Reviewerly/exHarmony huggingface dataset:
Optimizing Edge Detection for Image Segmentation with Multicut Penalties

The Minimum Cost Multicut Problem (MP) is a popular way for obtaining a graph decomposition by optimizing binary edge labels over edge costs. While the formulation of a MP from independently estimated costs per edge is highly flexible and intuitive, solving the MP is NP-hard and time-expensive. As a remedy, recent work proposed to predict edge probabilities with awareness of potential conflicts by incorporating cycle constraints in the prediction process. We argue that such formulation, while providing a first step towards end-to-end learnable edge weights, is suboptimal, since it is built upon a loose relaxation of the MP. We therefore propose an adaptive CRF that allows to progressively consider more violated constraints and, in consequence, to issue solutions with higher validity. Experiments on the BSDS500 benchmark for natural image segmentation as well as on electron microscopic recordings show that our approach yields more precise edge detection and image segmentation.
A Comprehensive Review of Vehicle Detection Techniques Under Varying Moving Cast Shadow Conditions Using Computer Vision and Deep Learning

Design of a vision-based traffic analytic system for urban traffic video scenes has a great potential in context of Intelligent Transportation System (ITS). It offers useful traffic-related insights at much lower costs compared to their conventional sensor based counterparts. However, it remains a challenging problem till today due to the complexity factors such as camera hardware constraints, camera movement, object occlusion, object speed, object resolution, traffic flow density, and lighting conditions etc. ITS has many applications including and not just limited to queue estimation, speed detection and different anomalies detection etc. All of these applications are primarily dependent on sensing vehicle presence to form some basis for analysis. Moving cast shadows of vehicles is one of the major problems that affects the vehicle detection as it can cause detection and tracking inaccuracies. Therefore, it is exceedingly important to distinguish dynamic objects from their moving cast shadows for accurate vehicle detection and recognition. This paper provides an in-depth comparative analysis of different traffic paradigm-focused conventional and state-of-the-art shadow detection and removal algorithms. Till date, there has been only one brief survey which highlights the shadow removal methodologies particularly for traffic paradigm. In this paper, a number of research papers containing results of urban traffic scenes have been shortlisted from the last three decades to give a comprehensive overview of the work done in this area. The study reveals that the only way to make a comparative evaluation is to use the existing Highway I, II, and III datasets which are frequently used for qualitative or quantitative analysis of shadow detection or removal algorithms. Furthermore, the paper not only provides cues to solve moving cast shadow problems, but also suggests that even after the advent of Convolutional Neural Network (CNN)-based vehicle detection methods, the problems caused by moving cast shadows persists. Therefore, this paper proposes a hybrid approach which uses a combination of conventional and state-of-the-art techniques as a pre-processing step for shadow detection and removal before using CNN for vehicles detection. The results indicate a significant improvement in vehicle detection accuracies after using the proposed approach.
A Dirichlet Model of Alignment Cost in Mixed-Membership Unsupervised Clustering

Mixed-membership unsupervised clustering is widely used to extract informative patterns from data in many application areas. For a shared dataset, the stochasticity and unsupervised nature of clustering algorithms can cause difficulties in comparing clustering results produced by different algorithms, or even multiple runs of the same algorithm, as outcomes can differ owing to permutation of the cluster labels or genuine differences in clustering results. Here, with a focus on inference of individual genetic ancestry in population-genetic studies, we study the cost of misalignment of mixed-membership unsupervised clustering replicates under a theoretical model of cluster memberships. Using Dirichlet distributions to model membership coefficient vectors, we provide theoretical results quantifying the alignment cost as a function of the Dirichlet parameters and the Hamming permutation difference between replicates. For fixed Dirichlet parameters, the alignment cost is seen to increase with the Hamming distance between permutations. Datasets with low variance across individuals of membership coefficients for specific clusters generally produce high misalignment costs—so that a single optimal permutation has far lower cost than suboptimal permutations. Higher variability in data, as represented by greater variance of membership coefficients, generally results in alignment costs that are similar between the optimal permutation and suboptimal permutations. We demonstrate the application of the theoretical results to data simulated under the Dirichlet model, as well as to membership estimates from inference of human-genetic ancestry. The results can contribute to improving cluster alignment algorithms that seek to find optimal permutations of replicates. Supplementary materials for this article are available online.
HNPU-V2: A 46.6 FPS DNN Training Processor for Real-World Environmental Adaptation based Robust Object Detection on Mobile Devices

■ Smarter DNNs: # of Parameter ▲
Research on the Strategies of Improving English Speaking Teaching in Universities under the Perspective of Internet+

As a key part in the process of teaching English in colleges and universities, spoken English has a non-negligible role in the cultivation of students' comprehensive English literacy.However, from the current development of English teaching classroom activities in colleges and universities, There is a widespread phenomenon, college English teachers do not pay good attention to the status and role of English speaking classroom, and many of the students have cultivated the defects and shortcomings of only doing practice problems but not communicating fluently, which is an embarrassing situation for the cultivation of talent quality.At present, the coming of Internet+ era and the proposal of Internet+ education provide a new teaching and learning carrier for the teaching of spoken English in colleges and universities.Based on this, this paper conducts a research on the strategy of teaching English speaking in colleges and universities under the Internet+ perspective, in order to contribute to the improvement of the quality of English speaking teaching in colleges and universities.
Design Visions for Future Energy Systems: Towards Aligning Developers’ Assumptions and Householders’ Expectations

We increasingly see smart energy technology moving into peoples' homes, designed to support householders in strategically managing different forms of energy conservation. Often, design visions for such technologies assume householders will accept smart energy systems as long as the interaction remains effortless, efficient, and convenient. Likewise, developers rely on assumptions that smart energy technologies meeting such usability goals may urge householders to become active players in a future energy market. Yet, a growing body of HCI research illustrates that actively engaging people with sustainability issues through technology requires more than merely meeting usability goals. Against this backdrop, our study investigates the design vision of a cutting-edge home energy management system created as part of an EU project and householders' expectations of it. Our findings highlight (mis)alignments that emerged from a questionnaire study with 167 households participating in a field trial of the developed technology. We use the insights to discuss the alignment of sustainable visions, HCI, and the design of future energy systems.
Reversible Watermarking Approach for Ensuring the Integrity of Private Databases

Watermarking implies embedding of data in a manner thatit is easily accessible by a user that’s authenticated rather than other users, bearing in mind that the underlying data may experience some changes as a result of such data embedding. Reversible watermarking has been developed in advance of watermarking, whereby both of data quality and data recovery are guaranteed. For long time, the watermarking scheme to meet image, audio and video data type security was considered ever popular, but at the same time, since last several years ago relational database watermarking was really in mind and under study, even various watermarking approaches have been suggested. Nonetheless, these techniques yet are not sturdy enough against malignant attacks that can result in false insertion, modification or elimination leading to data performance and quality deterioration. So, and with aim of advancing and improving existing watermarking techniques, a new robust and reversible relational database watermarking method is suggested by which better outcomes were achieved.
Detecting Anomalies with $${{\textrm{Latent}}Out}$$: Novel Scores, Architectures, and Settings

$${{\textrm{Latent}}Out}$$ is a recently introduced algorithm for unsupervised anomaly detection which enhances latent space-based neural methods, namely (Variational) Autoencoders, GANomaly and ANOGan architectures. The main idea behind it is to exploit both the latent space and the baseline score of these architectures in order to provide a refined anomaly score performing density estimation in the augmented latent-space/baseline-score feature space. In this paper we extend the research on the $${{\textrm{Latent}}Out}$$ methodology in three directions: first, we provide a novel score performing a different kind of density estimation at a reduced computational cost; second, we experiment the combination of $${{\textrm{Latent}}Out}$$ with GAAL architectures, a novel type of Generative Adversarial Networks for unsupervised anomaly detection; third, we investigate performances of $${{\textrm{Latent}}Out}$$ acting as a one-class classifier. The experiments show that all the variants of $${{\textrm{Latent}}Out}$$ here introduced improve performances of the baseline methods to which they are applied, both in the unsupervised and in the semi-supervised settings.
Dual Stream Conditional Generative Adversarial Network Fusion for Video Abnormal Behavior Detection

Deep learning has been successfully applied to video anomaly detection. However, the way that deep network learn spatio-temporal features autonomously will ignore the specificity of different pattern features. Therefore, this paper focuses on how to efficiently learn deep appearance feature, introduces the idea of learning appearance information by predicting future frame, and proposes dual stream conditional generative adversarial network fusion for video abnormal behavior detection. The video frame and its corresponding optical flow image are transferred to the conditional generative adversarial network to learn the motion feature representation. In addition, inputting the video frame and its corresponding future frame to the network to generate the appearance representation complementary to motion feature. The model is only trained with normal events, therefore it is not able to generate abnormal events accurately. During the test, for the foreground moving targets, the images generated by the model are compared with the ground truth to obtain a two-stream anomaly probability distribution model based on the mean square error used to achieve the purpose of region anomaly detection. Experiments on the public datasets show that the proposed method can effectively detect and locate abnormal behaviors in the video.
Analysis Model of the Influence of Self-Efficacy on Professional Toughness of Preschool Teachers under the Condition of Ensuring Children’s Mental Health and Healthy Family Environment

Preschool teachers' professional toughness (PT) has received a lot of attention recently, and this research is one of the main areas of study for their ability to manage stress and develop normally. Based on how well a person thinks of his own abilities, SE (self-efficacy) is formed. 300 kindergarten teachers from 10 metropolitan kindergartens were chosen as research samples in order to examine the relationship between the SE of kindergarten teachers and their PT, as well as the mediating effects of professional identity and work passion. A paradigm for measuring teacher performance based on the BPNN (BP neural network) is proposed, and several data processing techniques are developed. The study's findings indicate that teachers' WE (work enthusiasm) fills a limited intermediary role between PT and SE; the intermediary effect value is 0.041 and accounts for 15.7% of the overall effect (0.6559). The conclusion is that in order to help new instructors cope with their negative feelings, it is important to engage in a variety of leisure activities.
Business Brand Research Based on Multi-Feature Fusion Emotion Analysis

With the deepening of globalization, brand plays an important role in determining the competitiveness of enterprises. It is worth thinking about how to quantify the brand value reasonably to achieve the purpose of improving the competitiveness of enterprises. The research of commercial brands based on emotion analysis extracts the views of consumers on the evaluation data of brand attributes, analyzes the emotional tendency of consumers' views, and then helps enterprises adjust their production strategies. The purpose of emotion analysis is to judge users' views and attitudes toward goods or services by analyzing texts. In this paper, the linear support vector machine model is used to extract and study the features in sentiment analysis. Then, the brand based on the fusion model is evaluated, and the experimental conclusions are drawn: the method of fusing the depth features extracted by word vector and the named entity extracted by CRF makes the brand effect of fusing emotional factors better than the feature extraction method only using CRF named entity recognition. It is proved that the model method proposed in this paper has a certain role in actual business. Feature fusion often combines shallow model methods and fuses various shallow feature screening technologies based on word segmentation results. This paper introduces fusion features and supplements feature vectors based on the results of the deep learning word vector model. Support Vector Machine (SVM) maps feature vectors to some sample points in the space, finds a plane that can realize sample classification, and maximizes the distance between the two sample points closest to the plane in two types of samples, to maximize the classification performance and improve the generalization of the model.
Multi-ontology mapping generative adversarial network in internet of things for ontology alignment

On the Semantic web, ontologies are thought to be the remedy to data heterogeneity, and correlating ontologies is a highly effective technique. Although the use of representation learning approaches to a variety of applications has showed significant promise, they have had little effect on the issue of ontology matching and classification. In order to establish alignments between two ontologies, this research presents the Multi-Ontology Mapping Generative Adversarial Network in Internet of Things (MOMGANI). For the instance of ontology mapping, we suggest using a two-system representation learning network consisting of a Generator and Discriminator. The Generator applies a probabilistic softmax classifier to the different Name, Label, Comments, Properties, Instance descriptions, concept characteristics, and the neighbourhood concepts for each of the ontology's properties. In order to support the assertions that the Generator has generated, the Discriminator network employs a novel Bidirectional Long Short-Term Memory (Bi-LSTM network) with an Ontology Attention mechanism enhanced by the concept's descriptions. As a result, both systems are in a feedback mechanism where they can learn from one another. The system will produce a set of triples that list all the associated concepts from various ontologies as its final product. Domain experts will review these triples outside of the band to ensure that only true concepts and triples are chosen for the alignment. In comparison to using the ontologies separately, the aligned ontology enables extended querying and inference across related ontologies and domains. Considering metrics like recall, precision, and F-measure, the experimental evaluation was performed utilizing the datasets for classes alignment, property alignment, and instances alignment. The proposed architecture provides a recall, precision, and F-measure of 0.92, 0.99, and 0.83 respectively which reveals that this model outperforms the traditional methods.
GAIN: A Gated Adaptive Feature Interaction Network for Click-Through Rate Prediction

CTR (Click-Through Rate) prediction has attracted more and more attention from academia and industry for its significant contribution to revenue. In the last decade, learning feature interactions have become a mainstream research direction, and dozens of feature interaction-based models have been proposed for the CTR prediction task. The most common approach for existing models is to enumerate all possible feature interactions or to learn higher-order feature interactions by designing complex models. However, a simple enumeration will introduce meaningless and harmful interactions, and a complex model structure will bring a higher complexity. In this work, we propose a lightweight, yet effective model called the Gated Adaptive feature Interaction Network (GAIN). We devise a novel cross module to drop meaningless feature interactions and preserve informative ones. Our cross module consists of multiple gated units, each of which can independently learn an arbitrary-order feature interaction. We combine the cross module with a deep module into GAIN and conduct comparative experiments with state-of-the-art models on two public datasets to verify its validity. Our experimental results show that GAIN can achieve a comparable or even better performance compared to its competitors. Furthermore, in order to verify the effectiveness of the feature interactions learned by GAIN, we transfer learned interactions to other models, such as Logistic Regression (LR) and Factorization Machines (FM), and find out that their performance can be significantly improved.
Role of Mobile Technology on the Learning Structure of the Students

Mobile era, considerably cellular phones, have proliferated swiftly with inside the unique nations.Mobile era improvements are speedy broadening the scope of mastering outdoor of formal education with the aid of using facilitating adaptable and instant get right of entry to rich virtual resources.M-learning can be a crucial addition to conventional education.Mobile gadgets can increase the mastering revel in past faculties and universities with the aid of using allowing instructional substances in a dynamic, portable, and unbiased mastering environment; they could offer college students with a method of communiqué each amongst themselves and with their instructors.The goal of this studies is to offer a complete evaluation of college student's willingness to apply M-learning in their curriculum.
Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification

We consider zero-shot cross-lingual transfer in legal topic classification using the recent Multi-EURLEX dataset. Since the original dataset contains parallel documents, which is unrealistic for zero-shot cross-lingual transfer, we develop a new version of the dataset without parallel documents. We use it to show that translation-based methods vastly outperform cross-lingual fine-tuning of multilingually pre-trained models, the best previous zero-shot transfer method for Multi-EURLEX. We also develop a bilingual teacher-student zero-shot transfer approach, which exploits additional unlabeled documents of the target language and performs better than a model fine-tuned directly on labeled target language documents.
Early Fusion Based CNN Architecture for Visual Servoing Systems

Visual feedback control refers to the usage of image data to design the motion of a robotic system. This type of problem is equivalent to generating linear and angular velocities that will drive a robotic systems from an initial image to target one. Classic visual servoing methods have disadvantages, such as high challenges on extracting and tracking visual features, regardless of the environmental conditions, and nonlinear dependencies regarding camera calibration. During the last years, these limitations have been alleviated by employing Convolutional Neural Networks (CNNs). The main goal of this work is to increase the performance of CNNs in visual feedback control expanding the neural input arrays with extra available data. For this, extra maps created via region-based segmentation are considered as input in an early fusion based architecture. These ready-to-use simplified descriptions of the initial and final layouts can help CNN understand the scenes, and compute accurate velocities. The role of the segmented maps is experimentally investigated on two different architectures that exemplify the suggested design idea. The results show that CNNs with input fusion offer a better approximation of the linear and angular velocities, and proper robustness to segmentation errors, as well.
Implementation of area-efficient AES using FPGA for IOT applications

The increase in internet technology has made the number of devices connected to the internet increase day by day. The information transferred over the electronic media is vulnerable to security attacks. To protect the information, the data transferred over the internet should be encrypted. Therefore, it is essential to design the encryption architecture which is suitable for the resource constrained devices. FPGA implementation of area-efficient AES encryption algorithm is presented in this work. The proposed architecture includes 8-bit data path which reduces the number of internal wires. There are two register banks, the state register and the key register, which are used for storing intermediate results along with performing the operations in encryption and key expansion phase which reduces the area. Substitute bytes operation is implemented using ROM based LUT structure. The mix columns operation with 8-bit data path is realised using the four internal registers. This reduces area which is suitable for IOT applications which is the primary objective.
Pedestrian Attribute Recognition Based on Multi-Scale Fusion and Cross Attention

Pedestrian Attribute Recognition has attracted increasing attention due to its wide range of potential applications. However, the pedestrian images are taken from a far distances significantly increase the difficulty of attribute feature extraction integrity. To solve these problems, and further improve the accuracy of pedestrian attribute recognition by convolutional neural networks, we proposed a method based on multi-scale feature fusion and cross attention. First, we use the ResNeXt50 structure to extract shallow and deep features, then fusion them for improving the recognition accuracy of attributes. Second, we adopt cross attention mechanism to generate pixel-level contextual information for enhancing the important feature information about attributes. Finally, we transmit the feature map into the fully connected layer for classification. Extensive experiments show that our proposed method could achieve state-of-the-art results on three pedestrian attribute datasets, including PA-100K, RAP, and PETA. The corresponding mean accuracies achieve 81.57%, 81.23% and 85.85%,respectively.