Reviewerly/exHarmony huggingface dataset:
Image Enlargement Technique Based On The Combination of SVD and Hermite Interpolation

The present paper proposes a simple method built on Hermite interpolation and Singular value decomposition (SVD) to enlarge images with the factor of less computational complexity. This approach also has the advantage that it is shifting the dimensions of the image from low to high while keeping the same content. SVD is applied to the three components of a color image to extract the information. Hermite interpolation has been then applied to the matrices obtained using SVD whose increment value is gained by optimizing the signal-to-noise ratio (SNR). The new information is attained using this increment value which has been utilized to resize the image. The proposed technique has been compared with the bilinear and bicubic interpolation by using entropy, spatial frequency, and standard deviations parameters to show its proficiency and applicatory.
Gaussian Process Regression with Grid Spectral Mixture Kernel: Distributed Learning for Multidimensional Data

Kernel design for Gaussian processes (GPs) along with the associated hyper-parameter optimization is a challenging problem. In this paper, we propose a novel grid spectral mixture (GSM) kernel design for GPs that can automatically fit multidimensional data with affordable model complexity and superior modeling capability. To alleviate the computational complexity due to the curse of dimensionality, we leverage a multicore computing environment to optimize the kernel hyper-parameters in a distributed manner. We further propose a doubly distributed learning algorithm based on the alternating direction method of multipliers (ADMM) which enables multiple agents to learn the kernel hyper-parameters collaboratively. The doubly distributed learning algorithm is shown to be effective in reducing the overall computational complexity while preserving data privacy during the learning process. Experiments on various one-dimensional and multidimensional data sets demonstrate that the proposed kernel design yields superior training and prediction performance compared to its competitors.
Blockchain-based decentralized security using Crypto-Proof of Stake for securing sensitive personal health care records

The blockchain is tremendous technology for securing data transmission over a decentralized cloud environment. A PHR can offer easy data exchange and storage. However, unauthorised individuals may steal sensitive users' data from cloud devices, leading to significant privacy leakage risks. The main problem is that the non-authentication policy leads to key leakages due to unauthorised entries through intruders. To resolve this problem, we propose Blockchain-Based Decentralized Security (BCDS) using Crypto-Proof of Stake (CPoS) for securing sensitive Personal health care records. First, to process the security, the lattice construction phase is used based on blockchain decentralized. The Random shuffle block padding is intended to rehash the block to score with padding bits to block-level encryption. Then the sensitive terms are marginalized by threshold labels being highly encrypted by Optimized Starvation Point Link Encryption (OSPLE) to protect the data. By integrating Master Node Key Aggregation Policy (MNKAP) to verify the contact proof of staking authentication to the peer end to authenticate the security. The Delegate proof of stack provides higher security to protect the sensitive terms by accessing PHR records transactions to improve the integrity proofing to the admittance of the key to deliver the data. The key signature verification then validates the key for authentication to decrypt the protected data. This proves a higher security protocol and authentication principle than any other method as well as security proofing.
CryptoScratch: Teaching Cryptography with Block-based Coding

We have implemented a novel framework called CryptoScratch for teaching cryptography using the widely available Scratch platform. CryptoScratch enables K-12 students to learn how to use cryptographic algorithms such as AES, RSA, and SHA2 and combine these algorithms to build complex modern cryptographic schemes such as Digital Signatures and Pretty Good Privacy. CryptoScratch implements the algorithms as intuitive visual blocks abstracting away the mathematical and technical details of the algorithms. This poster discusses the implementation of CryptoScratch and how using these blocks can help students develop and understand the importance of cryptography in their digital lives.
Arguing Correctness of ASP Programs with Aggregates

This paper studies the problem of arguing program correctness for logic programs with aggregates in the context of Answer Set Programming. Cabalar, Fandinno, and Lierler (2020) championed a modular methodology for arguing program correctness. We show how a recently proposed many-sorted semantics for logic programs with aggregates allows us to apply their methodology to this type of program. This is illustrated using well-known encodings for the Graph Coloring and Traveling Salesman problems. In particular, we showcase how this modular approach allows us to reuse the proof of correctness of a Hamiltonian Cycle encoding studied in a previous publication when considering the Traveling Salesman program.
Classification of Acute Lymphoblastic Leukemia on White Blood Cell Microscopy Images Based on Instance Segmentation Using Mask R-CNN

Manual classification of acute lymphoblastic leukemia carried out by doctors will certainly take a lot of time and effort.The challenges in automated computer-based systems for classification of acute lymphoblastic leukemia are when providing proper lightning in stained white blood cell microscopy images and when segmenting the touching or overlapping cells in the image.Previous studies related to the classification of acute lymphoblastic leukemia still require many steps when using conventional methods, whereas when using deep learning methods are still limited to classification without providing analysis for the instance segmentation of lymphoblast in the image.Therefore, we propose instance segmentation using Mask R-CNN on white blood cell microscopy images to classify acute lymphoblastic leukemia that can support the diagnosis process efficiently and effectively.In this study, we implemented Mask R-CNN by transfer learning method to fit the instance segmentation task on white blood cell microscopy images.We added a contrast enhancement process to the image dataset to overcome the bad lightning problem in stained white blood cell microscopy images.We used the real dataset obtained from hospital to evaluate our method.The method we used was able to get 83.72 % accuracy, 85.17 % precision, and 81.61 % sensitivity.
Quantum Generative Adversarial Network and Quantum Neural Network for Image Classification

In this paper, a Quantum Neural Network (QNN) has been proposed using the Projected Quantum Kernel feature for an image classification task. The QCNN consists of four dense layers; the first layer collects the quantum data as an input and the fourth layer produced the classification output. Moreover, a Quantum Generative Advisory Network (QGAN) has been developed using the patching technique for enhancing the number of samples in the image dataset. The proposed QNN and QGAN are constructed using quantum filters. The MNIST handwritten digit dataset was used to train and test the QNN model performance on image classification. A binary classification dataset was created from the MNIST handwritten digit database using digits 0 and 6. The QGAN generated 221 samples on digits 0 and 6 classes. The generated samples were added to the training dataset for the QNN model. The size of the Filtered MNIST handwritten dataset was extended from 13779 to 14000 samples. There are 12,000 images are split for training and 2,000 images for testing. The principal component analysis technique was used to reduce the dimension of the data. The QNN was trained on the enhanced dataset using a GPU environment. The testing accuracy of the QNN model was 98.65 percent; it is superior to the traditional neural network.
Reconstructing phase aberrations for high-precision dimensional microscopy

In many industrial sectors, dimensional microscopy enables non-destructive and rapid inspection of manufacturing processes. However, wave-optical effects and imaging errors of the optical system limit the accuracy. With modelbased approaches it is possible to measure the physical position of edges and corners with submicron uncertainty. This requires an accurate model for phase aberrations of the optical system. We present a method to model and quantify those phase aberrations by an efficient inverse measurement.
FRATCF: Feature-Residue Real-Time UAV Tracking Based on Automatic Spatio-Temporal Regularization Correlation Filter

Traditional discriminative correlation filter (DCF) has received widespread popularity due to its high computational efficiency. However, most of the existing DCF-based trackers improve the learning of the target object by introducing some simple regularization methods in the detection stage, which may easily lose the tracking target in scenes with background clutter, fast-moving cameras and similar targets. We propose a feature residual filter with automatic spatio-temporal regularization, namely FRATCF, which can be strengthened the filter learning by introducing the feature residual between two adjacent frames in the training phase. Extensive experiments are conducted on two challenging unmanned aerial vehicle (UAV) benchmarks, i.e., UAV123@10fps and DTB70. Results prove that our tracker runs at ∼43 FPS on an extremely cheap configuration, which is about twice the speed of AutoTrack. The performance is also better than other state-of-the-art (SOTA) trackers.
Graph Convolution Network Based Representation for Multi-View Multi-Label Learning

For multi-view multi-label learning, exploring the relations between multiple views and the rational utilization of sam-ple correlations are challenging tasks. In order to explic-itly explore the commonality and individuality between mul-tiple views and to make use of the sample correlations, Graph Convolution Network based Representation (GCNR) for multi-view multi-label learning is proposed. The model first extracts the intermediate representations and constructs the graphs based on different views. Then the intersection of different adjacency matrices and graph convolutional neu-ral networks are used to explore the commonality between different views, while the individuality of views is explored based on the same network parameters and respective adja-cency matrices. Finally, the common and individual represen-tations are concatenated to predict labels. The experimental results show that the proposed model outperforms the state-of-the-art methods on multiple datasets.
Neural Network Based in-Loop Filter with Constrained Memory

In this paper, neural network based in-loop filters are designed to explore the potential performance improvement beyond the latest video coding standard Versatile Video Coding (VVC). Besides the performance, the model memory size is also taken into consideration. There are three filters which are designed with different sizes of memory requirement. Along with the un-filtered image, the prediction image and the partitioning image are also fed into the network. Additionally, the quantized parameter (QP) or slice type is fed into the network for classifying different situations when the model is shared by multiple sequence level base QPs or slice types. In the experiments, these filters are integrated into the latest reference software of VVC. An average of 12.26% YUV BD-rate saving is achieved for the filter with the largest memory size, and an average of 10.20% YUV BD-rate saving is achieved even if the required memory is reduced to 1/22.
Vehicle Tracking Method Based on Attention-YOLOv5 and Optimized DeepSort Models

Vehicle tracking plays a vital role in traffic management and autonomous driving. In order to further improve the accuracy of vehicle tracking, reduce the number of ID-Switch and enhance the anti-interference ability to the outside world, we propose a vehicle detection and tracking model based on Attention-YOLOv5 and optimized DeepSort. Firstly, YOLOv5 with attention mechanism was used as the front-end target detector, that is, add CBAM and ECA attention mechanisms to the backbone network of the benchmark YOLOv5 separately to improve the feature extraction ability. Then use the transfer learning idea to train it on the BDD dataset, it is concluded that ECA-YOLOv5 has the highest detection accuracy. Further we optimize the DeepSort algorithm, in order to overcome the disadvantages of the pre-training model not targeting traffic vehicles, we improve the re-recognition network structure and optimize the VeRi re-identification dataset, next perform re-identification pre-training on the Improved-VeRi dataset based on transfer learning. In order to make the vehicle features evenly distributed around the center of the class and improve feature resolution, a loss function <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${L_{vehicle\_CL}}$</tex> combining the Softmax loss function and Center loss function is proposed. In the experimental part, collecting some highway traffic videos of different scenes. At first, conduct visual analysis, then perform ablation experiments, and evaluate by using MOTA, MOTP and ID-Switch. Compared with the benchmark algorithm, the tracking performance is significantly improved.
Mobile robot path planning based on fusion of improved A* algorithm and adaptive DWA algorithm

Abstract For mobile robot autonomous navigation, path planning algorithm matters. For the reason that traditional A* algorithm is unable to achieve the global path planning perfectly and avoid unknown obstacles at the same time, the mobile robot algorithm combining improved A* with adaptive DWA was proposed for path planning. Firstly, safe distance judgment is added in A* algorithm to avoid the problem that the path crosses the vertex of obstacle diagonally. Secondly, the dynamic tangential method is used to smooth the path of A* algorithm, which is more consistent with the actual motion curve of mobile robot. Then, the path key points explored by improved A* algorithm are extracted as intermediate target points of the velocity adaptive DWA algorithm. Unknown obstacles avoidance based on the optimal global path planning is realized. Finally, the proposed fusion algorithm’s feasibility is verified through mobile robot ROS simulation.
A DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation

This demonstration is related to the submitted paper, “A 0.95 mJ/frame DNN Training Processor for Robust Object Detection with Real-World Environmental Adaptation”, (Submission ID: 45). Lightweight DNN is essential for energy-efficient DNN execution in mobile/edge devices. However, it suffers from significant accuracy degradation when it is applied to the new environment. In other words, the lightweight DNN loses generality due to its low network capacity. In particular, the mobile-oriented DNNs do not work properly in unexpected situations such as camera malfunction as shown in Fig. 1. Therefore, accuracy compensation for unpredictable accidents is important to prevent critical system damage. Real-time online DNN tuning is a promising solution to compensate accuracy of the lightweight network while maintaining its hardware benefits. In this demonstration, we demonstrate online tuning-based lightweight object detection execution based on our proposed processor and systems. The proposed processor successfully demonstrates 46.6 FPS object detection with 0.95 mJ/frame energy consumption which is the state-of-the-art performance compared with the existing processors.
An Anchor-Free Network With Box Refinement and Saliency Supplement for Instance Segmentation in Remote Sensing Images

The mainstream instance segmentation in remote sensing images takes the way of “Detect then Segment”. Anchor-free detectors get rid of the predefined anchors and can be used for proposal generation. However, in the existing methods based on anchor-free detectors, incompact proposals are directly used for segmentation, which may result in incomplete mask segmentation. In addition, the commonly used mask segmentation module is based on the trimmed features, which is not sufficient for accurate mask segmentation due to the loss of spatial details. In this letter, an anchor-free network is proposed for instance segmentation in remote sensing images. In order to obtain more compact proposals, a box refinement module, which predicts the distance offsets of a pixel to four sides of a proposal, is designed for initial proposals generated by an anchor-free detector. Based on the refined proposals, a saliency supplement module is designed to obtain accurate instance-wise masks by embedding the saliency map into the coarse masks. Compared with other methods, the proposed method conducted on two remote sensing datasets achieves optimal performance, with average precision (AP) of 0.652 and 0.678, respectively.
Parameter Tuning and Control: A Case Study on Differential Evolution With Polynomial Mutation

Metaheuristics are known to be effective for solving a broad category of optimization problems. However, most heuristics require different parameter settings appropriately for a problem class or even for a specific problem. Researchers address this commonly by performing a parameter tuning study (also known as hyper-parameter optimization) or developing a parameter control mechanism that changes parameters dynamically. Whereas parameter tuning is computationally expensive and limits the parameter configuration to stay constant throughout the run, parameter control is also a challenging task because all dynamics induced by various operators must be learned to make an appropriate adaptation of parameters on the fly. This paper investigates parameter tuning and control for a well-known optimization method - differential evolution (DE). In contrast to most existing DE practices, an additional individualistic evolutionary operator called polynomial mutation is incorporated into the offspring creation. Results on test problems with up to 50 variables indicate that mutation can be helpful for multi-modal problems to escape from local optima. On the one hand, the effectiveness of parameter tuning for a specific problem becomes apparent; on the other hand, its generalization capabilities seem to be limited. Moreover, a generic coevolutionary approach for parameter control outperforms a random choice of parameters. Recognizing the importance of choosing a suitable parameter configuration to solve any optimization problem, we have incorporated a standard implementation of both tuning and control approaches into a single framework, providing a direction for the evolutionary computation and optimization researchers to use and further investigate the effects of parameters on DE and other metaheuristics-based algorithms.
Applications for Green Technology Practices in Analyzing the Edge Detection Techniques on Digital Image Processing by using MATLAB

Abstract Increasing awareness and growing focus on environmental issues such as climate change, energy use, and the loss of non-renewable resources have created a great need for research that provides possible solutions to these problems. Environmental science and engineering research continue to play an important role in revealing new “green” future opportunities. Digital Image Processing plays a major role in contributing to its relevant research works under these Environmental and Green Technologies. By applying tools related to Environment technology this Digital Image Processing produces numerous applications. In such cases, Edges provide an important dynamic change in digital image processing. It is a process of finding and finding edges with sharp changes made by the power of its images. This is the best way to get an accurate edge in good shape and also different edge finders will work in better different conditions. Different methods of edge detection were compared comparatively to determine which method was most suitable for obtaining edges without any noise conditions. Here, the three types of edges have horizontal, vertical and diagonal edges. In this paper, we review the different editing methods and compare different identification methods, advantages, and disadvantages. Implementation is done using MATLAB. A sudden change of discontinuity is evident in the image.
NLP Based Anomaly Detection for Categorical Time Series

Identifying anomalies in large multi-dimensional time series is a crucial and difficult task across multiple domains. Few methods exist in the literature that address this task when some of the variables are categorical in nature. We formalize an analogy between categorical time series and classical Natural Language Processing and demonstrate the strength of this analogy for anomaly detection and root cause investigation by implementing and testing three different machine learning anomaly detection and root cause investigation models based upon it.
The Open Data Kit Project

Abstract This chapter presents the Open Data Kit (ODK) project’s design, challenges, development, and evolution of configurable software frameworks and their international development impacts. The ODK project created a suite of mobile data collection tools designed to help organizations working in resource-constrained contexts collect, aggregate, and analyze data. ODK mobile tools are designed to maintain their functionality in locations with limited, intermittent, or no Internet connectivity. While the first set of ODK tools has experienced wide adoption with millions of users collecting data, its primary use case of unidirectional data flowing from the field to a central repository could not be adapted to certain use cases. To broaden the scope of potential applications, a second generation of tools was developed that added support for bidirectional data management applications. This chapter discusses the iterative research and development process of the ODK project.
Federated Medical Image Analysis with Virtual Sample Synthesis

Hospitals and research institutions may not be willing to share their collected medical data due to privacy concerns, transmission cost, and the intrinsic value of the data. Federated medical image analysis is thus explored to obtain a global model without access to the images distributed on isolated clients. However, in real-world applications, the local data from each client are likely non-i.i.d distributed because of the variations in geographic factors, patient demographics, data collection process, and so on. Such heterogeneity in data poses severe challenges to the performance of federated learning. In this paper, we introduce federated medical image analysis with virtual sample synthesis (FedVSS). Our method can improve the generalization ability by adversarially synthesizing virtual training samples with the local models and also learn to align the local models by synthesizing high-confidence samples with regard to the global model. All synthesized data will be further utilized in local model updating. We conduct comprehensive experiments on five medical image datasets retrieved from MedMNIST and Camelyon17, and the experimental results validate the effectiveness of our method. Our code is available at Link .