Reviewerly/exHarmony huggingface dataset:
The Research of Multi-scale Effect on Remote Sensing Image Object Detection

Object detectors based on convolutional neural network (CNN) have already achieved state-of-the-art performance due to the powerful feature representation capabilities in computer vision tasks including image classification, object detection, and image segmentation. With the rapid development of remote sensing technology, the object detection of high-resolution optical remote sensing images has become an important part of remote sensing. On account of the large sight and complex background of the remote sensing images, along with the large size variation of the objects, the performance of the methods based on CNN is restricted. This paper will focus on the objects that multi-scale features are of the most obvious to analyze and discuss the impact of multi-scale effect on object detection. A new criterion for setting small objects is presented. Through the analysis of evaluation indicators, we conclude that the multi-scale effect decreases the detection accuracy of small objects under the same condition. Furthermore, a method is proposed to improve the accuracy of small objects detection.
SphereCF: Sphere Embedding for Collaborative Filtering

Recently, metric learning has shown its advantage in Collaborative Filtering (CF). Most works capture data relationships based on the measure of Euclidean distance. However, in the high-dimensional space of the data embedding, the directionality between data, that is, the angular relationship, also reflects the important relevance among data. In this paper, we propose Sphere Embedding for Collaborative Filtering (SphereCF) which learns the relationship between cosine metric learning and collaborative filtering. SphereCF maps the user and item latent vectors into the hypersphere manifold and predicts by learning the cosine similarity between the user and item latent vector. At the same time, we propose a hybrid loss that combines triplet loss and logistic loss. The triplet loss makes the inter-class angle between the positive and negative samples of a user as large as possible, and the logistic loss makes the intra-class angle of the positive user-item pairs as small as possible. We consider the user and item latent vector as a point on the hypersphere, which makes the margin in the triplet loss only depend on the angle, thus improving the performance of the model. Extensive experiments show that our model significantly outperforms state-of-the-art methods on four real-world datasets.
Novel View Synthesis of Dynamic Human with Sparse Cameras

This paper proposes a new method to synthesize a novel view of a human in motion. For image-based rendering, the challenging problem is to synthesize an image of a novel view with sparse images. As the number of cameras decreases, there will exist missing regions in the synthetic image. To address this challenge, we use a skinned multi-person linear model (SMPL) model to represent the surface and posture of the human body in motion and correlate the images of the human in different poses. If the missing pixel at the novel view is visible at other times, we can use spatio-temporal sequence information to complete it. Therefore, we choose images from different frames to synthesize images of the novel view. Then, we use deformable convolutional network to align these images and take advantage of ConvLSTM to perform temporal aggregation. Finally, we can obtain a more realistic free-view image of the human. This method allows us to freely move the camera view in time and space to synthesize free-view video.
Domain Adaptation for Medical Semantic Textual Similarity

Semantic textual similarity is a common task to determine whether two sentences in a pair own the same meaning. In the medical domain, the annotated data is limited and sparse, which brings great difficulty to obtain accurate semantic information from it. In this paper, we propose a two-stream model to adapt knowledge learned from other domains to the medical domain. To optimize and reduce the computation, we further compress the proposed model by knowledge distillation. Experimental results show that our proposed method achieves better performance than the baseline methods.
2D Wavelet Tree Ordering Based Localized Total Variation Model for Efficient Image Restoration

The degraded image during the process of image analysis needs more number of iterations to restore it. These iterations take long waiting time and slow scanning, resulting in inefficient image restoration. A few numbers of measurements are enough to recuperate an image with good condition. Due to tree sparsity, a 2D wavelet tree reduces the number of coefficients and iterations to restore the degraded image. All the wavelet coefficients are extracted with overlaps as low and high sub-band space and ordered them such that they are decomposed in the tree ordering structured path. Some articles have addressed the problems with tree sparsity and total variation (TV), but few authors endorsed the benefits of tree sparsity. In this paper, a spatial variation regularization algorithm based on tree order is implemented to change the window size and variation estimators to reduce the loss of image information and to solve the problem of image smoothing operation. The acceptance rate of the tree-structured path relies on local variation estimators to regularize the performance parameters and update them to restore the image. For this, the Localized Total Variation (LTV) method is proposed and implemented on a 2D wavelet tree ordering structured path based on the proposed image smooth adjustment scheme. In the end, a reliable reordering algorithm proposed to reorder the set of pixels and to increase the reliability of the restored image. Simulation results clearly show that the proposed method improved the performance compared to existing methods of image restoration.
ARPIC: Personal Information Recognition of ID Card with Interference of Watermark

Although Optical character recognition (OCR) in printed document texts is well developed, it’s still challenging to recognize the personal information in copy of ID card (CIC) under the interference of visible watermark. To address this issue, we present a framework named ARPIC for automatically recognizing the personal information in CIC. The proposed framework consists of four parts: ID card region detection module (IRDM) to locate and rectify the ID card region, watermark removing module (WRM) to remove the visible watermark overlaying on the ID card region, key texts locating and recognition module (KLRM) to recognize the key texts and text correction module (TCM) to correct the recognition results. Combining data-driven sub-models WRM and KLRM with rule-based sub-models IRDM and TCM, the ARPIC is robust, effective and flexible. Besides, A synthetic dataset has been used for training WRM and KLRM, and for evaluating the performance of ARPIC.
Test Case Reuse based on ESIM Model

Software testing is crucial to guarantee the reliability of software, and test case plays an important role in software testing. Test cases are mainly written by testers, which not only consumes a lot of time, but the quality of test cases depends heavily on the designer’s technical capability. Test case reuse technology can reuse historically executed test cases, which are written by experts and stored in the knowledge base. Different from the test case reuse based on modeling and other methods, we propose a test case reuse technology based on the similarity of requirements documents, and use the model ESIM (Enhanced Sequential Inference Model), which performs well in the field of text matching. when employing this method, testers do not have to possess rich experiences and high-level expertise, in this way, the issue of the stability of design quality caused by difference of designer’s technical capability is solved.
Application of Adaptable College English Innovation Teaching in the Network Environment

The rapid development of the Internet has enabled the world to realize the co-building and sharing of resources. People’s thinking has transferred accordingly, and online big data has caused a huge impact on the current English teaching in colleges and universities. Meanwhile, it has also brought certain opportunities to English teaching. Therefore, college English teachers should adapt to the evolution of the ages, update teaching concepts, improve teaching methods, and explore actively a path which is suitable for college English teaching in the environment of the network. The adaptive learning system breaks through the traditional teaching mode essentially, which can automatically present suitable learning content according to the individual characteristics of each learner. Based on the above two points, this paper attempts to integrate the network environment and adaptive learning, and elaborates the effective ways of English teaching in colleges and universities in the Internet environment and adaptive learning system.
Pose-aware Outfit Transfer between Unpaired in-the-wild Fashion Images

Virtual try-on systems became popular for visualizing outfits, due to the importance of individual fashion in many communities. The objective of such a system is to transfer a piece of clothing to another person while preserving its detail and characteristics. To generate a realistic in-the-wild image, it needs visual optimization of the clothing, background and target person, making this task still very challenging. In this paper, we develop a method that generates realistic try-on images with unpaired images from in-the-wild datasets. Our proposed method starts with generating a mock-up paired image using geometric transfer. Then, the target’s pose information is adjusted using a modified pose-attention module. We combine a reconstruction and a content loss to preserve the detail and style of the transferred clothing, background and the target person. We evaluate the approach on the Fashionpedia dataset and can show a promising performance over a baseline approach.
An Anisotropic Local Method for Boundary Detection in Images

Boundary detection is a powerful tool for quantitative image analysis that allows researchers to extract crucial information about a scene. Many methods rely on sharp changes in luminance, chromaticity, or texture in order to correctly predict edge locations between regions, and images without these features have proven difficult to partition. We present a supervised statistical boundary detection method based on image segmentation that incorporates spatial information to locate boundaries in images with low contrast between classes, heteroskedasticity, and objects whose intensity values vary spatially. Similar to its predecessor, the algorithm incorporates user-selected training data to place each pixel into the most probable class based on the statistics of local training data. The major contribution of this work is in the algorithm using a single insensitive parameter to restrict the statistics to be calculated on anisotropic (elliptical) local training data since pixels that lie along a boundary are assumed to be most informative of the boundary location. The algorithm’s success is demonstrated on images from the Berkeley Segmentation Dataset and on X-ray images of olivine sand under dynamic compaction loading. Additionally, because of the statistical nature of the algorithm, maps showing the uncertainty in the boundary location are provided.
Monitoring of Development Of Information And Management Competencies Of Masters Of Technical Higher Educational Institutions

In the article presents the results of an ascertaining experiment on problems in the field of development of information and management competencies of masters of Technical higher educational institutions. The author analyzes the effectiveness of the process of development of information and management competencies, identifies factors that contribute to improving the development of masters of of Technical higher educational institutions for future teaching activities. In the article the diagnostics of revealing readiness of development of information and management competencies on a measuring instrument (indicators) is given. The quality of education was monitored, on the basis of which indicators for the following evaluation objects were developed and tested: educational activities; extracurricular activities; the quality of the conditions for ensuring the educational process. Their use in the process of experimental and experimental work is also considered. The results of the determination of readiness of development of information and management competencies on combined levels of assessment are summarized.
Remote Sensing Image Registration based on a Geometrical Model Matching

Remote sensing image registration is the method of aligning two images from the same scene taken under different imaging circumstances containing different times, angles, or sensors. Scale-invariant feature transform (SIFT) is one of the most common matching methods previously used in the remote sensing image registration. The defects of SIFT are the large number of mismatches and high execution time due to the high dimensions of classical SIFT descriptor. These drawbacks reduce the efficiency of the SIFT algorithm. To enhance the performance of the remote sensing image registration, this paper proposes an approach consisting of three different steps. At first, the keypoints of both reference and second images are extracted using SIFT algorithm. Then, to increase the speed of the algorithm and accuracy of the matching, the SIFT descriptor with the vector length of 64 is used for keypoints description. Finally, a new method has been proposed for the image matching. The proposed matching method is based on calculating the distances of keypoints and their transformed points. Simulation results of applying the proposed method to some standard databases demonstrated the superiority of this approach compared with some other existing methods, according to the root mean square error (RMSE), precision and running time criteria.
Project management maturity in the biotechnology industry

This paper contributes to the development of project management in companies that integrate biotechnology in their processes, given the increasing importance of this sector under the current context. The main goal is to assess project management maturity in a sample of companies that involve biotechnology processes, identifying their main weaknesses and strengths in project management. A quantitative approach was applied by analyzing the data collected through a questionnaire structured with the K-PMMM Level 2. This work allowed to diagnose 96 companies of this emerging sector, still scarcely studied as concerns project management. All participating companies recognized the importance of project management, however there are still several steps to consolidate the evaluated project management practices, once only about 12,5% reached the whole five life cycle phases of project management maturity. The originality of this work relies on the structure of the data analysis that allowed to highlight the multidimensional and simultaneous perspective of the project management maturity process. That is, a process that recognizing the need and added value of project management, implementing methodologies and tools, and having the executive and line management support. These factors must be taken care of in parallel to evolve the organization´s capacities to manage its projects consistently and long term. The study revealed that part of the participating companies meets this simultaneous multidimensional approach, although still very incompletely.
Probability and standpoint based argumentation framework

The abstract argumentation framework has become a core research in knowledge representation and artificial intelligence. Many extend argumentation frameworks proposed, while they have not referred to standpoint. In this paper, we propose the probability and standpoint based argumentation framework. The concept of standpoint is defined to represent the viewpoint of argument. We provide the semantics and properties of the probability and standpoint based argumentation.
Development of CCTV-camera-based System for Detection of Anomalous Behaviors in Penitentiary Institutions

This paper presents the interdisciplinary project aimed at detecting the undesired or dangerous behavior of persons in the confined institutions such as prisons or wards, based on the video streams provided by the CCTV cameras. Currently, there are IT systems working in such areas, but their efficiency is limited, forcing the operator to focus his/her attention on every image separately. Design of a system for autonomous detection of anomalous behaviors - such as fights or passing illegal material - based on the input from multiple surveillance cameras would allow for providing the decision support module. With the acceptable detection accuracy, the solution would help in minimizing the accidents and unwanted behavior of monitored inmates. The system should also provide the catalogue of predefined dangerous situations, prepared by the experts in criminal sciences, including the features useful for predicting the specific events.
A Deblurring Method of Electric Power Inspection Images Based on GAN

With large-scale application of drones in Electric power line inspection, the electrical images captured by their cameras can be used for target recognition and defect detection. However, due to the relative movement of the camera and the shooting target and the high-frequency vibration of the helicopter and UAV platform, Electric power images will be degraded. This is not conducive to the training of the model and subsequent recognition and detection. In this paper, the deblurring of electrical power images are achieved by generative adversarial networks. The article first investigates the current research status of image deblurring, and then studies the application of generative adversarial networks in power imagery, and finally trains the model on the electrical power image training set, and experimentally verifies that the method is blurred in the power inspection scene Effectiveness.
Enhanced Bidirectional Propagation Network for Video Super-Resolution

Video super-resolution (VSR) aims to restore a high-resolution (HR) frame from a given corresponding low-resolution (LR) frame. For most VSR models, the pipeline mainly includes feature extraction module, alignment module, fusion module and reconstruction module, among them, the alignment module and the fusion module are the most important. Based on BasicVSR, we propose an enhanced bidirectional propagation network which is both effective and efficient in exploiting previous and next frames to super-resolve the current frame. In the alignment module, in order to obtain a precisely offsets for alignment, we combine optical flow with deformable convolution (DCN). In the fusion module, we propose a channel and double attention module (CDAM) to extract the important features and suppress the unimportant features in channel dimension in addition, it can capture the long-range dependencies too. The experiments demonstrate the proposed method can effectively achieve superior performance compared with state-of-the-art methods.
Geometric Aspects of the Functional-Voxel Implementation of the ORCA Algorithm

The problem of avoiding a collision between moving agents constantly arises in multi-agent systems with decentralized control. The various algorithms for solving this problem are accompanied by computational complexity and increasing computational power requirements as the number of agents in question increases. There are difficulties in adapting these algorithms to practical applications on mobile platforms. It is necessary to develop simpler computational schemes and to apply appropriate models. The most computationally expensive step in the classical collision avoidance algorithm ORCA is to calculate the mutual half-planes of possible collision for each pair of robots and use linear programming to calculate the new velocity from them. The application of the functional-voxel method will simplify the necessary calculations by storing in graphical images the local geometric characteristics of the searched domain. Moreover, the application of such models will make it possible to perform most of the necessary calculations in advance, which will accelerate the work of the algorithm. This paper proposes the construction of a functional-voxel model of a required geometric domain by interpolating the contour of the domain using Bezier curves. The local geometric modelling by means of local zeroing function is used as a tool for functional-voxel curve modelling. The obtained functional-voxel model represents a static case of possible mutual positioning of two agents. A four-dimensional graphical model is proposed to solve the dynamic case. This model performs the distribution of the static case modelling results in the space-time characteristics.
Action Recognition Based on CSI Signal Using Improved Deep Residual Network Model

In this paper, we propose an improved deep residual network model to recognize human actions.Action data is composed of channel state information signals, which are continuous fine-grained signals.We replaced the traditional identity connection with the shrinking threshold module.The module automatically adjusts the threshold of the action data signal, and filters out signals that are not related to the principal components.We use the attention mechanism to improve the memory of the network model to the action signal, so as to better recognize the action.To verify the validity of the experiment more accurately, we collected action data in two different environments.The experimental results show that the improved network model is much better than the traditional network in recognition.The accuracy of recognition in complex places can reach 92.85%, among which the recognition rate of raising hands is up to 96%.We combine the improved residual deep network model with channel state information action data, and prove the effectiveness of our model for classification through experimental data.
Collaborative determination of flood shelter and evacuation route in a flooded area in Rumbai Subdistrict, Pekanbaru

Floods in Pekanbaru City have often hit the region along the Siak river, including the Rumbai subdistrict. Disasters such as floods have detrimental impacts on society, including a massive loss of lives. However, several strategies can minimize the impacts of flooding, including making a plan evacuation route mapping with Geographic Information Systems (GIS). This is a planning-based analysis of data using the algorithm djikstra for result pathways for efficient and effective evacuation. The evacuation routes involve seven simulation parameter modeling, specifically flood, length path, wide roads, road conditions, road materials, presence or absence of bridges, and the road’s direction. These parameters are processed using algoritma djikstra to generate the appropriate evacuation routes based on study area conditions. The analysis focuses on one evacuation route in Palas and the other six in Sri Meranti Village. The routes in Palas Village lead to the evacuation place of the Al-Jihad Mosque, while those in Sri Meranti Village heads to Al-Ikhlas Mosque, MDA Aula Rumbai, Nurul Haq Mosque, M Nurul Mosque, vacant land, and Stadium Parking Area.