Reviewerly/exHarmony huggingface dataset:
AGPN: Action Granularity Pyramid Network for Video Action Recognition

Video action recognition is a fundamental task for video understanding. Action recognition in complex spatio-temporal contexts generally requires fusing of different multi-granularity action information. However, existing works do not consider spatio-temporal information modeling and fusion from the perspective of action granularity. To address this problem, this paper proposes an Action Granularity Pyramid Network (AGPN) for action recognition, which can be flexibly integrated into 2D backbone networks. The core module is the Action Granularity Pyramid Module (AGPM), a hierarchical pyramid structure with residual connections, which is established to fuse multi-granularity action spatio-temporal information. From top to bottom level in the designed pyramid structure, the receptive field decreases and action granularity becomes more refined. To enrich temporal information of the inputs, a Multiple Frame Rate Module (MFM) is proposed to mix different frame rates at a fine-grained pixel-wise level. Moreover, a Spatio-temporal Anchor Module (SAM) is employed to fix spatio-temporal feature anchors to promote the effectiveness of feature extraction. We conduct extensive experiments on three large-scale action recognition datasets, Something-Something V1 & V2 and Kinetics-400. The results demonstrate that our proposed AGPN outperforms the state-of-the-art methods for the tasks of video action recognition.
A Graph Positional Attention Network for Session-Based Recommendation

The main idea of a session-based recommendation system is to model the user’s historical click sequence and then summarize user preferences and predict the items the user will interact with. The session recommendation model based on graph neural networks has attracted much attention in recent years because it can accurately obtain the local relationship between items. However, the traditional session recommendation model based on graph neural Networks lack the use of user’s higher-order features or fail to address the impact of item position information on the current session, which are both critical to the recommendation system. In addition, some models proposed the position information while neglects the click frequency information. We propose a graph network recommendation model called GPAN based on position attention in response to the abovementioned problems. Specifically, we propose a novel high-low order session perceptron that uses the perceptron to model undirected and directed graphs separately to obtain high and low order item representations in a session. For position information, we designed a position layer to calculate independently. Finally, the user’s short-term preference and long-term preference are aggregated to obtain the recommendation sequence. The results through a large number of experiments on three real datasets show that the performance of the proposed GPAN model is the best.
A Domain Adaptation Method for Land Use Classification Based on Improved HR-Net

In recent years, the recognition accuracy of a semantic segmentation model on natural images can yield a very high level. Thus, it is of great significance to utilize semantic segmentation algorithm to obtain land use classification with remote sensing images. However, due to the large differences between natural images and remote sensing images, the standard semantic segmentation algorithm is not effective for land use classification of remote sensing images. In this article, the structure of high-resolution network (HR-Net) algorithm is improved according to the difference between the two kinds of images to make it more suitable for remote sensing images. Furthermore, in order to overcome the dependence of the semantic segmentation algorithm on a large number of high-quality prior data sets, some research experiments are conducted with the improved HR-Net domain adaptation model, and both of the adversarial domain adaptation model and the fusion domain adaptation model based on improved HR-Net and CycleGAN are designed to reduce the workload of manually labeling data. The extensive experimental results show that the classification of our improved HR-Net algorithm and the two domain adaptation models outperform other algorithms that demonstrates the effectiveness and superiority of our algorithms.
A Novel 3D Topography Stitching Algorithm Based on Reflectance and Multimap

Surface topography is an efficient tool for the understanding of physical phenomena, especially if multiscale roughness analysis is performed. However, the observable scale range in a topography measured with 3D optical profilometers is quite limited. Therefore, all scales linked to a physical phenomenon might not be measured, which impedes the correct analysis of the surface. Stitching of 3D topographies, a technique combining elementary topographic maps into a larger one, can be used to increase the scale range for an objective lens. A high resolution over a large field of measurement topography is then generated. A literature review of 3D topography stitching algorithm highlights the stitching procedure, and detailed explanations on in-plane registration algorithms are provided. However, some existing 3D topography stitching algorithms are not sufficiently accurate for the registration of surface, especially at smaller scales. This paper proposes a new reflectance-based multimap 3D stitching algorithm and three of its variants. These algorithm variants are compared to three existing 3D stitching algorithms (geometric, cross-correlation and global optimization of differences) on four test cases, containing measured elementary topographic maps obtained on four surfaces and with four 3D optical profilometers (two focus variation microscopes and two interferometers). Five qualitative and quantitative criteria and indicators are proposed for the comparison of 3D topography stitching algorithms: visual inspection, run time, memory usage, mean repositioning error and stitching error estimator. Lastly, two quantitative indicators and criteria are new indicators proposed in this article. Overall, the new 3D stitching algorithms based on reflectance and multimaps have a lower mean repositioning error and stitching error estimator compared to other existing algorithms. This highlights the relevance of multimap stitching algorithms in the case of 3D topographies. A new decision-helping tool, the stitching gain lift plot (SGL plot), is described for the selection of the best stitching algorithm for a given test case. The SGL plot especially highlights the higher performance of two of the variants of the novel algorithm compared to the three existing 3D stitching algorithms.
Identifying Synthetic Faces through GAN Inversion and Biometric Traits Analysis

In the field of image forensics, notable attention has been recently paid toward the detection of synthetic contents created through Generative Adversarial Networks (GANs), especially face images. This work explores a classification methodology inspired by the inner architecture of typical GANs, where vectors in a low-dimensional latent space are transformed by the generator into meaningful high-dimensional images. In particular, the proposed detector exploits the inversion of the GAN synthesis process: given a face image under investigation, we identify the point in the GAN latent space which more closely reconstructs it; we project the vector back into the image space, and we compare the resulting image with the actual one. Through experimental tests on widely known datasets (including FFHQ, CelebA, LFW, and Caltech), we demonstrate that real faces can be accurately discriminated from GAN-generated ones by properly capturing the facial traits through different feature representations. In particular, features based on facial landmarks fed to a Support Vector Machine consistently yield a global accuracy of above 88% for each dataset. Furthermore, we experimentally prove that the proposed detector is robust concerning routinely applied post-processing operations.
Static and Dynamic Accuracy and Occlusion Robustness of SteamVR Tracking 2.0 in Multi-Base Station Setups

The tracking of objects and person position, orientation, and movement is relevant for various medical use cases, e.g., practical training of medical staff or patient rehabilitation. However, these demand high tracking accuracy and occlusion robustness. Expensive professional tracking systems fulfill these demands, however, cost-efficient and potentially adequate alternatives can be found in the gaming industry, e.g., SteamVR Tracking. This work presents an evaluation of SteamVR Tracking in its latest version 2.0 in two experimental setups, involving two and four base stations. Tracking accuracy, both static and dynamic, and occlusion robustness are investigated using a VIVE Tracker (3.0). A dynamic analysis further compares three different velocities. An error evaluation is performed using a Universal Robots UR10 robotic arm as ground-truth system under nonlaboratory conditions. Results are presented using the Root Mean Square Error. For static experiments, tracking errors in the submillimeter and subdegree range are achieved by both setups. Dynamic experiments achieved errors in the submillimeter range as well, yet tracking accuracy suffers from increasing velocity. Four base stations enable generally higher accuracy and robustness, especially in the dynamic experiments. Both setups enable adequate accuracy for diverse medical use cases. However, use cases demanding very high accuracy should primarily rely on SteamVR Tracking 2.0 with four base stations.
Neural Network Optimization Based on Complex Network Theory: A Survey

Complex network science is an interdisciplinary field of study based on graph theory, statistical mechanics, and data science. With the powerful tools now available in complex network theory for the study of network topology, it is obvious that complex network topology models can be applied to enhance artificial neural network models. In this paper, we provide an overview of the most important works published within the past 10 years on the topic of complex network theory-based optimization methods. This review of the most up-to-date optimized neural network systems reveals that the fusion of complex and neural networks improves both accuracy and robustness. By setting out our review findings here, we seek to promote a better understanding of basic concepts and offer a deeper insight into the various research efforts that have led to the use of complex network theory in the optimized neural networks of today.
Single-Image Dehazing Based on Improved Bright Channel Prior and Dark Channel Prior

Single-image dehazing plays a significant preprocessing role in machine vision tasks. As the dark-channel-prior method will fail in the sky region of the image, resulting in inaccurately estimated parameters, and given the failure of many methods to address a large band of haze, we propose a simple yet effective method for single-image dehazing based on an improved bright prior and dark channel prior. First, we use the Otsu method by particle swarm optimization to divide the hazy image into sky regions and non-sky regions. Then, we use the improved bright channel prior and dark channel prior to estimate the parameters in the physical model. Second, we propose a weighted fusion function to efficiently fuse the parameters estimated by two priors. Finally, the clear image is restored through the physical model. Experiments illustrate that our method can solve the problem of the invalidation of the dark channel prior in the sky region well and achieve high-quality image restoration, especially for images with limited haze.
Satellite Image Compression Guided by Regions of Interest

Small satellites empower different applications for an affordable price. By dealing with a limited capacity for using instruments with high power consumption or high data-rate requirements, small satellite missions usually focus on specific monitoring and observation tasks. Considering that multispectral and hyperspectral sensors generate a significant amount of data subjected to communication channel impairments, bandwidth constraint is an important challenge in data transmission. That issue is addressed mainly by source and channel coding techniques aiming at an effective transmission. This paper targets a significant further bandwidth reduction by proposing an on-the-fly analysis on the satellite to decide which information is effectively useful before coding and transmitting. The images are tiled and classified using a set of detection algorithms after defining the least relevant content for general remote sensing applications. The methodology makes use of the red-band, green-band, blue-band, and near-infrared-band measurements to perform the classification of the content by managing a cloud detection algorithm, a change detection algorithm, and a vessel detection algorithm. Experiments for a set of typical scenarios of summer and winter days in Stockholm, Sweden, were conducted, and the results show that non-important content can be identified and discarded without compromising the predefined useful information for water and dry-land regions. For the evaluated images, only 22.3% of the information would need to be transmitted to the ground station to ensure the acquisition of all the important content, which illustrates the merits of the proposed method. Furthermore, the embedded platform's constraints regarding processing time were analyzed by running the detection algorithms on Unibap's iX10-100 space cloud platform.
Implementation e-concierge in hotel

Hotels are the places that people choose to carry out activities such as recreation, business, and others. Guests expect to get good service, get comfort while staying at the hotel. During this pandemic, the hotel has started to enforce strict health protocols by reducing the number of employees working, by reducing the number of workers expected to minimize transmission in the hotel environment. With the reduction in hotel employees, it creates a shortage of labor in serving hotel guests who arrive. For this purpose, we created a chatbot, called as e-concierge, that can provide information about the hotel to the guests. The chatbot is also expected to help push the hotel industry forward by combining technology. The chatbot is developed using the AI-based Dialoglfow application that can answer questions asked by staying guests. Dialogflow uses json to be able to reply to messages with the design we want and uses node.js. Based on our evaluation, the chatbot can employ the purpose as e-concierge which can replace customer service in the hotel.
General pattern recognition using machine learning in the cloud

Machine learning (ML) and cloud computing are two subjects that mix very well. The existence of cloud computing enables data scientists to create their machine learning models with the benefits of cloud computing which are very low cost, high performance, and high availability. This opens a new opportunity of allowing students and other people to utilize powerful machines for daily use with the help of appropriate cloud service. On the other hand, machine learning is usually perfect for recognizing patterns in our daily life. This article discusses the possibility and the benefit of creating a user-friendly general-purpose pattern recognition that can be played around by anyone using software as a service.
On the benefits of machine learning classification in cashback fraud detection

Technology development has been getting more advanced and greatly facilitated human life. One of them is machine learning automation which has been proven to be consistent for doing various computations against extensive data such as transaction data in the e-commerce area. Seeing this opportunity, we implemented the machine learning approach to detect fraudulent cashback transactions in e-commerce that are currently rife in Indonesia. The training data used to build the machine learning model were the transaction data from one of the leading e-commerce in Indonesia that had been processed. The supervised classification algorithms used were K-Nearest Neighbor (k-NN), Convolutional Neural Networks (CNN), and Long Short-Term Memory (LSTM). In the end, the best steps and methods that could be taken against fraudulent cashback activities in the future are shown in this paper.
Automatic essay exam scoring system: a systematic literature review

Currently, Indonesia and the whole world are being hit by the Covid-19 pandemic which has an impact on various fields of life. It affects all sectors, including the education sector. The government through the Ministry of Education and Culture makes a policy in education in terms of the learning process. Teaching and learning activities that were initially carried out face to face become distance learning which was carried out at home. In this study, a systematic literature review is conducted on automatic assessment of essay answers. Various previous studies discuss the essay answer scoring system that has been developed using various methods. We synthesize the results to enrich our understanding of the automated essay exam scoring system. The expected result of this research is that it can contribute to further research related to the automated essay exam scoring system, especially in terms of considering methods and dataset forms.
Visible–Infrared Dual-Sensor Fusion for Single-Object Tracking

Visual object tracking is a classical yet rapidly evolving task in computer vision. Traditional RGB trackers in the visible spectrum suffer from poor lighting conditions and bad weather, which significantly affect image quality. To tackle this drawback, RGB trackers are extended from single modality (RGB) to dual modalities [RGB-thermal (RGB-T)] via visible–infrared dual-sensor fusion. Compared with visible cameras, thermal infrared cameras are insensitive to lightning conditions and have a strong ability to penetrate haze and smog. Inspired by the complementary merits of visible and thermal information, we propose a visible–infrared (RGB-T) tracker that fuses the visible modality and infrared modality on the pixel level, feature level, and decision level. First, for the pixel-level fusion, a fused histogram is derived from a four-channel RGB-T image that concatenates an aligned RGB and thermal image pair. With the fused histogram, a foreground mask is derived for guiding correlation filter learning by segmenting the foreground target from the neighbor background. Second, for the feature-level fusion, handcrafted features, such as histogram of gradient (HOG) and color name (CN), are extracted from the RGB and thermal images, respectively, and then concatenated along the channel dimension for feature fusion. Third, for the decision-level fusion, the channel weight of each feature channel in both modalities is estimated for weighting per-channel filter response in localization. To demonstrate the effectiveness of our visible and infrared fusion strategy, we perform an ablation study to evaluate each level of fusion independently on the RGBT234 dataset. We also perform extensive experiments on the RGBT234 and VTUAV_ST datasets to evaluate the performance of the proposed RGB-T tracker compared with state-of-the-art trackers. It is worth noting that our three-level visible–infrared fusion framework is generic and flexible and our implementation is far from optimal. This framework is open-source and easy to be combined with any correlation filter-based RGB-T tracker for further performance gain.
SIGMA++: Improved Semantic-complete Graph Matching for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) generalizes the object detector from an annotated domain to a label-free novel one. Recent works estimate prototypes (class centers) and minimize the corresponding distances to adapt the cross-domain class conditional distribution. However, this prototype-based paradigm 1) fails to capture the class variance with agnostic structural dependencies, and 2) ignores the domain-mismatched classes with a sub-optimal adaptation. To address these two challenges, we propose an improved SemantIc-complete Graph MAtching framework, dubbed SIGMA++, for DAOD, completing mismatched semantics and reformulating adaptation with hypergraph matching. Specifically, we propose a Hypergraphical Semantic Completion (HSC) module to generate hallucination graph nodes in mismatched classes. HSC builds a cross-image hypergraph to model class conditional distribution with high-order dependencies and learns a graph-guided memory bank to generate missing semantics. After representing the source and target batch with hypergraphs, we reformulate domain adaptation with a hypergraph matching problem, i.e., discovering well-matched nodes with homogeneous semantics to reduce the domain gap, which is solved with a Bipartite Hypergraph Matching (BHM) module. Graph nodes are used to estimate semantic-aware affinity, while edges serve as high-order structural constraints in a structure-aware matching loss, achieving fine-grained adaptation with hypergraph matching. The applicability of various object detectors verifies the generalization of SIGMA++, and extensive experiments on nine benchmarks show its state-of-the-art performance on both AP 50 and adaptation gains.
Clustering-based Active Learning Classification towards Data Stream

Many practical applications, such as social media and monitoring system, will constantly generate streaming data, which has problems of instability, lack of labels and multiclass imbalance. In order to solve these problems, a cluster-based active learning method is proposed to achieve data stream classification. Firstly, a label query strategy combining marginal threshold matrix is proposed, which selects difficult to classify or potential concept drift samples for marking, to solve the problem of high cost label and unbalanced data. Secondly, dynamic maintenance of a group of micro clusters, by adjusting the weight of micro clusters in the model, correctly reflects the current data distribution, and finally, uses the buffer to store new micro clusters to participate in the update of the model, to adapt to the new data environment. Experimental results on three real data sets and three synthetic data sets show that compared with the classical data stream classification algorithm, it is less affected by concept drift and has higher classification accuracy than the online semi-supervised learning algorithm ADSM. The average accuracy of the six datasets increased by 5.56%, 2.32%, 1.77%, 1.83%, 3.78%, and 2.04%, respectively. The model processes data streams online and improves classification performance with less memory consumption.
A Comparative Study of Rank Aggregation Methods in Recommendation Systems

The aim of a recommender system is to suggest to the user certain products or services that most likely will interest them. Within the context of personalized recommender systems, a number of algorithms have been suggested to generate a ranking of items tailored to individual user preferences. However, these algorithms do not generate identical recommendations, and for this reason it has been suggested in the literature that the results of these algorithms can be combined using aggregation techniques, hoping that this will translate into an improvement in the quality of the final recommendation. In order to see which of these techniques increase the quality of recommendations to the greatest extent, the authors of this publication conducted experiments in which they considered five recommendation algorithms and 20 aggregation methods. The research was carried out on the popular and publicly available MovieLens 100k and MovieLens 1M datasets, and the results were confirmed by statistical tests.