Reviewerly/exHarmony huggingface dataset:
A novel update rule of HALS algorithm for nonnegative matrix factorization and Zangwill’s global convergence

Abstract Nonnegative Matrix Factorization (NMF) has attracted a great deal of attention as an effective technique for dimensionality reduction of large-scale nonnegative data. Given a nonnegative matrix, NMF aims to obtain two low-rank nonnegative factor matrices by solving a constrained optimization problem. The Hierarchical Alternating Least Squares (HALS) algorithm is a well-known and widely-used iterative method for solving such optimization problems. However, the original update rule used in the HALS algorithm is not well defined. In this paper, we propose a novel well-defined update rule of the HALS algorithm, and prove its global convergence in the sense of Zangwill. Unlike conventional globally-convergent update rules, the proposed one allows variables to take the value of zero and hence can obtain sparse factor matrices. We also present two stopping conditions that guarantee the finite termination of the HALS algorithm. The practical usefulness of the proposed update rule is shown through experiments using real-world datasets.
I/O-Efficient Algorithms for Topological Sort and Related Problems

This article presents I/O-efficient algorithms for topologically sorting a directed acyclic graph and for the more general problem identifying and topologically sorting the strongly connected components of a directed graph G = ( V, E ). Both algorithms are randomized and have I/O-costs O ( sort ( E ) · poly(log V)), with high probability, where sort ( E ) = O( E / B log M / B ( E/B )) is the I/O cost of sorting an | E |-element array on a machine with size- B blocks and size- M cache/internal memory. These are the first algorithms for these problems that do not incur at least one I/O per vertex, and as such these are the first I/O-efficient algorithms for sparse graphs. By applying the technique of time-forward processing, these algorithms also imply I/O-efficient algorithms for most problems on directed acyclic graphs, such as shortest paths, as well as the single-source reachability problem on arbitrary directed graphs.
Unidirectional Identity-Based Proxy Re-Signature with Key Insulation in EHR Sharing System

The introduction of the electronic medical record (EHR) sharing system has made a great contribution to the management and sharing of healthcare data.Considering referral treatment for patients, the original signature needs to be converted into a re-signature that can be verified by the new organization.Proxy re-signature (PRS) can be applied to this scenario so that authenticity and nonrepudiation can still be insured for data.Unfortunately, the existing PRS schemes cannot realize forward and backward security.Therefore, this paper proposes the first PRS scheme that can provide key-insulated property, which can guarantee both the forward and backward security of the key.Although the leakage of the private key occurs at a certain moment, the forward and backward key will not be attacked.Thus, the purpose of key insulation is implemented.What's more, it can update different corresponding private keys in infinite time periods without changing the identity information of the user as the public key.Besides, the unforgeability of our scheme is proved based on the extended Computational Diffie-Hellman assumption in the random oracle model.Finally, the experimental simulation demonstrates that our scheme is feasible and in possession of promising properties.
Poster: Testbed in Wireless City Mesh Network with Application to Federated Learning Experiments

The increase of the computing capacity of IoT devices and the appearance of lightweight machine learning frameworks have led to the situation that machine learning can nowadays be run in IoT applications at the network edge. There is an opportunity to implement machine learning algorithms with the more and more computationally powerful edge nodes and using the ever increasing amount of local data coming from nearby sensors. For this purpose, federated learning becomes a promising machine learning approach, where a machine learning model is trained by various nodes using their local data. For performing practical federated learning experiments, we have built a testbed deployed within a wireless city mesh network with geographically distributed low capacity devices. We describe the testbed implementation and show its potential to experimentally study federated learning protocols and algorithms in real edge environments.
Deep attentive pixels for face super-resolution

Face super-resolution, successfully using fusion network approach, has successfully solved the problem of face image restoration. Recently, face attributes have been effectively used to guide the low-level feature point of the face to perform viable face recovery. First, the low-resolution image is enlarged into a super-resolution face image. Landmarks are estimated to guide the network to enhance the super-resolution image repeatedly. However, the face super-resolution network architecture parameter is redundant, and the learning efficiency is low on mapping input and target output. This paper proposes a deep attention pixel for face super-resolution, which applies an attention mechanism to optimize feature extraction and fuses the channel attention with facial landmarks heatmaps. Experimental results demonstrate that the proposed method achieves higher performance than other state-of-the-art face super-resolution methods.
Interactive drawing interface with 3D animal model retrieval

In this work, we propose an interactive drawing guidance interface with 3D animal model retrieval, which aims to help common users draw 2D animal sketches by exploring the desired animal models from the pre-collected dataset. We first construct an animal model dataset and generate line drawing images of 3D models from different viewpoints. Then, we develop the drawing interface, which illustrates the retrieval models through matching freehand sketch inputs with line drawing images. We utilize the state-of-the-art sketch-based image retrieval algorithm for sketch matching, which describes the appearance and relative positions of multiple objects by measuring compositional similarity. The proposed system can accurately retrieve similar partial images and provide the blended shadow guidance underlying the user’s strokes to guide the drawing process. We verified that the proposed interface could improve the drawing quality of users’ animal sketches from our user study.
Retrieval Algorithm of Water Pollutant Concentration Based on UAV Remote Sensing Technology

With the development of the society and economy, traditional water pollution monitoring methods can no longer meet the normal needs of work. Unmanned aerial vehicle remote sensing technology has gradually emerged, and it has shown a development trend of multimodel and multifunction. However, the application of UAV remote sensing technology in water pollution monitoring is in its infancy and has not formed a unified method and standard. This paper introduces the disadvantages of UAV Remote Sensing Technology in water pollution monitoring and provides a way to improve the application level of UAV Remote Sensing Technology in water pollution monitoring. In order to make full use of the advantages of remote sensing monitoring technology in water ecological environment monitoring, including wide coverage, fast analysis speed, accurate and reliable monitoring results, and large amount of information, according to the spectral effect of polluted water, a remote sensing inversion model of river and lake parameters is established to determine the water quality of rivers and lakes. Concentrations of pollutants in the body are inverted and analyzed. The analysis results show that the fitting effect of the inversion model is accurate and the accuracy errors reflected by the degree of dispersion and deviation are also acceptable. The inversion results of the concentration of river and lake characteristic parameters based on the Gaofen-1 remote sensing image data are in line with the actual situation. The research results can greatly strengthen the monitoring capacity of the sewage outlet and provide reference for related research on water pollution caused by the sewage outlet.
Polynomial fitting for period prediction in sliding-DCT-based filtering

Gaussian filtering (GF) is a fundamental smoothing filter that determines the weights in the kernel according to the Gaussian distribution. GF is an essential tool in image processing and is used in various applications. Therefore, accelerating GF is essential in various situations. The sliding DCT-based GF is one of the fastest methods for approximating GF. The Gaussian kernel is decomposed into multiple cosine kernels using the DCT transform and is approximated by the limited number of kernels. When calculating the period of the DCT for fitting the best length, a linear search method is used; however, the brute-force search has a significant impact on the filtering processing time. In this paper, we accelerate the period estimation by polynomial fitting. Experimental results show that the proposed method has almost the same accuracy as the brute-force approach.
Color laparoscopic high-definition video quality assessment for super-resolution

After considering medical image analysis support, it is one of important factors to assess high-definition image quality for high-definition display. In case of processing recognition and detection of body region and lesion in medical images, it is not enough to judge the only information of still image or some frame images. To find out image content and characteristics more accurately, we need to consider image quality from information obtained by video. In this paper, in the case of changing bit-rate and focusing on image quality for color laparoscopic high-definition video including super-resolution, we carried out the objective assessment experiment by using PSNR, SSIM among frame of image, bit-rate of video. Finally, we discussed with regard to display of medical image.
Quality Assessment Method of Information Model Reform of Higher Mathematics Education Based on Big Data

The quality of higher mathematics teaching in colleges and universities is related to a variety of influencing factors, and the changing laws are very complex, which makes the current model unable to accurately evaluate the quality of higher mathematics teaching in colleges and universities. In order to solve the deficiencies in the current teaching quality evaluation process in colleges and universities and improve the correct rate of college teaching quality evaluation, a quality evaluation method based on big data is proposed, which reforms the information model of college teaching quality evaluation. The literature related to mathematics teaching quality evaluation is researched and analyzed, and the influencing factors of higher mathematics teaching quality evaluation in colleges and universities are established. Then, collect the data of the influencing factors of higher mathematics teaching quality in colleges and universities, and establish a learning sample of college teaching quality evaluation through the star rating of the teaching quality of colleges and universities by experts. Finally, the neural network and decision tree of big data technology are introduced to train the learning samples to form an evaluation model of higher mathematics teaching quality in colleges and universities. The results show that our method can achieve high accuracy.
Causal Interactions in Agile Application Development

The Agile approach and tools are popular for the management of Enterprise Application Software (EAS) development. This article focuses on the issue of inconsistency between strategic business objectives and the functionality of the software developed. Agile management tools lack the functionality of EAS project activities coordination. This article aims to rethink Agile project management using the causal modelling approach. A causal model of Agile project management using a management transaction (MT) concept was developed. The notion of the space of processes was used to identify the MTs location along the axes of aggregation, generalization, and time and to formalize their interaction specifications. Taxonomy of the coordination meta-types and types was developed using the identifiers of the MTs. The modified Agile activities hierarchy was developed, and vertical and horizontal causal interactions between Agile activities were identified. This modified Agile management model helps to consistently track the integrity of EAS project content. Complexity indicators were introduced to evaluate the EAS project complexity and their average and normalized values are presented. Additional attributes in the Agile management tool Jira are proposed. Monitoring mismatch between strategic business objectives and development activities content helps to improve the success of EAS projects delivery.
Tolerance of CNN watermarking against model optimizations

Models created by companies and individuals by training a large amount of data are important assets and need to be protected by copyright. As a method of copyright protection, experiments have been conducted to embed a watermark into the learning model. This watermark is embedded directly into the parameters of the learning model, but the values of the parameters will change when the learning model is subjected to model compression process such as quantization. In our previous study, we showed that the effect of quantization on the watermark was small and that the embedded watermark could be retrieved. In this paper, as a further investigation, we conduct experiments on the effect of both pruning and quantization, and quantization aware training on the watermarking when creating the trained model. In the experiments, we used models of two different scales, one large and one small, and performed the above-mentioned processing on each model to check the state of the watermark. The results show that the models with both pruning and quantization show significant degradation of the watermark for small-scale models, but this is eliminated when the models are quantized. In the case of quantization aware training, there was no effect on watermarking.
Recommendation Method of Online Teaching Resources in Colleges and Universities Considering Different User Preference Factors

In order to improve the recommendation performance of online teaching resources in colleges and universities and the learning efficiency of users, this paper considers user preference factors and studies the recommendation method of online teaching resources in colleges and universities. This paper selects different user preference factors and extracts preference keywords and builds a keyword projection model for different user preference resources based on this. In this paper, Markov is used to construct a keyword probability model, and the TF-IDF algorithm is introduced to refine online teaching resources. According to the calculated closeness of the resources required by the user, this paper realizes the accurate recommendation of resources. The experimental results show that each recommendation performance index of this method has reached a high value. The recommendation accuracy rate of this algorithm is the highest at 82.2%, the recall rate is the highest at 78.7%, the <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> value is the highest at 83.5%, and the average absolute error is lower than 0.56. And the successful recommendation rate of this method is as high as 97.6%.
Active learning for human pose estimation based on temporal pose continuity

In recent years, human pose estimation based on deep learning has been actively studied for various applications. A large amount of training data is required to achieve good performance, but, annotating human poses is quite an expensive task. Therefore, there is a growing need to improve the efficiency of training data preparation. In this paper, we take an active learning approach to reduce the cost of preparing training data for human pose estimation. We propose an active learning method that automatically selects images effective for improving the performance of a human pose estimation model from unlabeled image sequences, focusing on the fact that the human pose continuously changes between adjacent frames in an image sequence. Specifically, by comparing the estimated human poses between frames, we select images incorrectly estimated as candidates for manual annotation. Then, the human pose estimation model is re-trained by adding a small portion of manually annotated data as training data. Through experiments, we confirm that the proposed method can effectively select training data candidates from unlabeled image sequences, and that the proposed method can improve the performance of the model with reducing the cost of manual annotations.
Improvement of Task Allocation in TP Algorithm for MAPD Problem by Relaxation of Movement Limitation and Estimation of Pickup Time

Multi-Agent Path Finding (MAPF) problem is an extended class of pathfinding problems where multiple paths are assigned to multiple agents without collision. MAPF problem can been applied to various practical domains including path-planning of robot carriers in warehouses, automated taxiing of airplanes, and video games. Multi Agent Pickup and Delivery (MAPD) problem that is a continuous MAPF problem has been studied for more practical cases such as automated warehouses in which robot carriers are assigned to tasks that appear over time. A major goal of MAPD problems is to reduce the execution time or the total path length for the tasks. Token Passing (TP) has been proposed as a solution method for MAPD problems. TP is a task allocation method that also employs a pathfinding algorithm such as Cooperative A* (CA*) or Multi-Label A* (MLA*) algorithm. With TP, pickup-and-delivery tasks are sequentially allocated to agents by employing a shared memory called token where the agents reserve their paths of tasks. In this study, we focus on two issues of the TP. Firstly, TP targets well-formed problems which satisfy the sufficient condition for solvable MAPD instances. The well-formed MAPDs also limit the passable nodes on a graph representation of a warehouse and decreases the utilization of warehouse space in pathfinding. However, if this restriction is relaxed, the collision between agents' paths might occur. Secondly, the task allocation is greedily performed depending on a default order in which unallocated agents access the token. However, there might be another agent whose current task is almost completed and its current destination is nearer the pickup location of a new task. Without considering such an agent, it might generate a redundant path by allocating a new task to an inappropriate agent. To address the first problem, we relax the movement limitation and introduce a reservation method for stays at destinations that maintains the consistency of reserved paths of agents. For the second problem, we propose a task allocation method that estimates the pickup time of agents to determine more appropriate agents to be allocated to new tasks. Experimental results show that our proposed algorithm reduces the total path length and the total task execution time by reducing the length of pickup paths in comparison to the existing method.
Geometry reconstruction for spatial scalability in point cloud compression based on neighbour occupancies

Spatial scalability is an important functionality for point cloud compression. The current design of geometry reconstruction for spatial scalability applies the points at the center of nodes, ignoring correlations among neighbour nodes. In this work, a geometry reconstruction method based on neighbour occupancies is proposed, where the distribution of real points in the current node is predicted using the information of neighbour occupancies. In comparison to the state-of-the-art geometry-based point cloud compression, i.e., G-PCC, performance improvement of 1.15dB in D1- PSNR and 3.80dB in D2-PSNR in average, can be observed using proposed method.
Blockchain-Empowered High-Frequency Spectrum Management IoT: A Multilayer PBFT Consensus Perspective

High frequency (HF) is an important method for long-range communications and even the only mean when satellites are destroyed or interfered, which play an essential role in defense and economic construction. However, noncooperative frequency competition accompanied with power competition results in the continuously deterioration of HF electromagnetic environment. This article endeavors to resolve this issue through proposing blockchain-empowered HF spectrum management. Specifically, massive personal HF devices are organized around the preselected nodes to construct HF spectrum management IoT, further monitor, and share HF data through PBFT (Practical Byzantine Fault Tolerance) protocol. To address the scalability problem during the consensus, a multilayer PBFT consensus protocol is employed. Scalability evaluations show that increasing consensus layers of PBFT greatly reduces the communication complexity. Security assessments illustrate that the security performance will decline with the increase of layers. Tradeoff has been made between the communication complexity and security performance, indicating 2-4 layers PBFT is sufficient, which bring down the communication complexity and also achieve acceptable security performance.
Traction force presentation in redirected walking

Walking real space for move in virtual environment is one of methods for improve immersion of Virtual Reality (VR) contents. However, a movable space in virtual environment is limited by real space obstacles such as walls. As a result, an experience of the VR content may be impaired. One solution of this problem is Redirected Walking (RDW). RDW is a method for enabling walk on virtual space that larger than limited real space. The method led to an illusion of walking direction by rotation of virtual scene. Previous research has indicated that haptics can be enhance a level of illusion. In this study, we propose a RDW method combining traction force presentation with virtual scene rotation. We develop a RDW system that presents traction force as the subject walks, and conducted experiments with few subjects. Result of experiment suggests possibility of enhancing an illusion level by traction force presentation.
Template matching using a small number of pixels selected by distinctiveness of quantized hue values

We propose a template matching method using a small number of distinctive pixels selected on the basis of hue values as color information. By analyzing the template image, a distribution of co-occurrence probabilities regarding the hue values of two-pixel pairs is generated. A small number of pixels that have low probability are selected and used for matching. Since such pixels have high distinctiveness, reliable matching can be achieved. The recognition success rate of our method is 97% when 571 pixels are selected, and the processing time is 219 msec.