasingly important to bring more cautious in network communications for IoT services and applications which naturally embed traditional security and privacy risks, such as service hijacking, DDoS attack, denial service, IP spoofing, man-in-the-middle. To extend such frameworks, in this work we present a software-defined protection-oriented hardware technique to support physical isolation of memory compartments and of hardware devices such as DMAs and accelerators inside modern Systems-on-Chip (SoCs), not only at the edge but also at the IoT high-end accelerator-rich devices. In addition to network functions commonly supported in software-defined environments, we describe innovative lightweight software-controlled hardware mechanisms for enhancing IoT ecosystem security by design.
On the Deduction of the<scp>EPP</scp>from Labeling<sup>*</sup>

Abstract The present paper examines a recent attempt to deduce the EPP from labeling. Chomsky (2015) argues that the EPP is reducible to Label Weakness. In this paper, I first point out that Label Weakness raises problems that are not faced under the EPP. I then propose that pair‐Merge (i.e. the Merge that yields an asymmetric structure) provides a solution to Label Weakness, claiming that Merge, together with Label Weakness, explains the EPP. I show that the proposal brings theoretically and empirically favorable consequences. Given the proposed analysis, I also argue that externalization is the other side of the EPP, demonstrating that it explains the EPP when a labeling problem is solved and that it reduces parametric variations with the EPP to the peripheries of the computation. This paper argues that Merge plays a key role in language and endorses the Merge‐based language design proposed in the Minimalist Program.
Real-Time Hair Filtering with Convolutional Neural Networks

Rendering of realistic-looking hair is in general still too costly to do in real-time applications, from simulating the physics to rendering the fine details required for it to look natural, including self-shadowing. We show how an autoencoder network, that can be evaluated in real time, can be trained to filter an image of few stochastic samples, including self-shadowing, to produce a much more detailed image that takes into account real hair thickness and transparency.
A particle swarm algorithm combined with flower pollination algorithm

At present, many scholars have conducted extensive research on parameter design in particle swarm optimization algorithms and have achieved many results. The particle swarm algorithm is easy to fall into the local optimum, so the paper modifies the particle swarm parameters to improve the algorithm's performance. To further improve the algorithm, this paper proposes a combination of flower pollination and particle swarm algorithm. The comparison of the optimization accuracy and the convergence speed on the four test functions proves the superiority of the flower pollination and particle swarm algorithm.
An Efficient Data Classification Decision Based on Multimodel Deep Learning

A single model is often used to classify text data, but the generalization effect of a single model on text data sets is poor. To improve the model classification accuracy, a method is proposed that is based on a deep neural network (DNN), recurrent neural network (RNN), and convolutional neural network (CNN) and integrates multiple models trained by a deep learning network architecture to obtain a strong text classifier. Additionally, to increase the flexibility and accuracy of the model, various optimizer algorithms are used to train data sets. Moreover, to reduce the interference in the classification results caused by stop words in the text data, data preprocessing and text feature vector representation are used before training the model to improve its classification accuracy. The final experimental results show that the proposed model fusion method can achieve not only improved classification accuracy but also good classification effects on a variety of data sets.
Computer-Aided Mural Digital Restoration under Generalized Regression Neural Network

Aiming at the digital protection of classical murals and according to the method of generalized regression neural network (GRNN), a digital restoration is proposed in the paper. Firstly, the existing defect photos are processed preliminarily, including the elimination of noise, the extraction of boundary pixels of the region to be repaired, and the establishment of several small block regions centered on these pixels. Then, similar known pixel regions are found as sample pixel blocks, which are used as input samples of GRNN. Finally, the GRNN is adopted to obtain the approximate estimation function, and the adaptive smoothing parameters are introduced to obtain the pixel information of the area to be repaired. Through model prediction, by acquiring the pixel information of the area to be repaired, the damaged area of the original image can be repaired. The method proposed is compared with the traditional repair methods, the results show that the method is close to the texture structure image restoration method in peak signal-to-noise ratio, and the restoration results are in line with the expectation.
Integrating vulnerability risk into the software process

Software developers often focus on the functional aspects of software and defer consideration of security vulnerabilities until late in the development process. Consequently, vulnerabilities plague contemporary software. This work presents an approach that leverages contemporary vulnerability data in determining risk for the type of software. A novel process model is used to infuse vulnerability risk into the specification, design and implementation phases of the software process. This approach brings security concerns to the forefront of the entire process. A case study demonstrates mitigation actions for specific weaknesses in each phase of development.
Analysis and Recognition of Sports Video in Physical Coaching Process Using Particle Swarm Optimization Algorithm

The proportion of network video traffic in the network is increasing as Internet and streaming media technology continues to advance. The current network environment is becoming more complicated as new application services emerge. ISPs are grappling with how to effectively allocate limited network bandwidth resources while maintaining network service quality (Internet service provider). Currently, in the process of physical coaching, trainers use the video replay and analysis management method with less difficulty to explain the essentials of movements to athletes, but it is not intuitive enough to identify and classify network video services and can guide Internet service providers (ISPs) to optimise network resource management and rationally configure traffic engineering, in order to ensure the quality of service (QOS) of video services. Finally, using the shot as the unit, find similar video clips in the video database based on the video examples submitted by the user, and return retrieval results based on similarity. This paper uses streaming video as the research object and examines the transmission control of sports video using the particle swarm optimization algorithm in detail, based on a comprehensive introduction to the analysis and recognition of sports video in the process of physical coaching.
Feature assortment for deep learning-based bug localization with a program graph

Bug localization can effectively reduce software maintenance costs. Recently, deep learning-based bug localization (DLBL) has demonstrated its effectiveness in bridging the lexical gaps between bug reports (BRs) and source code files (SFs). Deliberate feature selection that considers the unique characteristics of SFs can boost DLBL. Using the various features of SFs, we propose the following three methods to identify the features that can improve DLBL 1) text token restriction, 2) program graph construction, and 3) projection. First, the text token information of SFs is used to avoid selecting a text token that can become a noise feature. Second, we propose a five rules to construct a program graph that can supplement the textual features. Our program graph can highlight the difference between buggy and non-buggy SFs while preserving the individual characteristics of each SF and interleaved relationships of SFs. We treat the entire program of the project as a knowledge graph, whose subgraphs are SFs. Even if the features of the SFs are presented well by existing approaches, these approaches have a limitation in that they choose parts irrelevant to the bug as a feature, because the same features represent the SF for all of the different input BRs. Therefore, we propose projecting the SF feature vectors onto the BR feature vectors to highlight the BR-relative features of the SF for different BRs. We evaluated our proposed method on widely used open-source Java projects. The experimental results on 1,928 BRs from 10 Java projects showed the effectiveness of the proposed method. The proposed method can improve bug localization accuracy by an average of 34%.
Analysis of Cloud and Self-Web-Hosting Services Based on Security Parameters

Most of the web applications are hosting by cloud due to low cost and low infrastructure setup is required. In continue to this, user no need to maintain the infrastructure by their own. In this paper, both the technology, self-web-hosting, and cloud-web-hosting are compared based on different security parameters like key generation (PKI), automatic authentication and protection of intra-tenant networks, secure logging system events, spam filtering, CAPTCHA generation and authentication, and software widgets such as password metering. These parameters are classified into seven categories and the review has been conducted based on these categories. The bibliometric analysis has been conducted where ever more than 70 research found in Web-of-Science (WoS) database through bibliometric library and biblioshine package in R programming. The outcome of this analysis is presented in tabular form, and open challenges of both the technologies are discussed in details with proposed solutions.
DIPS: A Dyadic Impression Prediction System for Group Interaction Videos

We consider the problem of predicting the impression that one subject has of another in a video clip showing a group of interacting people. Our novel Dyadic Impression Prediction System ( DIPS ) contains two major innovations. First, we develop a novel method to align the facial expressions of subjects p i and p j as well as account for the temporal delay that might be involved in p i reacting to p j ’s facial expressions. Second, we propose the concept of a multilayered stochastic network for impression prediction on top of which we build a novel Temporal Delayed Network graph neural network architecture. Our overall DIPS architecture predicts six dependent variables relating to the impression p i has of p j . Our experiments show that DIPS beats eight baselines from the literature, yielding statistically significant improvements of 19.9% to 30.8% in AUC and 12.6% to 47.2% in F1-score. We further conduct ablation studies showing that our novel features contribute to the overall quality of the predictions made by DIPS .
A landmark building detection and recognition based on improved Faster R-RDN algorithm

This paper proposes an improved Faster R-DRN (Dense Residual Network, DRN) algorithm, which is based on Faster R-CNN, using densely connected residual network DRNet to replace VGG network. This algorithm is suitable for special scenes of building recognition. It has a residual network and a deep convolution residual network structure, which can efficiently perform image detection, classification and recognition. This design optimizes the problem of algorithm overfitting due to the increase of network depth. In this paper, a comprehensive sample data set for various landmark buildings is established, and samples with different weather, different lighting, and different angles are taken to effectively improve the resistance of the training model. Combined with the optimization of the network structure and the training of targeted data sets, the final feature block diagram generated by DRNet not only does not lose the lowlevel edge texture information, but also reuses the low-level feature block diagrams in the deep convolutional network to make the fused feature block Richer feature information effectively improves the model's recognition rate for photos taken in complex environments. The experimental results show that the accuracy of this method for predicting landmark buildings can reach 82.0% of mAP, and the recognition performance of images taken in complex environments is excellent.
Optimized Design and Implementation of Testing Laboratory Based on Blockchain Technology

Abstract Traditional testing laboratories suffer from inefficient management of testing equipment, high maintenance efficiency costs, non-transparent testing processes and easy tampering of testing data. This paper makes use of blockchain technology somewhat to optimise the design of data traceability for testing laboratories. Through the optimised design of this paper, an intelligent testing process is achieved for the traditional testing laboratory. At the same time, the level of secure storage of data is improved, making the testing process and test results safer and more reliable. This paper uses blockchain technology to optimise the testing laboratory and achieves significant results. The novel way of using blockchain technology to do data traceability explored in this paper has reference significance and application value for the optimisation and upgrading of traditional laboratories.
Risley prisms applications: an overview

Risley prisms systems consist of two-wedge aligned prisms that deviate a light beam or wavefront. The relative angle between prisms determines the displacement of the central point wavefront. In the case of wavefront propagation through the Risley system, the relative angle also can introduce a controlled tilt. Then, the beam's or wavefront displacement direction is controlled by changing the relative angle between prisms. Risley prisms have been used in multiple applications such as super-resolution imaging and field of view (FOV) extension, steering systems, precision pointers, scanning systems, and wavefront alignment and positioning. This paper presents a description of the techniques used by Risley prisms making a compilation of their most essential characteristics. One of the related applications is reviewed on a Vectorial Shearing Interferometer. The paper overviews the advantages and disadvantages of using Risley prisms in different valuable applications.
Density peaks clustering: a gentle comparison

Density peaks clustering (DPC) is a new algorithm based on density in clustering analysis. The algorithm considers local density and relative distance to draw the decision graph, quickly identify cluster centers, and complete clustering. The DPC has a unique input parameter and requires no prior knowledge or iteration. Since it was proposed in 2014, DPC has aroused great interest among scholars and developed rapidly. The paper first introduces the advantages and disadvantages of DPC and its basic theory. Secondly, we analyze the DPC optimization method from four perspectives. Then, five improved algorithms of DPC are introduced, and experiments on DPC and these improved algorithms are carried out in synthetic datasets and real-world datasets to evaluate the performance of these algorithms.
Extracting Salient Facts from Company Reviews with Scarce Labels

In this paper, we propose the task of extracting salient facts from online company reviews. Salient facts present unique and distinctive information about a company, which helps the user in deciding whether to apply to the company. We formulate the salient fact extraction task as a text classification problem, and leverage pretrained language models to tackle the problem. However, the scarcity of salient facts in company reviews causes a serious label imbalance issue, which hinders taking full advantage of pretrained language models. To address the issue, we developed two data enrichment methods: first, representation enrichment, which highlights uncommon tokens by appending special tokens, and second, label propagation, which interactively creates pseudopositive examples from unlabeled data. Experimental results on an online company review corpus show that our approach improves the performance of pretrained language models by up to an F1 score of 0.24. We also confirm that our approach competitively performs well against the state-of-the-art data augmentation method on the SemEval 2019 benchmark even when trained with only 20% of training data.
Reconfigures Protected Protocols for Advanced Cloud Computing

Cloud computing is a term which used to depict both a stage and kind of utilization. Cloud computing varies from customary registering ideal models as it is adaptable, can be embodied as a conceptual these elements which gives us diverse level of administrations to the customers, driven by economies of scale and the administrations are progressively configurable. Information put away in outsider stockpiling frameworks like the cloud probably won't be secure since classification and honesty of information are not ensured. In spite of the fact that distributed computing gives savvy stockpiling administrations. Subsequently, numerous associations and clients may not utilize the cloud administrations to store their information in the cloud until the point that specific security ensures are made. In this paper, an answer for the issue of safely putting away the customer's information by keeping up the privacy and trustworthiness of the information inside the cloud is produced. The proposed conventions are created which guarantee that the customer's information is put away just on confided away servers, reproduced just on confided away servers, and certification that the information proprietors and other favored clients of that information get to the information safely.
FEAST

Self-adaptation is considered as one of the effective means to enable IoT systems to overcome run-time uncertainties and thereby provide guarantees on the Quality of Service (QoS). Traditionally this is accomplished by extending the IoT system (managed system) with a managing system responsible for handling the adaptation. Many different approaches have been proposed in the literature to perform such adaptations but, they mainly focus on improving the adaptation logic inside the managing system to provide better guarantees on the QoS of the managed system. However, they do not focus on the implementation architecture of the managing system itself. Further, the choice of implementation can impact the QoS offered by the IoT system and that of the managing system. To this end, in this work, we propose a framework that allows the architect to systematically design, analyze and evaluate different architectural strategies for implementing the managing system of self-adaptive IoT systems. The evaluation results of our approach on the case study of a real-world IoT system demonstrate promising results in allowing architects to evaluate multiple architectural strategies for implementing the managing system.
Recognition of handwritten digits on blackboard using Resnet model

Handwritten numbers play a huge role in human production and life. At present, how to automatically recognize handwritten digits accurately and efficiently has become a very realistic research problem. This article takes the handwritten digits on the blackboard as the research object, and introduces the convolutional neural network model and the Resnet model. Furthermore, a kind of recognition of handwritten digits on blackboard based on Resnet model is proposed, which can improve the recognition rate of handwritten digits by adding residual module to realize residual learning. Based on the above theory, the Resnet model is constructed using the python programming language, and the model is trained through the public data set MNIST. Finally, the model is applied to recognize the handwritten digits data set of blackboard writing, and the corresponding digits are successfully recognized.
Boosting Video Object Segmentation Based on Scale Inconsistency

We present a refinement framework to boost the performance of pre-trained semi-supervised video object segmentation (VOS) models. Our work is based on scale inconsistency, which is motivated by the observation that existing VOS models generate inconsistent predictions from input frames with different sizes. We use the scale inconsistency as a clue to devise a pixel-level attention module that aggregates the advantages of the predictions from different-size inputs. The scale inconsistency is also used to regularize the training based on a pixel-level variance measured by an uncertainty estimation. We further present a self-supervised online adaptation, tailored for test-time optimization, that bootstraps the predictions without ground-truth masks based on the scale inconsistency. Experiments on DAVIS 16 and DAVIS 17 datasets show that our framework can be generically applied to various VOS models and improve their performance.