Reviewerly/exHarmony huggingface dataset:
Infant Action Database: A Benchmark for Infant Action Recognition in Uncontrolled condition

Abstract The main focus of our work is to create a database for action recognition of unattended infants in uncontrolled environment with wide variations in surroundings, lighting, interactions with objects, camera motions, etc., Action recognition of infants is emerging as an important and technically challenging computer vision problem as compared with adult action recognition because of their physical appearance. Most of the previous action recognition techniques have focused on the recognition of action captured under a controlled environment in a standard laboratory setting. In this study a novel database is introduced that can be used as a benchmark for surveillance parenting. This database involves nine normal and nine abnormal actions classes which consist of actions and movements of infants occurring in fairly uncontrolled conditions. This database consists of realistic user-uploaded videos which are recorded in the clustered background and different camera motion. After the collection of all videos, they are manually trimmed to form a database. To further evaluate the performance of the database, HOG features were extracted from database set and trained by different Machine Learning classifiers like Multi-class Naive Bayes, Support Vector Machine, Ensemble classifier, Discriminant analysis and Decision tree classifiers. This experimentation shows that the database is complex and robust that can serve as a base for testing action recognition algorithms.
User Representation Learning for Social Networks: An Empirical Study

Gathering useful insights from social media data has gained great interest over the recent years. User representation can be a key task in mining publicly available user-generated rich content offered by the social media platforms. The way to automatically create meaningful observations about users of a social network is to obtain real-valued vectors for the users with user embedding representation learning models. In this study, we presented one of the most comprehensive studies in the literature in terms of learning high-quality social media user representations by leveraging state-of-the-art text representation approaches. We proposed a novel doc2vec-based representation method, which can encode both textual and non-textual information of a social media user into a low dimensional vector. In addition, various experiments were performed for investigating the performance of text representation techniques and concepts including word2vec, doc2vec, Glove, NumberBatch, FastText, BERT, ELMO, and TF-IDF. We also shared a new social media dataset comprising data from 500 manually selected Twitter users of five predefined groups. The dataset contains different activity data such as comment, retweet, like, location, as well as the actual tweets composed by the users.
Learn to Optimize Panchromatic Imagery for Accurate Building Extraction

Due to the limited training data, current data-driven algorithms, including deep convolutional networks (DCNs), are susceptible to training data that cannot be applied to new data directly. Unlike existing methods that are trying to improve model generation capability using limited data, we introduce a learning-based image translation method to generate data that share the same characteristics of target data. The low-resolution panchromatic satellite images are converted into high-resolution color images through interpolation and colorization with the proposed symmetric colorization network (SCN). Experiments on a very-high-resolution (VHR) dataset show that images generated by our SCN are with both quantitatively and qualitatively high color fidelity. Furthermore, we also demonstrate that high extraction accuracy is retained during the model transferring from aerial to satellite images. For pre-trained feature pyramid network (FPN), compared to the performance on raw panchromatic images, the interpolated and colorized images increase 305.7% of recall (0.929 vs. 0.229), 78.2% of overall accuracy (0.768 vs. 0.431), 132.5% of f1-score (0.851 vs. 0.366), and 230.8% of Jaccard index (0.741 vs. 0.224), respectively.
Object Decoupling with Graph Correlation for Fine-Grained Image Classification

Fine-grained image classification has drawn increasing attention as it is much closer to practical applications than generic image classification. The majority of current fine-grained approaches locate the discriminative regions and leverage the features of these regions for classification as their magic weapons. However, these approaches simply ignore the internal semantic region correlation. As is well known, the correlation reveals the salient information of images, which can further boost the performance of fine-grained image classification. To this end, we propose an Object Decoupling with Graph Correlation network (ODGC) to explore the informative potentials of region correlation. A Responsive Object Location Module (ROLM) is first introduced to obtain the fine-grained object within a bounding box automatically. A Semantic Decoupling Module (SDM) then segments the object into different parts. ODGC learns the representations of these parts by transferring these part features into a Graph Correlation Module (GCM). Consists of these three main modules, ODGC is trained for fine-grained image classification in an end-to-end way. Extensive experiments conducted on CUB-200-2011 demonstrate that the aforementioned modules significantly improve the ODGC, and it achieves a new state-of-the-art performance to 88.2% top-1 accuracy. Besides, we collect a practical business e-commercial dataset, named Ecom-15K. The evaluation on it further validates the applicability of our method in practical scenarios.
Data-Driven Channel Pruning towards Local Binary Convolution Inverse Bottleneck Network Based on Squeeze-and-Excitation Optimization Weights

This paper proposed a model pruning method based on local binary convolution (LBC) and squeeze-and-excitation (SE) optimization weights. We first proposed an efficient deep separation convolution model based on the LBC kernel. By expanding the number of LBC kernels in the model, we have trained a larger model with better results, but more parameters and slower calculation speed. Then, we extract the SE optimization weight value of each SE module according to the data samples and score the LBC kernel accordingly. Based on the score of each LBC kernel corresponding to the convolution channel, we performed channel-based model pruning, which greatly reduced the number of model parameters and accelerated the calculation speed. The model pruning method proposed in this paper is verified in the image classification database. Experiments show that, in the model using the LBC kernel, as the number of LBC kernels increases, the recognition accuracy will increase. At the same time, the experiment also proved that the recognition accuracy is maintained at a similar level in the small parameter model after channel-based model pruning by the SE optimization weight value.
Asymmetric Loss for Positive-Unlabeled Learning

Positive-unlabeled (PU) learning is a learning paradigm when only positive and unlabeled data are available in the training stage. This paradigm is particularly useful for the applications that the negative samples are hard to define or expensive to obtain. We propose a simple yet effective and scalable method to address PU learning with a novel asymmetric loss. The proposed asymmetric loss behaviors differently for the prediction errors of the labeled and unlabeled samples, and thus encourages the identification of the negative examples from the unlabeled set. For the PU learning with SCAR assumption, neither hyper-parameter nor class prior is required to be tuned or known. For the situation with selection bias on the labeled samples, we propose a heuristic method to automatically choose the hyper-parameter according to the class prior on the training data. Compared with previous approaches, our method only requires a slight modification of the conventional cross-entropy loss and is compatible with various deep neural networks in an end-to-end way. Extensive experiments on synthetic and real-world datasets with and without SCAR assumption verify the superior performance of the proposed method.
Requirements Elicitation for Applications Running on a Blockchain: Preliminary Results

Blockchain has been one of Gartner’s Top 10 Strategic Technology Trends for several consecutive years. The technology has evolved from a platform allowing transactions of cryptocurrency between peers (e.g. Bitcoin) to a platform allowing the design of Decentralized Applications (DApps). Despite their growing popularity, little attention has been paid to the Software Engineering aspect of DApps. In this work, we aim to start bridging this gap by addressing the Requirements Engineering of DApps. We collect, analyze and integrate DApp user reviews in order to propose a first list of user requirements for DApps. The results can have practical implications for both practitioners and researchers. The former can use the results to guide them in the design of DApps, while the latter can see this paper as a first result to build upon to advance the software engineering field of blockchain-based applications.
Wireless Channel Data Augmentation for Artificial Intelligence of Things in Industrial Environment Using Generative Adversarial Networks

The rise of Artificial Intelligence of Things (AIoT) makes everything connected smartly, which can enrich industrial productivity. With the help of learning-based wireless communications, terminals in AIoT can communicate with each other and collaborate intelligently. However, one of the critical issues faced in AIoT deployment is the lack of available large datasets for the training of artificial intelligence algorithms in the industrial environment. In this paper, we propose a channel data augmentation algorithm for the dataset limitation cases in intelligent industrial wireless communication systems using generative adversarial networks (GAN). Specifically, we first show the performance of deep learning-based channel state information (CSI) feedback algorithm trained with different size of datasets. Then we develop a GAN for the channel data augmentation to enhance the performance of CSI feedback algorithm. Finally, we apply the enhanced approach to an insufficient dataset for the performance evaluation. Experimental results show that our method can achieve at most 3dB performance improvement than other traditional data augmentation approaches in increasing the accuracy of CSI feedback algorithm when the size of dataset is limited to 10000.
A kind of fast Gaussian particle filter based on Artificial Fish School Algorithm

This paper proposes an improved Gaussian particle filter integratingthe Artificial Fish School Algorithm to optimise the measured values to improve the overall estimation accuracy of the system. Meanwhile, it also solves the problems of susceptibility to interference and insufficient estimation accuracy in nonlinear systems. Furthermore, since the calculation time of the fusion algorithm increases, in order to ensure the speed of state estimation, the linear transformation of standard particle swarm is used to replace the particle sampling link of Gaussian particle filter. Simulation results show that the calculation speed of a fast Gaussian Particle Filter based on the Artificial Fish School Algorithm is 21.7% faster than the Particle Filter based on the Artificial Fish School Algorithm. Compared with Particle Filter, Gaussian particle filter, and the Artificial Fish School Algorithm, the proposed algorithm has a higher accuracy.
Object Design System by Interactive Evolutionary Computation Using GAN with Contour Images

This paper proposes a method to design objects by interactive evolutionary computation (IEC) using generative adversarial network (GAN) with the contour image of the object which the user wants to design. Here, a conditional GAN is adopted in order to utilize the contour image. GANs can generate lifelike images of objects, however, it is hard to control the output, since GAN just generates images randomly with the distribution learned from training data. Thus, IEC is introduced here to control the latent vectors in the conditional GAN. Moreover, using the contour image, conditional GAN can efficiently generate images of objects which the user prefers. In the proposed method the latent vectors in the conditional GAN are optimized for each user, so that the design is satisfactory to him/her through the process of IEC. Since IEC is good at optimizing system parameters on the basis of human subjective criteria, the user can obtain preferable lifelike design image. Experiment results show the high performance of this method.
Efficient boolean SSE: A novel encrypted database (EDB) for biometric authentication

Biometric authentication is up-and-coming to replace the traditional identity authentication method (e.g., passwords, PIN, identification cards) for its convenience and intelligence. With more and more users using this method, the database becomes more extensive, and the functions are seriously challenged. Data outsourcing has advantages in terms of convenience and cost savings, so it has attracted much research effort. However, due to the biometric's immutability of the whole life, it is extremely sensitive, and disclosing it to a third party is undesirable. In this paper, we address the issue of securely outsourcing biometric database. We propose a novel boolean searchable symmetric encryption (SSE) to construct a secure interactive protocol when outsourcing. A new encrypted database construction method was proposed, using the more efficient boolean vectors. Based on this, We suggest three kinds of expressive SSE, supporting disjunctive query, boolean query, and lightweight settings. We prove the schemes' correctness and security theoretically. Our constructions use simple cryptographic tools, such as symmetric cryptography and pseudo-random functions. They are straightforward to understand and easy to implement. The experiments show that all our schemes are practical and more efficient than the existing methods.
Syntax Vector Learning Using Correspondence for Natural Language Understanding

Natural language understanding (NLU) is a core technique for implementing natural user interfaces. In this study, we propose a neural network architecture to learn syntax vector representation by employing the correspondence between texts and syntactic structures. For representing the syntactic structures of sentences, we used three methods: dependency trees, phrase structure trees, and part of speech tagging. A pretrained transformer is used to propose text-to-vector and syntax-to-vector projection approaches. The texts and syntactic structures are projected onto a common vector space, and the distances between the two vectors are minimized according to the correspondence property to learn the syntax representation. We conducted massive experiments to verify the effectiveness of the proposed methodology using Korean corpora, i.e., Weather, Navi, and Rest, and English corpora, i.e., the ATIS, SNIPS, Simulated Dialogue-Movie, Simulated Dialogue-Restaurant, and NLU-Evaluation datasets. Through the experiments, we concluded that our model is quite effective in capturing a syntactic representation and the learned syntax vector representations are useful.
Determination System for House Improvement Recipients In Serdang Bedagai By Using Clustering K-Means Method And Višekriterijumsko Kompromisno Rangiranje (Vikor)

Abstract To improve the welfare of the community, the Government created a program called Bantuan Stimulan Perumahan Swadaya (BSPS) or better known as house improvement program. One of the implementations is in Kabupaten Serdang Bedagai. Therefore, we need a system capable of grouping the house situation of the poor then ranked to determine the priority in obtaining Program Bedah Rumah. For grouping were done using k-means clustering method consists of several criteria: welfare status, number of individuals in the family, type of floor, type of wall material, type of roof, use of toilet facility, type of toilet, and final disposal place. Then in ranking is used one of the multi-attribute decisions making method Višekriterijumsko Kompromisno Rangiranje (VIKOR). The criteria were used in ranking are the sources of drinking water, the primary lighting source and the cooking fuel. From the test results of 1,180 poor households (RTM) data produced 3 clusters of 538 RTM in cluster 1, then 593 RTM on cluster 2 and 49 RTM on cluster 3. The house improvement program assistance on the first year will be given to 185 RTM, then second year for 162 RTM, third year for 300 RTM, fourth year for 250 RTM, fifth year for 150 RTM and the last year for 133 RTM.
Privacy-Oriented Successive Approximation Image Position Follower Processing

In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.
A novel holes filling method based on layered depth map and patch sparsity for complex-scene images

The depth image based rendering (DIBR) is a key technology in the emerging 3D video and 3D-TV, which uses a single texture image and the corresponding depth map to generate the virtual views. A critical problem occurs when the virtual view generation refers to regions covered by foreground objects in the original view, which might be disoccluded in the synthetized view. In this paper, a holes filling method based on layered depth map and patch sparsity for complex scenes is proposed. The proposed approach consists of three parts. The first part refers to use of the depth information to divide the complex scene into simple scenes, which represents the separation of foreground and background with a special depth value using an adaptable layered method based on the depth map. The second part represents the holes filling in the depth map using the patch sparsity. Finally, in the third part, the background information and the filled depth map are used to fill the holes in the color map. The experimental results have indicated that compared to other methods, the proposed method provides better quality of synthetized complex scene and 3D scene.
Research on the Relationship Between Computer Educational Teaching and College Physical Education Reform

Abstract Every school wants its students to have an optimistic attitude and healthy body. However, the teaching strategies of many schools are evading this theory. The emergence of exam oriented education is that many schools do not pay attention to students’ physical education. Today’s students’ physical quality is very poor. Many parents are also worried about their children’s health[4]. Experts believe that the relationship between computer education technique and physical education reform is very close. This paper describes the influence of computer educational teaching on physical education. Finally, this paper puts forward the specific application relationship between them.
A novel CNN method for the accurate spatial data recovery from digital images

The parsing of floorplans has been an issue for a long time in automated document processing and with algorithmic methods until recent years. This problem has also improved output with the emergence of convolutionary neural networks (CNN). The job here is to obtain spatial and geometrical data from floorplans as accurately as possible. The aim of this project is to extract the most information from a floor plan image around instance segmentation models like Cascade Mask R-CNN. A new style of key point CNN is being implemented to supplement the segmentation to find correct corner positions. Then the resulting segmentation is combined in a post-processing stage. With a mean IoU of 72.7 percent versus 57.5 percent, the resulting segmentation scores surpass the existing baseline of the CubiCasa5k floorplan data base. Moreover, for almost every class, the mean IoU for each class is increased. Cascade Mask R-CNN has also been shown to be better suited to this role than Mask R-CNN.
Elitist Guided Parameter Adaptive Brain Storm Optimization Algorithm

With the increasing complexity of continuous optimization problems, the requirement of solving algorithms is higher and higher. To improve the performance of brain storm optimization algorithm, an elitist guided parameter adaptive BSO (EGBSO) is proposed in this paper. The population is sorted in the objective space based on the fitness. The top M individuals are regarded as elitists to guide the ordinary individuals to cluster, which accelerates the convergence speed of the algorithm. The updating mechanism of elite guidance is introduced, which utilizes the cooperation between global optimal individual and elitists to guide the population to a better direction. An adaptive selection parameter is set to make the algorithm more inclined to global search in the early stage and local search in the later stage, balancing the exploration and exploitation capabilities. The proposed EGBSO algorithm and three comparison algorithms are tested on the CEC2017 benchmark test suit, and the experimental results show that the EGBSO has good performance in solving complex optimization problems.
Contour Based Deep Learning Engine to Solve CAPTCHA

A 'Completely Automated Public Turing test to tell Computers and Humans Apart' or better known as CAPTCHA is a image based test used to determine the authenticity of a user (ie. whether the user is human or not). In today's world, almost all the web services, such as online shopping sites, require users to solve CAPTCHAs that must be read and typed correctly. The challenge is that recognizing the CAPTCHAs is a relatively easy task for humans, but it is still hard to solve for computers. Ideally, a well-designed CAPTCHA should be solvable by humans at least 90% of the time, while programs using appropriate resources should succeed in less than 0.01% of the cases. In this paper, a deep neural network architecture is presented to extract text from CAPTCHA images on various platforms. The central theme of the paper is to develop an efficient & intelligent model that converts image-based CAPTCHA to text. We used convolutional neural network based architecture design instead of the traditional methods of CAPTCHA detection using image processing segmentation modules. The model consists of seven layers to efficiently correlate image features to the output character sequence. We tried a wide variety of configurations, including various loss and activation functions. We generated our own images database and the efficacy of our model was proven by the accuracy levels of 99.7%.