Reviewerly/exHarmony huggingface dataset:
A Shadow Detection Method for Retaining Key Objects in Complex Scenes

The existing shadow detection methods have achieved good results on standard shadow datasets such as SBU and UCF. However, in actual large-scale scenes, key objects covered by shadows are often regarded as shadows, which may harm computer vision tasks. In the paper, we are the first to propose the Object-aware Shadow Detection Network (OSD-Net) model for computer vision tasks in complex scenes. It introduces the direction-aware spatial context (DSC) module to detect shadows, uses semantic segmentation with Mask RCNN to extract key objects in the picture, and designs a function to perform mask fusion. Qualitative experiments have been performed to test OSD-Net on three public datasets commonly used in computer vision. Compared with popular shadow detection methods, OSD-Net is able to effectively protect the key targets in the picture from being misjudged as shadows, and ensure shadow detection accuracy.
Forground-Guided Vehicle Perception Framework

As the basis of advanced visual tasks such as vehicle tracking and traffic flow analysis, vehicle detection needs to accurately predict the position and category of vehicle objects. In the past decade, deep learning based methods have made great progress. However, we also notice that some existing cases are not studied thoroughly. First, false positive on the background regions is one of the critical problems. Second, most of the previous approaches only optimize a single vehicle detection model, ignoring the relationship between different visual perception tasks. In response to the above two findings, we introduce a foreground segmentation branch for the first time, which can predict the pixel level of vehicles in advance. Furthermore, two attention modules are designed to guide the work of the detection branch. The proposed method can be easily grafted into the one-stage and two-stage detection framework. We evaluate the effectiveness of our model on LSVH, a dataset with large variations in vehicle scales, and achieve the state-of-the-art detection accuracy.
Analysis on the Influencing Factors of Graduate Employment Quality Based on Training Data Mining

This paper takes an electronic information university with obvious industry characteristics as an example, and conducts data mining research on the school's 2016-2018 electronic information graduate training data. This paper on the basis of literature reading and sorting, select whether to enter the world's top 500 and whether to work in an economically developed area as the graduate employment quality evaluation indicators, and use 12 indicators that may affect the quality of graduate employment for cross-statistical analysis. Finally, 8 variables including the number of articles published, weighted grades of degree courses, academic scholarships, and book readings are comprehensively determined for further data mining. The research results show that, in addition to the objective factor of gender, the weighted grades of degree courses and academic scholarships have a significant positive impact on the high-quality employment of graduate students during the training process.
Deciphering the spectral collapse in two-photon Rabi model

Abstract In this communication, based upon a squeezed-state trial wave function, we have performed a simple variational study of the spectral collapse of the two-photon Rabi model. Our analysis indicates that the light-matter interaction and the spin-flipping effectively constitute two competing impacts upon the radiation mode. Whilst the former tries to decrease the radiation mode frequency, the latter may counteract or reinforce it, contingent upon the state of the atomic system. The light–matter interaction appears to dominate the frequency modulation as its coupling strength goes beyond the critical value, leading to the emergence of the spectral collapse. However, at the critical coupling the dominance of the light–matter interaction is not complete, and incomplete spectral collapse appears. The extent of incomplete spectral collapse is found to depend upon the energy difference between the two atomic levels as well.
Auto-generating Virtual Human Behavior by Understanding User Contexts

Virtual humans are most natural and effective when it can act out and animate verbal/gestural actions. One popular method to realize this is to infer the actions from predefined phrases. This research aims to provide a more flexible method to activate various behaviors straight from natural conversations. Our approach uses BERT as the backbone for natural language understanding and, on top of it, a jointly learned sentence classifier (SC) and entity classifier (EC). The SC classifies the input into conversation or action, and EC extracts the entities for the action. The pilot study has shown promising results with high perceived naturalness and positive experiences.
Learning Fairly With Class-Imbalanced Data for Interference Coordination

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods.
Challenges and Opportunities of Language Representation Model

Pre-trained distribution natural language representation has been shown to be effective for improving many natural language processing tasks. However, recent research shows that the pre-trained language model has obvious defects in robustness, interpretability and so on. This survey reviews the development of natural language representation model in detail, including but not limited to Word2Vec, Embeddings from Language Models, and Bidirectional Encoder Representations from Transformers. Furthermore, this paper analyzes advantage and disadvantages of existing models from multiple perspectives. Finally, the potential challenges of natural language representation model have been discussed in this survey.
Residual Learning of Video Frame Interpolation Using Convolutional LSTM

Video frame interpolation aims to generate intermediate frames between the original frames. This produces videos with a higher frame r ate and creates smoother motion. Many video frame interpolation methods first estimate the motion vector between the input frames and then synthesizes the intermediate frame based on the motion. However, these methods rely on the accuracy of the motion estimation step and fail to accurately generate the interpolated frame when the estimated motion vectors are inaccurate. Therefore, to avoid the uncertainties caused by motion estimation, this paper proposes a method that directly generates the intermediate frame. Since two consecutive frames are relatively similar, our method takes the average of these two frames and utilizes residual learning to learn the difference between the average of these frames and the ground truth middle frame. In addition, our method uses Convolutional LSTMs and four input frames to better incorporate spatiotemporal information. This neural network can be easily trained end to end without difficult to obtain data such as optical flow. Our experimental results show that the proposed method can perform favorably against other state-of-the-art frame interpolation methods.
Machine Vision and Big Data-Driven Sports Athletes Action Training Intervention Model

In recent years, athlete action recognition has become an important research field for showing and recognition of athlete actions. Generally speaking, movement recognition of athletes can be performed through a variety of modes, such as motion sensors, machine vision, and big data analysis. Among them, machine vision and big data analysis usually contain significant information which can be used for various purposes. Machine vision can be expressed as the recognition of the time sequence of a series of athlete actions captured through camera, so that it can intervene in the training of athletes by visual methods and approaches. Big data contains a large number of athletes’ historical training and competition data which need exploration. In-depth analysis and feature mining of big data will help coach teams to develop training plans and devise new suggestions. On the basis of the above observations, this paper proposes a novel spatiotemporal attention map convolutional network to identify athletes’ actions, and through the auxiliary analysis of big data, gives reasonable action intervention suggestions, and provides coaches and decision-making teams to formulate scientific training programs. Results of the study show the effectiveness of the proposed research.
Kubernetes Security: A Quick Reference

A new product is like a newborn child. You can’t just develop it and let it be in the air, it must be protected. As the product ages, either it acquires a customer base or it dies. It becomes successful only when there is a great correlation among all the variables responsible to fall or grow up that product. This paper focuses on providing efficient tools to make the Kubernetes and its environment secure so that a deployed product is very much protected. Security of Kubernetes has become a trend [1]. We believe the paper can have a great impact on decision-making during the deployment of SaaS products.
Blockchain and Smart Contracts: A Revolution

Blockchain is currently one of the hot topics in academia and industry, mainly due to the implications that the implementation of this modern technology could have. This paper examines the peer-to-peer network that forms the blockchain, from its origin to the most recent developments and applications in various industries and sectors (mostly in supply chain management, energy, education, and medicine) as well as the different forms it can adopt, primarily through smart contracts. Some of the main issues of this technology are also addressed, mainly through the analysis of scale, security, and misbehavior. These are the major concerns regarding blockchain and smart contracts after its expansion in recent years. While the application of blockchain technology still faces many issues that must be addressed, it is through further investigation and investments that blockchain becomes a revolutionary technology. Although today it is far from being the solution to most of the challenges in many industries (logistics, education, and energy), its features set hopes for a bigger significance in the years to come.
Advanced Environmental Monitoring Solution Using the Internet of Things (IoT) and Blockchain

Currently, the quality of the environment in urban areas is a matter of concern of countries in particular and the world in general. The application of new technology for this field is becoming increasingly urgent in the context of severe environmental pollution, environmental monitoring systems need to be upgraded to be able to effectively manage and evaluate the environment of a large geographic area. Transparency, accuracy and real time of data must be at the forefront to provide timely solutions when environmental pollution occurs to minimize socio-economic impacts.
Subspace Clustering via Joint Unsupervised Feature Selection

Any high-dimensional data arising from practical applications usually contains irrelevant features that may impact on the performance of existing subspace clustering methods. This paper proposes a novel subspace clustering method which reconstructs the feature matrix by the means of unsupervised feature selection (UFS) to achieve a better dictionary for subspace clustering (SC). Different from most existing clustering methods, the proposed approach uses the reconstructed feature matrix as the dictionary rather than the original data matrix. As the feature matrix reconstructed by representative features is more discriminative and closer to the ground-truth, it results in improved performance. The corresponding non-convex optimization problem is effectively solved using the half-quadratic and augmented Lagrange multiplier methods. Extensive experiments on four real datasets demonstrate the effectiveness of the proposed method.
Distance-Oriented Surface Skeletonization on the Face-Centered Cubic Grid

Strand proposed a distance-based sequential thinning algorithm for producing surface skeletons of binary objects sampled on the face-centered cubic (FCC) grid. In this paper, we present two modified versions of his algorithm, which are faster and - according to our experiments - less sensitive to the visiting order of border points in the sequential thinning phase.
Parallel Social Spider Optimization Algorithms with Island Model for the Clustering Problem

The digital age came with an extraordinary ability to generate data across organizations, people, and devices, data that needs to be analyzed, processed and stored. A well-known technique for analyzing this kind of data is Clustering. Many bio-inspired algorithms were proposed for this problem such as the Social Spider Optimization (SSO). In this work, we propose parallel island models of the SSO algorithm for the Clustering problem, using 24 processors for each parallel algorithm. Such models were implemented using static and dynamic topologies, and datasets from the UCI Machine Learning Repository used for the stage of experiments. The achieved average speedups range from 15 to 28 times faster than the SSO algorithm for large and small datasets, respectively, and a parallel model with static ring topology performs a little bit faster than the other parallel models. The parallel algorithms provide results with similar precision to the ones computed with the SSO algorithm.
Magnetact Animals: A Simple Kinetic Toy Kit for a Creative Online Workshop for Children

With the global COVID-19 pandemic that began in early 2020, it has become difficult to hold workshops that bring people together in one place. Consequently, demand for online events and virtual workshops is increasing so as to minimize the loss of learning opportunities. However, providing an experience of building things remotely is not easy because of the time and cost of preparing materials and equipment, and the difficulties of distance teaching. So, we designed a kinetic toy kit that can be sent in an envelope, be constructed with ease, and work without any batteries. The kinetic toy, constructed using magnetic sheets, cardboard, and paper, allows the users to design and create “animals” with variable motions. At the two online workshops we held for 15 participants from 3 to 11 years old, all of the children enjoyed building toys and some participants invented original mechanisms and new animals. In this paper, we describe the details of the toy kit and the online workshops that used it.
Beyond Traditional Cultural Heritage Recommender Systems: Suggesting Airbnb Experiences to Users

Traditional recommender systems suggest Cultural and Natural Heritage items to visitors by matching the target user to the available options, one-to-one. However, the increasing diffusion of informal activities and events, supported by location-based services such as Airbnb, extends personalized recommendation to a many-to-one match-making task. Airbnb experiences, which any citizen can propose to offer geographic tours and thematic activities, are composed of at least two entities to be evaluated: the former is the experience itself (in terms of topic, cost, etc.); the latter is the host, who directly interacts with guests during the management of the planned activities. As both entities can dramatically influence guests’ perceptions, they should be jointly taken into account by recommender systems. This paper presents our preliminary work aimed at extending the personalized suggestion of Cultural Heritage items to such composite objects.
Corpus Annotation System Based on HanLP Chinese Word Segmentation

The Guangdong-Hong Kong-Macao Greater Bay Area is one of the most open and economically dynamic areas in China. It has been playing an important strategic role in the overall national development. The diversified languages and cultures in Hong Kong, Macau and Guangdong makes language problems much more complicated. Natural language processing based on deep learning has become an important method to construct and develop standardized semantic rules for language resources in Guangdong, Hong Kong and Macao. Therefore, a large amount of correct corpus data has become the basis for solving such problems. The corpus annotation system based on HanLP Chinese word segmentation performs preliminary annotation on the uploaded corpus, which is further proofread by annotators. In addition, the system cross-validate and submit the annotation results to output high-quality corpus. The corpus annotation system consists of a user module and an administrator module, with functions such as full-text corpus retrieval and data statistics based on Elasticsearch. As a natural language processing tool, HanLP Chinese word segmentation is used for storing the annotated corpus word segmentation in the system, which has certain advantages in corpus analysis and mining. This corpus annotation system can provide a large amount of high-quality corpus data for the semantic analysis of the Guangdong-Hong Kong-Macao Greater Bay Area.
Detection Method of Malicious Mirroring Site in Mass Network Traffic

This paper proposes a method for detecting malicious mirrored websites under large-scale network traffic. This method passively extracts webpage source code from network traffic and actively obtains webpage snapshots through a combination of active and passive methods, extracts corresponding features for similarity comparison, and detects malicious Mirror web pages. The experiment used 1447 malicious webpages as benchmark webpages. In a large-scale network flow environment, 49 phishing webpages, 13 gambling webpages, 23 obscene and pornographic webpages, and 8 illegal webpages were detected. The accuracy rate of the algorithm is 93.94%, the recall rate is 92.08%, and the F value is 0.93, which verifies that the malicious mirror webpage detection algorithm proposed in this paper is practical and effective.
Using Machine Learning to Identify Movie Genres through Online Movie Synopses

Movie synopsis is important for audience to know about a movie within a short time. A good movie synopsis should reflect the genre, structure and main plot of a certain movie. The aim of this paper is to use machine learning to identify the genres of movie through movie synopses. The movies and corresponding synopses in database are downloaded from the Kaggle and ROTTEN TOMATOES websites. This study uses two supervised learning models (k-NN and SVM) and two deep learning models (CNN and RNN) to classify the genres of movie through movie synopses. Secondly, it tries to eliminate the interference by actively eliminating proper nouns. Finally, it compares and analyzes the performance of all models in different training sets. The result is that RNN with LSTM layer is the most suitable model for analyzing a large number of text for movie synopses, and the accuracy of judging movie genres is 80.5%. This study promotes the understanding of machine learning model selection in the adoption of movie genres classification based on movie synopses.
Vulnerability Detection is Just the Beginning

Vulnerability detection plays a key role in secure software development. There are many different vulnerability detection tools and techniques to choose from, and insufficient information on which vulnerability detection techniques to use and when. The goal of this research is to assist managers and other decision-makers on software projects in making informed choices about the use of different software vulnerability detection techniques through empirical analysis of the efficiency and effectiveness of each technique. We will examine the relationships between the vulnerability detection technique used to find a vulnerability, the type of vulnerability found, the exploitability of the vulnerability, and the effort needed to fix a vulnerability on two projects where we ensure all vulnerabilities found have been fixed. We will then examine how these relationships are seen in Open Source Software more broadly where practitioners may use different vulnerability detection techniques, or may not fix all vulnerabilities found due to resource constraints.
RICAPS

The field of broadcast sports video analysis requires attention from the research community. Identifying the semantic actions within a broadcast sports video aids better video analysis and highlight generation. One of the key challenges posed to sports video analysis is the availability of relevant datasets. In this paper, we introduce a new dataset SP-2 related to broadcast sports video (available at https://github.com/abdkhanstd/Sports2). SP-2 is a large dataset with several annotations such as sports category (class), playfield scenario, and game action. Along with the introduction of this dataset, we focus on accurately classifying the broadcast sports video category and propose a simple yet elegant method for the classification of broadcast sports video. Broadcast sports video classification plays an important role in sports video analysis as different sports games follow a different set of rules and situations. Our method exploits and explores the true potential of capsule network with dynamic routing, which was introduced recently. First, we extract features using a residual convolutional neural network and build temporal feature sequences. Further, a cascaded capsule network is trained using the extracted feature sequence. Residual inception cascaded capsule network (RICAPS) significantly improves the performance of broadcast sports video classification as deeper features are captured by the cascaded capsule network. We conduct extensive experiments on SP-2 dataset and compare the results with previously proposed methods, and the results show that RICAPS outperforms the previously proposed methods.
Deeply-fused Attentive Network for Stereo Matching

In this paper, we propose a novel learning-based network for stereo matching called DF-Net, which makes three main contributions that are experimentally shown to have practical merit. Firstly, we further increase the accuracy by using the deeply fused spatial pyramid pooling (DF-SPP) module, which can acquire the continuous multi-scale context information in both parallel and cascade manners. Secondly, we introduce channel attention block to dynamically boost the informative features. Finally, we propose a stacked encoder-decoder structure with 3D attention gate for cost regularization. More precisely, the module fuses the coding features to their next encoder-decoder structure under the supervision of attention gate with long-range skip connection, and thus exploit deep and hierarchical context information for disparity prediction. The performance on SceneFlow and KITTI datasets shows that our model is able to generate better results against several state-of-the-art algorithms.