Reviewerly/exHarmony huggingface dataset:
An Attention Fusion Network For Event-Based Vehicle Object Detection

Under the extreme conditions such as excessive light, insufficient light or high-speed motion, the detection of vehicles by frame-based cameras still has challenges. Event cameras can capture the frame and event data asynchronously, which is of great help to address the object detection under the aforementioned extreme condition. We propose a fusion network with Attention Fusion module for vehicle object detection by jointly utilizing the features of both frame and event data. The frame and event data are separately fed into the symmetric framework based on Gaussian YOLOv3 to model the bounding box (bbox) coordinates of YOLOv3 as the Gaussian parameters and predict the localization uncertainty of bbox with a redesigned cross-entropy loss function of bbox. The feature maps of these Gaussian parameter and confidence map in each layer are deeply fused in the Attention Fusion module. Finally, the feature maps of the frame and event data are concatenated to the detection layer to improve the detection accuracy. The experimental results show that the method presented in this paper outperforms the state-of-the-art methods only using the traditional frame-based network and the joint network combining the event and frame information.
A(DP)^2SGD: Asynchronous Decentralized Parallel Stochastic Gradient Descent with Differential Privacy

As deep learning models are usually massive and complex, distributed learning is essential for increasing training efficiency. Moreover, in many real-world application scenarios like healthcare, distributed learning can also keep the data local and protect privacy. Recently, the asynchronous decentralized parallel stochastic gradient descent (ADPSGD) algorithm has been proposed and demonstrated to be an efficient and practical strategy where there is no central server, so that each computing node only communicates with its neighbors. Although no raw data will be transmitted across different local nodes, there is still a risk of information leak during the communication process for malicious participants to make attacks. In this paper, we present a differentially private version of asynchronous decentralized parallel SGD framework, or A(DP) 2SGD for short, which maintains communication efficiency of ADPSGD and prevents the inference from malicious participants. Specifically, Rényi differential privacy is used to provide tighter privacy analysis for our composite Gaussian mechanisms while the convergence rate is consistent with the non-private version. Theoretical analysis shows A(DP) 2SGD also converges at the optimal O(1/√T) rate as SGD. Empirically, A(DP) 2SGD achieves comparable model accuracy as the differentially private version of Synchronous SGD (SSGD) but runs much faster than SSGD in heterogeneous computing environments.
3D Traversability Map Generation for Mobile Robots Based on Point Cloud

Analysing the traversability from the point cloud input is a critical task for mobile robot navigation. However, there is no mature method for mobile robots to exploring complicated 3D environments, especially those with multi-layered roads. This paper proposes a new traversability map based on the point cloud input, and the map will help analyze where the mobile robot can move in the whole space. The proposed 3D traversability map consists of nodes and edges generated according to the mobile robot’s geometry and driving performance. Nodes represent where the robot can be placed, and edges indicate the cost of moving to another node. The traversability map is stored in digraph form and is convenient to be applied to path planning. Experiments show the successful results of traversability map generation and path planning with D algorithm in different situations.
DIGITAL IMAGE AND TEXT DATA SECURITY IMPROVEMENT USING THE COMBINATION OF STENOGRAPHY AND EMBEDDING TECHNIQUES

In today age of technology max of the data are exchanges through the internet. With advances of the innovation the computerized media has gone most popular data transfer apparatuses. So the security of the digital data is need of today generation, the digital data includes text, images, audio, video. For the protection of the digital data many techniques can be used such as the embedding, encryption, data hiding etc. To secure information, cryptography and Steganography are widely used. In this paper present a digital image and text data security improvement technique using the combination of stenography and embedding. For the Steganography the DCT technique is used and for the embedding data LSB technique is used.  For better security purpose required the value of the MSE is low and value of the PSNR is high. So in this work got the value of the MSE is low and the value of the PSNR is high then previous work done. By using this proposed technique achieve the security of secret text and secret image is very high level and robust.
Multi-Granularity Code Smell Detection using Deep Learning Method based on Abstract Syntax Tree

smell refers to poor design that is perceived to have a negative impact on readability and maintainability during software evolution, and it implies the possibility of refactoring.Therefore, the effective detection of code smell is of great importance.Many approaches including metric-based, heuristic-based, and machine learning approaches have been proposed to detect code smells.However, all these methods use manually selected features, which is highly subjective and difficult to select the most appropriate features.Recently, deep learning methods without extensive feature engineering have been proposed.Nevertheless, these token-based approaches may not achieve good results because they ignore many semantic and structural information of source code.To this end, we propose a novel deep learning approach based on abstract syntax trees(ASTs) to detect multi-granularity code smells, which captures the semantic and structural features of code fragments from the ASTs.The experimental results on four types of smells show that this approach achieves better results than the stateof-the-art approaches for detecting code smells with different granularities.
Embracing New Techniques in Deep Learning for Predicting Image Memorability

Various work has suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget. While older work used a now-outdated deep learning architecture to predict image memorability, innovations in the field have given us new techniques to apply to this problem. Here, we propose and evaluate five alternative deep learning models to MemNet which exploit developments in the field from the last five years, largely the introduction of residual neural networks. We also evaluate the pre-existing implementation of MemNet on a broader set of images. Five new models with architectural differences were implemented and tested on a mixture of MemNet’s original training set, LaMem, and a recent dataset, MemCat. LaMem is a large database of objects and scenes, many of which are designed to have high memorability. MemCat complements this, with a large number of exemplars in object categories. The new models all utilize residual neural networks, which are intended to mimic the structure of pyramidal cells with skip connections, in their feature extraction stages, allowing the model to use semantic information in the memorability estimation process. The most complex model also utilizes semantic segmentation, which ascribes a semantic category to each pixel. Our findings suggest that the original paper overstated MemNet’s generalizability and MemNet likely was overfitting on LaMem. Our new models outperform MemNet, all achieving similar scores to one another, but when allowed to retrain the semantic segmentation based model outperforms the rest. This information leads us to conclude that Residual Networks outperform simpler convolutional neural networks in memorability regression, which will in turn improve memory researchers’ ability to make predictions about memorability on a wider range of images.
The European CHAISE Initiative to Shape the Future of Blockchain Skill Qualification and Certification

Blockchain is at the core of the EU strategy to advance digital transformation, benefitting society/businesses and stimulating sustainable growth. The competitiveness of blockchain development and applications largely relies on the availability of competent/versatile workforce. Whereas the demand for blockchain skills is steadily increasing, employers are facing a shortfall of skilled professionals. The sector is challenged by talents shortage, global competitive pressures, as well as a limited connection between education and the labor market. The aim of CHAISE initiative presented in this paper is to set forward an open, inclusive blockchain skills governance system, to address skills mismatches and deliver appropriate training, intelligence gathering & mobility solutions tailored to sectoral needs.
Revealing the Relative Contributions of Conceptual and Perceptual Information to Visual Memorability

Visual Memorability has been shown to be an intrinsic property of images, yet it remains unclear what combination of image properties fully define visual memorability. We tested memory performance on the THINGS database (Hebart et al, 2019), a database of 26,107 images from 1854 different object categories, quantified by embeddings in a 49-dimensional space reflecting perceptual and conceptual components underlying human similarity judgments. We used these embeddings to examine image memorability in the context of this high dimensional object space. We carried out a continuous recognition task on 13,496 AMT participants, providing us with memorability scores for all images in the THINGS database. We observed significant consistency across participants’ memory performance, allowing us to use memorability as an intrinsic attribute for each image. Our findings identified no significant correlation between memorability and ratings of typicality, suggesting that different images were memorable for different reasons. Correlations between the 49 dimensions and the memorability scores revealed that some dimensions (e.g. animal-relatedness) are more strongly associated with memorability than others (e.g. hardness). We capitalized on the nature of the dimensions as either perceptual or conceptual by focusing on relative contributions of perceptual and conceptual information to memorability. Using ratings of the dimensions along a scale from purely conceptual to purely perceptual, we analyzed the differences in correlation with memorability, variance in corrected recognition (hit rate – false alarm rate), and differences in raw embedding values between perceptual and conceptual dimensions. Our results indicated a stronger correlation with memorability for conceptual information than perceptual, where conceptual dimensions captured 31.72% of variance while perceptual dimensions only captured 6.07%. These results may indicate a greater contribution of conceptual information to the memorability of an image, suggesting a target for further study of the structure of visual memorability.
X-Ray Image Recognition Based on Improved Mask R-CNN Algorithm

It is an important part of security inspection to carry out security and safety screening with X-ray scanners. Computer vision plays an important role in detection, recognition, and location analysis in intelligent manufacturing. The object detection algorithm is an important part of the intelligent X-ray machine. Existing threat object detection algorithms in X-ray images have low detection precision and are prone to missed and false detection. In order to increase the precision, a new improved Mask R-CNN algorithm is proposed in this paper. In the feature extraction network, an enhancement path is added to fuse the features of the lower layer into the higher layer, which reduces the loss of feature information. By adding an edge detection module, the training effect of the sample model can be improved without accurate labeling. The distance, overlap rate, and scale difference between objects and region proposals are solved using DIoU to improve the stability of the region proposal’s regression, thus improving the accuracy of object detection; SoftNMS algorithm is used to overcome the problem of missed detection when the objects to be detected overlap each other. The experimental results indicate that the mean Average Precision (mAP) of the improved algorithm is 9.32% higher than that of the Mask R-CNN algorithm, especially for knife and portable batteries, which are small in size, simple in shape, and easy to be mistakenly detected, and the Average Precision (AP) is increased by 13.41% and 15.92%, respectively. The results of the study have important implications for the practical application of threat object detection in X-ray images.
Game Semantics for Constructive Modal Logic

In this paper we provide the first game semantics for the constructive modal logic \(\mathsf {CK}\). We first define arenas encoding modal formulas, we then define winning innocent strategies for games on these arenas, and finally we characterize the winning strategies corresponding to proofs in the logic \(\mathsf {CK}\). To prove the full-completeness of our semantics, we provide a sequentialization procedure of winning strategies. We conclude the paper by proving their compositionality and showing how our results can be extend to the constructive modal logic \(\mathsf {CD}\).
Considering the Speed and Comprehension Trade-Off in Reading Mediated by Typography

The quantity of information an individual absorbs each day is rapidly expanding. We consider how everyday people adapt to these demands, reading to maximize speed or comprehension, and the resultant speed – comprehension trade-offs. In a large-scale Interlude Reading experiment, 445 crowdworkers read 12 short passages set to a 12th-grade reading level. They read passages in Times and 5 other randomly selected fonts from a group of 26 total fonts. They read 2 passages per font and answered 3 comprehension questions after reading each passage. We divided each passage into 2 short screens to measure reading speed. We found a significant inverse correlation between reading speed and comprehension per-trial (R = -0.27, p < 0.001), consistent with a speed-accuracy trade-off. We found a similar correlation when aggregating per font, suggesting that font may mediate the effect (R = -0.39, p < = 0.041). Font also significantly affected reading speed (p = 0.018). Confirmatory analyses on each font's relative speed (ranked speeds per participant) demonstrated a strengthened relationship (R = -0.48, p = 0.011), suggesting the fonts' design characteristics mediated the trade-off within and between participants. Reading speed rises by 31 WPM over the session, and second screens are consistently read faster than first screens by 31 WPM (both p < 0.001). Conversely, passage order and the font did not significantly affect comprehension, nor did they indicate that additional practice could mediate the trade-off. Interestingly, practice effects appear insufficient to overcome an intrinsic speed – comprehension trade-off. Fonts demonstrated the same trade-off in an intra-participant analysis; our results suggest that leveraging typeface design could minimize individuals' trade-offs. Indeed, finding the right font for the reader could improve individual performance in the context of their reading goal: comprehension or speed.
Orchestrating Agile IT Quality Management for Complex Solution Development Through Topic-Specific Partnerships in Large Enterprises – An Example on the EFIS Framework

Enterprises have to find their ways to ensure systematic quality management in modern agile environments. However, there is no standard approach or framework which addresses the special demands of large enterprises. This work presents the approach of the Volkswagen Group IT to handle this topic. The work presents the challenge and the way to solve it from the perspective of the product owner of the quality innovation network and its embedding organization.
Fitting Very Flexible Models: Linear Regression With Large Numbers of Parameters

Abstract There are many uses for linear fitting; we consider here the interpolation and denoising of data, as when the goal is to fit a smooth, flexible function to a set of noisy data points. Investigators often choose a polynomial basis, or a Fourier basis, or wavelets, or something equally general. They also choose an order, or number of basis functions to fit, and (often) some kind of regularization. We discuss how this basis-function fitting is done, with ordinary least squares and extensions thereof. We emphasize that it can be valuable to choose far more parameters than data points , despite folk rules to the contrary: Suitably regularized models with enormous numbers of parameters generalize well and make good predictions for held-out data; over-fitting is not (mainly) a problem of having too many parameters. It is even possible to take the limit of infinite parameters, at which, if the basis and regularization are chosen correctly, the least-squares fit becomes the mean of a Gaussian process, or a kernel regression. We recommend cross-validation as a good empirical method for model selection (for example, setting the number of parameters and the form of the regularization), and jackknife resampling as a good empirical method for estimating the uncertainties of the predictions made by the model. We also give advice for building stable computational implementations.
A Nested Chinese Restaurant Topic Model for Short Texts with Document Embeddings

In recent years, short texts have become a kind of prevalent text on the internet. Due to the short length of each text, conventional topic models for short texts suffer from the sparsity of word co-occurrence information. Researchers have proposed different kinds of customized topic models for short texts by providing additional word co-occurrence information. However, these models cannot incorporate sufficient semantic word co-occurrence information and may bring additional noisy information. To address these issues, we propose a self-aggregated topic model incorporating document embeddings. Aggregating short texts into long documents according to document embeddings can provide sufficient word co-occurrence information and avoid incorporating non-semantic word co-occurrence information. However, document embeddings of short texts contain a lot of noisy information resulting from the sparsity of word co-occurrence information. So we discard noisy information by changing the document embeddings into global and local semantic information. The global semantic information is the similarity probability distribution on the entire dataset and the local semantic information is the distances of similar short texts. Then we adopt a nested Chinese restaurant process to incorporate these two kinds of information. Finally, we compare our model to several state-of-the-art models on four real-world short texts corpus. The experiment results show that our model achieves better performances in terms of topic coherence and classification accuracy.
On Accurate Computation of Trajectory Similarity via Single Image Super-Resolution

Measuring the similarity between trajectories is fundamental to many location-aware applications. However, the trajectory data collected in the real world suffer from the low-quality problem caused by non-uniform sampling rates and noises, which significantly affects the accuracy of similarity measurement. Traditional pairwise point-matching methods are susceptible to non-uniform sampling rates inherently, since they assume the consistent sampling rate. Although the recurrent neural network (RNN) based methods have addressed this problem by complementing trajectory data, they have the drawback of predicting positions conditioned on the historical data generated by the model itself, which could lead to accumulated bias during the inference stage. In this paper, we propose a novel generative model to address the important issue of low-quality trajectory data based on single image super-resolution. The trajectory similarity is thus computed using trajectory images instead of the trajectory sequential data. By utilizing the images to represent trajectories, we effectively overcome the issues encountered by existing methods. Extensive experimental results using real-world datasets demonstrate that our method outperforms existing methods in terms of accuracy.
A local dynamic extreme learning machine based iterative learning control of nonlinear batch process

Abstract This article deals with the optimal control issue of nonlinear batch process. First, in order to derive high efficiency and accuracy process model, a novel hierarchical searching mechanism local dynamic nonlinear model is constructed which is composed of just‐in‐time learning and extreme learning machine (JITL‐ELM). Then, based on the local dynamic JITL‐ELM model, an optimal quadratic‐criterion‐based iterative learning control (Q‐ILC) algorithm is presented, where the control input trajectory can be obtained by solving a quadratic programming problem. Moreover, on the basis of inverse model system, the initial batch control input trajectory of the Q‐ILC algorithm can be obtained by the use of JITL method. As a result, not only the issue of model‐plant mismatch and real‐time disturbance can be solved, but also obtain faster system convergence rate and smaller tracking error. Besides, the convergence properties of control input and tracking error are analyzed. Finally, a typical batch process is presented to demonstrate the feasibility and superiority.
Rapid 3D measurement of high dynamic range surface based on multi-polarization fringe projection

It is challenging to measure objects with a high dynamic range surface. We propose a three-dimensional measurement method of high dynamic surfaces based on multi-polarization fringe projection. The saturated pixels can be recognized to get the optimal exposure time by projecting a series of designed auxiliary patterns onto the surface of the object. It does not need to project multiple N-step phase-shifting fringe patterns for recognition, and it greatly improves the detection speed and efficiency. A multi-polarization camera captures full-resolution phase-shifting images with four polarization directions simultaneously. An appropriate polarization direction is selected by analyzing the per-pixel of auxiliary images captured by camera, and then optimal phase-shifting fringe images with high quality are obtained. The effectiveness and rapidness of this method are verified by the measurement experiments of different objects, and the method provides a new idea for rapid measurement.