Reviewerly/exHarmony huggingface dataset:
A Recurrent Reinforcement Learning Approach for Small Object Detection with Dynamic Refinement

Small object detection is one of the challenging tasks in recent computer vision researches. The low resolution and noisy representation are internal reasons associated with small object detection. To better address this task, we propose a novel reinforcement learning approach with dynamic refinement. Specifically, we design a Recurrent Reinforcement Module (RRM) to iteratively learn contextual information and improve semantic label dependency and the image-label relevance. Besides, the Dynamic Refinement Module (DRM) is designed to dynamically adjust the attentive regions that are related to small objects. To evaluate the effectiveness of our proposed approach, we design extensive experiments and comparisons on four datasets (i.e. PASCAL VOC, MS COCO, Google AVA and UA-DETRAC). The comprehensive experiment results demonstrate that our approach has the superiority of small object detection for both images and videos.
A Feasibility Study on the Popularization of Tai Chi with Virtual Reality Technology

On December 17, 2020, Tai Chi was included in Representative List of the Intangible Cultural Heritage of Humanity, which attracted worldwide attention. In order to promote Tai Chi, this study analyses the limitations including teacher shortage, insufficient training time, and inconsistent gestures, and attempts to use virtual reality (VR) technology to break through these restraints. This research has found that VR can help escape the limits in teaching stuff and practice time, and also stimulate learning interest and other advantages. To introduce Tai Chi to the world, we should strictly filter promotion content, unifying the movements and making specialised VR materials; establish an VR online learning platform; conduct online Duanwei assessment based on the requirements of the Duanwei system; combine online and offline teaching; gradually introduce virtual products of routines, push-hand, and Sanshou and other forms.
Design of the Safe Operation Management and Control System of Ring Main Unit based on Internet of Things Technology

Aiming at the problems of insufficient management and control in the process of operation and maintenance of ring main unit in traditional power system, the systematic, real-time, safety and standardized management and control are not rigorous, the process control method of operation and maintenance of ring main unit is researched. Through the organic integration of Internet of Things equipment and the operation process of operation and maintenance, combing and improving the operation process of ring main unit, closed-loop management is realized. On this basis, a design method of safety management and control system for operation and maintenance of ring main unit based on Internet of Things technology is proposed which can improve the safety and standardization of ring main unit operation by intelligent means, ensure the safety of operators and realize the active prevention of safety risks.
Next Item Prediction Using Neural Networks with Embedding Initialized Weights

Session-based recommendation systems became a very part of humankind’s daily life, as a result of the increasing transaction volume of e-commerce and e-marketing fields. Companies that can analyze the trails left by their current users on their systems and know their customers better than other firms, can become one step ahead of their rivals. In this concept, representations of items become a key point when related deep learning models are investigated closer. Since the relationship between each item within a session will have a direct effect on the task that involves predicting the next item in that session, extracting these relationships among each item needs to be handled in an effective manner. Using auto encoder systems to achieve the task of revealing hidden features between items and representing these relationships in a more meaningful way, will result in both boosting current state-of-art models’ performance and offering new session-based methods that can overperform the current state-of-art models. In this paper, state-of-the-art graph neural networks SR-GNN’s and TA-GNN’s weights are initialized with item embeddings that are obtained from autoencoder with RBM layers, and the performance of the models are compared with random weight initialization. According to the proposed weight initialization, using pre-trained item embeddings will increase the performance of the recommender model. Thanks to the pre-trained item embeddings, the hidden relationship between items modeled in a better way, and the introduced model has overperformed the current state-of-art techniques.
Improving the Performance of Frequently Used Korean Handwritten Character Verification Based on Artificial Intelligence through Multimodal Fusion

Handwriting verification is a biometric recognition field that identifies individuals’ unique characteristics contained in their handwriting. A single written character shows subtle differences depending on habits accumulated over time or the manner of writing. Based on this, it is often adopted in forensic investigations and as evidence in court. Existing handwriting verification is conducted by an expert, and is affected by the expert’s ability or subjectivity, causing different results to arise depending on the expert. Therefore, we propose a handwriting verification method that excludes human subjectivity and has objectivity. Using computer vision and artificial intelligence (AI), we derived results that excluded human subjectivity, and the judgment strength was expressed through a likelihood ratio. To improve the existing method’s accuracy, we performed a more accurate verification through multimodal use from the biometric field. Multimodal handwriting verification is conducted using up to four characters (not just one) because individual handwriting in each character is different. For learning, n-fold tests were conducted to maintain test objectivity, and the average performance of single character-based verification was 80.14% and the multimodal method averaged 88.96%. Here, we proposed the objectivity of handwriting verification through learning using AI, and show that performance improved through multimodal fusion.
Analysis of Regional Imbalances in Italy Based on Cluster Analysis

In 2021 ISTAT presented the Report on Equitable and Sustainable Well-being (BES 2020), consisting of a system of indicators that follow the significant changes that have characterized the Italian society in the last 10 years. With the integration of new indicators, realized in coherence with the fundamental lines of the Next Generation EU, there has been an enrichment of information on the country system concerning health aspects, education and training, and economic well-being. The 20 Italian regions, the 2 autonomous provinces of Bolzano and Trento, the 3 territorial divisions North, Center, South and the total of Italy constituting a set of 26 territorial units, have been described each with a set of 36 numerical indicators, concerning the areas of Health, Education and Training, Economic Wellbeing. These areas are the most suitable for highlighting regional imbalances in Italy. In this paper has been analyzed the input data matrix, made up of 26 rows, one for each of the territorial units, and of 36 columns, the number of descriptors used for each territorial unit, by means of a factor analysis, using the principal components method, in order to construct a regional taxonomy characterized by those of the 36 indicators that are most correlated with each of the factors that have emerged. Moreover, starting from the coordinates calculated for each of the 26 territorial units in the factor space, a cluster analysis of the 26 territorial units was carried out, using the connected graph method, in order to highlight the territorial similarities and differences in Italy.
Channel Pruning via Multi-Criteria based on Weight Dependency

Channel pruning has demonstrated its effectiveness in compressing ConvNets. In many related arts, the importance of an output feature map is only determined by its associated filter. However, these methods ignore a small part of weights in the next layer which disappears as the feature map is removed. They ignore the phenomenon of weight dependency. Besides, many pruning methods use only one criterion for evaluation and find a sweet spot of pruning structure and accuracy in a trial-and-error fashion, which can be time-consuming. In this paper, we proposed a channel pruning algorithm via multi-criteria based on weight dependency, CPMC, which can compress a pre-trained model directly. CPMC defines channel importance in three aspects, including its associated weight value, computational cost, and parameter quantity. According to the phenomenon of weight dependency, CPMC gets channel importance by assessing its associated filter and the corresponding partial weights in the next layer. Then CPMC uses global normalization to achieve cross-layer comparison. Finally, CPMC removes less important channels by global ranking. CPMC can compress various CNN models, including VGGNet, ResNet, and DenseNet on various image classification datasets. Extensive experiments have shown CPMC outperforms the others significantly.
Automated Verification of Sidewall Core Recovery Depth using Borehole Image Logs

Abstract Sidewall coring is a cost-effective process to complement conventional fullbore coring. Because sidewall cores target exact depth points, verification of the sidewall core recovery depth is required. We present an automated, fast workflow to perform the depth verification using borehole images, thereby providing consistent results. An application example using a typical dataset is used to showcase the workflow. A novel automated approach based on image analysis techniques and Bayesian statistical analysis is developed to verify sidewall core recovery depth using borehole image logs. A complete workflow is presented covering: 1) utilization of reference logs, e.g., gamma ray, to correct image log depth using cross correlation and/or dynamic time warping, 2) automated identification of sidewall core cavity in borehole image log using the circle Hough transform, and 3) estimation of confidence in the identification using Bayesian statistics and specialized metrics. The workflow is applied on a typical dataset containing tens of sidewall core cavities with varying quality. Results are comparable to the manual interpretation from an experienced engineer. A number of observations are made. First, the use of reference logs to correct the image log allows for determining the exact well logs values where the sidewall core was sampled, which is then compared to the initial target well logs values. This increases the confidence that the target lithofacies was sampled as planned. Second, the circle Hough Transform is suitable for this problem because it provides stable solutions for partially imaged sidewall core cavities typical in pad-based borehole images. Third, the use of Bayesian statistics and specialized metrics for the problem, such as average and standard deviation borehole image intensity in the cavity, provides customizability to work with multiple types of borehole images and with varying initial depth guess uncertainties. Overall, the use of fast and automated methodology for depth verification opens up avenues for near real-time combined sidewall coring, imaging, and verification workflows. The novelty in this study lies in using a combination of image processing techniques and statistical analysis to automate an established manual workflow. The automated workflow provides consistent results in minutes rather than hours. Results also incorporate a confidence index estimation.
Construction of LWCNN Framework and its Application to Pedestrian Detection with Segmentation Process

To solve the challenges in traffic object identification, fuzzification, and simplification in a real traffic environment, it is highly required to develop an automatic detection and classification technique for roads, automobiles, and pedestrians with multiple traffic objects inside the same framework. The proposed method has been evaluated on a database with complicated poses, motions, backgrounds, and lighting conditions for an urban scenario where pedestrians are not obstructed. The suggested CNN classifier has an FPR of less than that of the SVM classifier. Confirming the significance of automatically optimized features, the SVM classifier's accuracy is equal to that of the CNN. The proposed framework is integrated with the additional adaptive segmentation method to identify pedestrians more precisely than the conventional techniques. Additionally, the proposed lightweight feature mapping leads to faster calculation times and it has also been verified and tabulated in the results and discussion section.
Predicting Biometric Error Behaviour from Speaker Embeddings and a Fast Score Normalization Scheme

For the task of speaker recognition from audio, it is known that speakers experience different levels of error rates. In this work, predicting the proneness to false alarm and false reject of a given speaker embedding is investigated. Although exact prediction of biometric error behaviour appears to be a difficult problem, it is seen that the tendency to false alarm and false reject errors can be predicted directly from embeddings by training a neural network in a supervised manner. This prediction might be useful for several applications such as normalization of verification scores, incorporating those characteristics in embedding training or using it as an adversarial objective. We have utilized this predicted behaviour for a fast score normalization method. Our approach is compared to the frequently employed biometric normalization method that is s-norm which is a cohort-based technique and accounts only for imposter calibration. The proposed normalization is not only faster than s-norm, but also it outperforms s-norm by 8% and 3% for male and female speakers, respectively.
Dilated Residual Aggregation Network for Text-Guided Image Manipulation

Text-guided image manipulation aims to modify the visual attributes of images according to textual descriptions. Existing works either mismatch between generated images and textual descriptions or may pollute the text-irrelevant image regions. In this paper, we propose a dilated residual aggregation network (denoted as DRA) for text-guided image manipulation, which exploits a long-distance residual with dilated convolutions (RD) to aggregate the encoded visual content and style features and the textual features of the guiding descriptions. In particular, the dilated convolutions increase the receptive field without sacrificing spatial resolutions of intermediate features, benefiting to reconstructing the texture details matching with the textual descriptions. Furthermore, we propose an attention-guided injection module (AIM) to inject textual semantics into feature maps of DRA without polluting the text-irrelevant image regions by combining triplet attention mechanism and central biasing instance normalization. Quantitative and qualitative experiments conducted on the CUB-200-2011 and Oxford-102 datasets demonstrate the superior performance of the proposed DRA.
Semantic Segmentation of Indoor Scenes Based on RGBD images Feature Fusion

Depth learning has been applied in semantic segmentation and object recognition in computer vision. In this paper, we propose a high-efficiency pixel classification convolutional neural network based on encoder-decoder structure. Using depth image to enhance CNNS network make color image and depth information can be detected jointly. The experimental data shows that through the fusion of depth features representing different scales of information, the algorithm architecture can be jointly optimized and obtain more sophisticated image semantic features. Compared with similar methods, image semantic segmentation algorithm has obvious advantages.
A Study on Intrarenal Porous Segmentation of Endoscopic Images

The three-dimensional porous structure is a common scene in the field of medical surgery. However, the imaging resolution of the endoscope is low, and its scene has weak texture features and high noise, which leads it difficult for traditional algorithms to segment the porous structure. In this study, a new approach for efficient segmentation of intrarenal porous areas in endoscopic images is put forward. The proposed method first classifies the images into deep and shallow groups based on the statistical vectors derived from the color feature histogram. Then for each group, the improved U-Net learning strategy is used to extract the intrarenal porous areas at the pixel level, and its segmentation results could be accurately obtained. The effectiveness and accuracy of this work are evaluated on the data of the ureteroscopic holmium laser lithotripsy.
Study on the Application Fields and Development Prospects of Artificial Intelligence

Since the world entered the Internet age, various industries have developed rapidly, as has the industry of artificial intelligence. Moreover, there is a wide range of application areas in artificial intelligence, including industry, computer, education, and intelligent robots. With the rapid development of science and technology, it is believed that the artificial intelligence may develop towards the "artificial intelligence +" era and strong artificial intelligence, which will be comprehensively analyzed in terms of definition, development, and application areas. Especially the analysis of intellectual property rights is of reference significance to the intellectual property planning of domestic enterprises.
A Monadic and Effective Frame Work for Single Human Pose Estimation of 2D Images and Videos

Human pose estimation (HPE) is most dominant research areas in Computer Vision (CV) field. This technology will have huge implications. The significant applications include human computer interaction, activity recognition, video surveillance, motion recognition, etc. Different types of pose estimation are used for estimating the number of persons who are tracked. In this paper we are focusing on single 2D pose estimation. Present trends of pose estimation uses CNN based architectures for HPE and post statistical methods. In this work we propose a monadic frame work for both HPE and post processing into a single stage. It produces a human pose skeleton for a single two-dimensional image and videos. Here, take different modalities such as static image, static video and live video to generate different skeletons for SHPE. In this work we use pre-trained tensor flow based CNN model for pose estimation and also it produce better results compared to state of art in terms of performance metrics as PCP@.5, PCK@.5, and PCKh@.2.
SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detection

Deep learning based object detectors are commonly deployed on mobile devices to solve a variety of tasks. For maximum accuracy, each detector is usually trained to solve one single specific task, and comes with a completely independent set of parameters. While this guarantees high performance, it is also highly inefficient, as each model has to be separately downloaded and stored. In this paper we address the question: can task-specific detectors be trained and represented as a shared set of weights, plus a very small set of additional weights for each task? The main contributions of this paper are the following: 1) we perform the first systematic study of parameter-efficient transfer learning techniques for object detection problems; 2) we propose a technique to learn a model patch with a size that is dependent on the difficulty of the task to be learned, and validate our approach on 10 different object detection tasks. Our approach achieves similar accuracy as previously proposed approaches, while being significantly more compact.
A straightforward feature selection method based on mean ratio for classifiers

Feature Selection (FS) is currently a very important and prominent research area. The focus of FS is to identify and to remove irrelevant and redundant features from large data sets in order to reduced processing time and to improve the predictive ability of the algorithms. Thus, this work presents a straightforward and efficient FS method based on the mean ratio of the attributes (features) associated with each class. The proposed filtering method, here called MRFS (Mean Ratio Feature Selection), has only equations with low computational cost and with basic mathematical operations such as addition, division, and comparison. Initially, in the MRFS method, the average from the data sets associated with the different outputs is computed for each attribute. Then, the calculation of the ratio between the averages extracted from each attribute is performed. Finally, the attributes are ordered based on the mean ratio, from the smallest to the largest value. The attributes that have the lowest values are more relevant to the classification algorithms. The proposed method is evaluated and compared with three state-of-the-art methods in classification using four classifiers and ten data sets. Computational experiments and their comparisons against other feature selection methods show that MRFS is accurate and that it is a promising alternative in classification tasks.
Optimizing Communication Quality in Distance Education Based on Wibit Algorithm

Distance education is a new type of education with the development of computer technology, multimedia technology, network communication technology and Internet network. However, the problems faced by distance education seriously restrict the improvement of the quality of distance education. This paper proposes a communication optimization strategy based on Wibit algorithm, which can give full play to the advantages of distance teaching and effectively guarantee the quality of teaching. In this paper, it is seen as the implicit causes of voice signal, therefore it can be the voice signal used victor than algorithm to find the most likely a text string. A software implementation scheme (2, 1, 7) and Viterbi decoding is proposed. The Viterbi decoder function of convolution code (2, 1, 7) is realized in Matlab environment. It is proved that the error rate of the Viterbi decoding algorithm can still reach 10–6 under low SNR.
Generative Properties of Universal Bidirectional Activation-Based Learning

UBAL is a novel bidirectional neural network model with bio-inspired learning. It enhances contrastive Hebbian learning rule with an internal echo mechanism enabling self-supervised learning. UBAL approaches any problem as a bidirectional heteroassociation, which gives rise to emergent properties, such as generation of patterns while trained for classification. We briefly discuss and illustrate these properties using the MNIST dataset and conclude that with a slight trade-off in accuracy we can achieve feasible image generation without explicitly setting up the objective to do so.