Reviewerly/exHarmony huggingface dataset:
Robust decentralized stochastic gradient descent over unstable networks

Decentralized learning is essential for large-scale deep learning due to its great advantage in breaking the communication bottleneck. Most decentralized learning algorithms focus on reducing the communication overhead without taking into account the possibility of a shaky network connection, and existing analyses over unstable networks have various limitations such as centralized settings, strong unrealistic assumptions, etc. Hence, in this work, we study a non-convex optimization problem over unstable networks that fully consider unstable factors including unstable network connections, communication and artificially injected noise. Specifically, we focus on the most commonly used Stochastic Gradient Descent (SGD) algorithm in a mild decentralized setting and propose a robust algorithm to handle unstable networks. It is shown that our algorithm can attain a convergence rate which has the same order as decentralized algorithms over stable networks, and achieves linear speedup comparing with centralized ones. Moreover, the proposed algorithm also applies to the general case that the data are not independently and identically distributed. Extensive experiments on image classification demonstrate that the practical performance of our algorithm is comparable with the state-of-art decentralized algorithms in stable networks with only a little accuracy loss.
Dynamic heterogeneous graph representation learning with neighborhood type modeling

Graph representation learning aims to learn the representations of graph structured data in low-dimensional space, and has a wide range of applications in graph analysis tasks. Real-world networks are generally heterogeneous and dynamic, which contain multiple types of nodes and edges, and the graph may evolve at a high speed over time. The complex heterogeneous properties and rapidly evolving graph structures make it difficult to learn high-quality graph representations for dynamic heterogeneous graphs. Currently, studies concentrated on representation learning of temporal heterogeneous networks are insufficient. Existing methods either rely on meta-paths where the embedding quality heavily depending on experts' selection, or use network snapshots where the fine-grained temporal information cannot be captured. In this paper, we propose a novel graph neural network model–node signature based Temporal Heterogeneous Graph Attention Network, termed as THGAT, for learning the representations of dynamic heterogeneous networks. THGAT improves the aggregation way of neighborhood information, and pays attention to the enlightenment of the importance of neighbor nodes by heterogeneous information and temporal information that cannot be ignored in the network. We also innovatively propose three node signature methods for encoding the heterogeneous information of the nodes and use the time encoding technique suitable for real-time networks to directly represent the temporal information, so as to overcome the limitations of existing methods. We conduct experiments on four real-world datasets, and the results demonstrate that THGAT improves the representation learning quality significantly, in aspects of link prediction, node classification, and node clustering, compared to the state-of-the-art methods. To make the work more complete, we also analyze the applicable scenarios of the three node signature methods through experiments, respectively.
Consecutive layer collaborative filter similarity for differentiable neural network pruning

Filter pruning is proven to be an effective strategy in model compression. However, convolutional filter pruning methods usually pay all attention to evaluating filters’ importance at a single layer, ignoring their collaborative relationship with corresponding filters of the next layer. In this paper, we propose novel consecutive layer collaborative filter similarity (CLCS) to make full use of the complete filter information and learn binary selection vectors to prune the redundant filters automatically. With learned selection vectors, the pruning ratio of each layer can be determined, and we can also calculate the FLOPs of the candidate pruned network at the current stage. Under the accuracy constraint and the FLOPs constraint, the selection vectors of each layer can be optimized to achieve a better trade-off between accuracy and efficiency. Extensive experiments on CIFAR-10 and ImageNet with multiple networks demonstrate the effectiveness of our proposed method. Specifically, we obtain 54.29% and 67.33% FLOPs reduction with 0.01% and 0.09% accuracy improvement for ResNet-56 and ResNet-110 on CIFAR-10, respectively. On ImageNet, we reduce FLOPs by nearly half compared to the ResNet-50 baseline with almost no loss of accuracy. Compared with state-of-the-art filter pruning methods, our approach also achieves superior results.
RC-FODS algorithm for solving numerical solutions of fractional order dynamical system

We present a new method, the solving fractional order dynamical systems using reservoir computing (RC-FODS) algorithm, for solving fractional order nonlinear dynamical systems using deep learning. The method is shown to have advantages over traditional methods, such as less calculation time and higher accuracy. This study also compares the RC-FODS algorithm with the traditional recurrent neural network and echo state network algorithms and finds that it has a higher accuracy and shorter computation time. The accuracy of the method is validated using the largest Lyapunov exponent, and the study also analyzes the advantages and disadvantages of different deep learning models. Our study concludes that the RC-FODS algorithm is a promising method for solving fractional order nonlinear dynamical systems with a high accuracy and low error rate.
Full-span named entity recognition with boundary regression

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Implementing a deterministic search algorithm with a single qubit

Abstract Quantum search algorithms have important applications in quantum information, which have been experimentally demonstrated in several quantum systems with database sizes up to 16. We first propose an efficient quantum search scheme based on an optimized Grover’s algorithm and a novel encoding method. With this scheme, a target element can be deterministically searched in a large database using only a single qubit. Then we implement this search scheme in linear optics with database sizes up to 40. Experimental results fit well with the theoretical results, with all fidelities greater than 99.7%. The limitation of the number of elements encoded in a single qubit is also investigated in the experiment. The experimental results indicate that a target state can be successfully searched from a database of size 36 using a single qubit in our laboratory. Since only one qubit and one-qubit gates are required, this search scheme is quite efficient and general, and can be adopted to implement the same task in most quantum systems.
Factors Influencing Students’ Continuance Usage Intention with Virtual Classroom during the COVID-19 Pandemic: An Empirical Study

The constant variation of COVID-19 has intensified the spread and recurrence of the epidemic, and education continues to be hard hit in most countries. The virtual classroom has become the main platform to replace the traditional classroom in the COVID-19 pandemic context. Due to the lack of a comprehensive understanding of college students’ perceptions of the platform system, it is essential to explore the factors and mechanisms that influence students’ willingness to use virtual classrooms consistently to improve the learning efficiency and optimize the effect of educational communication during the epidemic. This study integrates the Delone and McLean (D&amp;M) information systems (IS) success model, expectation–confirmation model (ECM), and instructor quality factor to construct an operational model, and it used a structural equation model to analyze the 411 valid samples received from online questionnaires. The results reveal that the determinants of college students’ perceived usefulness of virtual classrooms are service quality, instructor quality, and confirmation, while system quality has no effect on perceived usefulness in the context of the COVID-19 pandemic. Secondly, system quality, service quality, and instructor quality are three critical antecedents of confirmation, and perceived usefulness and confirmation positively affect satisfaction. Finally, perceived usefulness and satisfaction directly affect college students’ continuance intention.
Advanced 5D logistic and DNA encoding for medical images

Since patient diagnostic data such as X-rays, MRIs, CT scans, etc. contain very sensitive information about a patient's health, it must be encrypted to prevent unauthorized access. In chaotic-based image encryption methods, the lower-dimensional chaotic maps are insufficient because they can be estimated through some signal estimation technologies; therefore, a higher-order novel 5D logistic chaotic map along with DNA encoding and the Arnold map for medical image encryption is presented to overcome the vital medical breaching that could cost a life. In this work, the advantages of DNA principles and high-dimensional chaotic maps are used to develop an efficient encryption method for the protection of medical images. The strategy is useful due to the fact that the higher order makes the system more complicated and tough to penetrate. The large keyspace up to, anti attack capabilities, and comparison of various standard parameters with different literatures illustrate the algorithm's superiority.
Automatic fake document identification and localization using DE-Net and color-based features of foreign inks

Document examination is a vital mission for revealing illegal modifications that assist in the detection and resolution of criminal acts. Addition and alteration are more frequently used in handwritten documents. However, most of the documents have been modified with similar inks, and it is tough to detect or observe them with human eyes. As a result, there is a need for methods to automatically detect handwriting forgery to reach an accurate detection efficiently. In this paper, a novel and efficient method is proposed for automatically detecting altered handwritten documents and locating the fake part. Therefore, DE-Net is proposed to identify the forged document using a digitally scanned version of the document. Unlike the existing methods, a further localization schema is applied to locate the forged parts in the candidate forged document accurately. Where each forged document is segmented into objects. Color histograms of R, G, and B channels are used to generate a fused feature vector for each object. Then a structural similarity index (SSIM) is applied to detect the lower similarity parts as forged. The experimental results demonstrate that the proposed method can identify and localize foreign ink in handwritten documents with high performance.
NuSegDA: Domain adaptation for nuclei segmentation

The accurate segmentation of nuclei is crucial for cancer diagnosis and further clinical treatments. To successfully train a nuclei segmentation network in a fully-supervised manner for a particular type of organ or cancer, we need the dataset with ground-truth annotations. However, such well-annotated nuclei segmentation datasets are highly rare, and manually labeling an unannotated dataset is an expensive, time-consuming, and tedious process. Consequently, we require to discover a way for training the nuclei segmentation network with unlabeled dataset. In this paper, we propose a model named NuSegUDA for nuclei segmentation on the unlabeled dataset (target domain). It is achieved by applying Unsupervised Domain Adaptation (UDA) technique with the help of another labeled dataset (source domain) that may come from different type of organ, cancer, or source. We apply UDA technique at both of feature space and output space. We additionally utilize a reconstruction network and incorporate adversarial learning into it so that the source-domain images can be accurately translated to the target-domain for further training of the segmentation network. We validate our proposed NuSegUDA on two public nuclei segmentation datasets, and obtain significant improvement as compared with the baseline methods. Extensive experiments also verify the contribution of newly proposed image reconstruction adversarial loss, and target-translated source supervised loss to the performance boost of NuSegUDA. Finally, considering the scenario when we have a small number of annotations available from the target domain, we extend our work and propose NuSegSSDA, a Semi-Supervised Domain Adaptation (SSDA) based approach.
Robust Object Tracking Framework for Occlusion Area using YOLOv5 and Dual Kalman Filter

YOLO(You Only Look Once)는 실시간으로 객체 검출이 필요한 분야에서 널리 사용되고 있지만, 검출하려는 대상이 다른 객체 혹은 주변 배경 등에 의해 가려지는 폐색영역에서 성능이 낮아지는 한계가 존재한다. 본 연구에서는 YOLOv5와 Dual Kalman Filter(KF)를 활용한 폐색영역에 강건한 객체 추적 프레임워크를 제안한다. 여기서, Dual KF는 상호 보완적인 특성을 가진 2개의 KF로 구성된다. 제안 프레임워크는 YOLOv5가 객체검출에 실패하면 YOLOv5의 검출 결과에 비중을 두고 업데이트되는 Detection KF의 결과를 사용한다. 만약 폐색영역으로 인해 연속적인 프레임 동안 객체를 검출하지 못하면, KF의 예측 결과에 비중을 두고 업데이트되는 Inference KF의 결과를 사용하여 객체 추적을 시도한다. 폐색영역이 등장하는 데이터를 활용한 실험을 통해, 제안 프레임워크는 기존 방법론보다 연산 속도는 소폭 느리지만, 정확도는 우수한 것을 확인할 수 있었다. Although YOLO(You Only Look Once) is a widely used algorithm in real-time object detection, it has a limitation in that its performance significantly deteriorates in occlusion areas where a detection target is obscured by another object or surrounding background. In this study, we propose a robust object tracking framework that utilizes YOLOv5 and a Dual Kalman Filter(KF) consisting of Detection and Inference KFs to address this issue. The proposed framework uses the Detection KF updated with a high weight on the detection results of YOLOv5 when YOLOv5 fails to detect objects. If the object is not detected over successive frames, the proposed framework attempts to track the object using the Inference KF updated with a high weight on the prediction results of KF. Through experiments using data with occluded regions, we confirmed that the proposed framework outperformed existing approaches in terms of detection accuracy while sacrificing less computation speed.
Comparison of lithium borate fusion and four-acid digestions for the determination of whole-rock chemistry – implications for lithogeochemistry and mineral exploration

Mining and exploration companies routinely use four-acid digestion (4AD), inductively coupled plasma, atomic emission spectra/mass spectrometry methods from commercial assay laboratories for analysing drill and rock samples for lithogeochemical assessment and resource reporting. This method is also known to exhibit lower recovery of elements hosted by resistate minerals. To assess the impact of lower recoveries on lithogeochemical interpretation, a suite of commonly used elements for lithogeochemical analysis (high-field-strength elements Zr, Hf, Nb, Ta, Ti and Eu and transition elements V and Sc) was analysed by 4AD and alkali fusion/acid digestion (AFAD). Lower recoveries in the 4AD relative to the AFAD were recorded for Zr, Hf, Nb, Ta, Ti and Eu; Sc and V reported similar concentrations for both decomposition methods. Despite the lower recoveries for Nb, Ta and Ti, element ratios were largely preserved with the 4AD method due to the recoveries covarying at a 1:1 ratio. A plot of Ti/Nb against V/Sc was found to be largely unaffected by decomposition method, producing similar compositional classifications between the two digestion methods. Use of the Eu anomaly (Eu/Eu*) to determine plagioclase fractionation was also found to be unaffected by decomposition method. In contrast, a standard Zr/Ti v. Nb/Y discrimination plot produced incorrect classifications with 4AD producing more mafic and alkaline classifications relative to the AFAD method. Magmatic fertility interpretations utilizing Zr/Hf were also found to be affected in the 4AD results due to the lower recovery of Zr relative to Hf. This resulted in a bias in the 4AD results and produced false-positive anomalism in fertility assessments. Multiple decomposition methods including combinations of acid and fusion methods are recommended for lithogeochemical analysis utilizing large regions of the periodic table. However, if only 4AD data are available, plots such as Ti/Nb v. V/Sc and Nb/Ta, which preserve their ratios, can be used for lithogeochemical classification. Supplementary material: Wholerock geochemical data and detailed methods are available at https://doi.org/10.6084/m9.figshare.c.6444444
A Minimal Solution for Image-Based Sphere Estimation

Abstract We propose a novel minimal solver for sphere fitting via its 2D central projection, i.e. , a special ellipse. The input of the presented algorithm consists of contour points detected in a camera image. General ellipse fitting problems require five contour points. However, taking advantage of the isotropic spherical target, three points are enough to define the tangent cone parameters of the sphere. This yields the sought ellipse parameters. Similarly, the sphere center can be estimated from the cone if the radius is known. These proposed geometric methods are rapid, numerically stable, and easy to implement. Experimental results—on synthetic, photorealistic, and real images—showcase the superiority of the proposed solutions to the state-of-the-art methods. A real-world LiDAR-camera calibration application justifies the utility of the sphere-based approach resulting in an error below a few centimeters.
Software selection in large-scale software engineering: A model and criteria based on interactive rapid reviews

Software selection in large-scale software development continues to be ad hoc and ill-structured. Previous proposals for software component selection tend to be technology-specific and/or do not consider business or ecosystem concerns.Our main aim is to develop an industrially relevant technology-agnostic method that can support practitioners in making informed decisions when selecting software components for use in tools or in products based on a holistic perspective of the overall environment.We used method engineering to iteratively develop a software selection method for Ericsson AB based on a combination of published research and practitioner insights. We used interactive rapid reviews to systematically identify and analyse scientific literature and to support close cooperation and co-design with practitioners from Ericsson. The model has been validated through a focus group and by practical use at the case company.The model consists of a high-level selection process and a wide range of criteria for assessing and for evaluating software to include in business products and tools.We have developed an industrially relevant model for component selection through active engagement from a company. Co-designing the model based on previous knowledge demonstrates a viable approach to industry-academia collaboration and provides a practical solution that can support practitioners in making informed decisions based on a holistic analysis of business, organisation and technical factors.
A Comparative Analysis of Optimization Heuristics Algorithms as Optimal Solution for Travelling Salesman Problem

Travelling Salesman Problem (TSP) is considered non-deterministic polynomial time hard (NP hard) problem that cannot be solved traditionally especially when the number of cities increase. Therefore, Heuristic Algorithms are feasible solution to such type of problem. TSP is a representative of a larger class of problems known as combinatorial optimization problems. In TSP, if a Salesman wants to sell goods in different cities, he leaves a city and visits each other cities exactly once and returns back to the starting city. People may want to plan for the fastest or the most economical method to their destinations. The research aims to examine and develop effective and efficient optimization technique to get a shortest or suboptimal path. Google map uses Dijkstra's Algorithm as its fast-finding algorithm which is reported to have problem in searching for all route within a limited location. On the other hand, Ant Colony Optimization Algorithm can contribute effectively in solving lots of problems including shortest path problems, particularly, where other algorithms are inefficient. Both ACO and Dijkstra's Algorithm for simulations with a given routes(length) gave better result than random generation of routes for given cities. Results from the simulation experiment for ACO shows the Best Routes and total length for the best routes while results from Dijkstra's Algorithm show Minimum Cost for source node and destination node, for PSO gives better result with slower convergence. More iterations lead to get accurate results especially PSO Algorithm.
Deep Highlight Removal Using Temporal Dark Prior in High-Speed Domain

This paper proposes a deep highlight removal method based on the dichromatic model under alternating current (AC) light sources with a new diffuse prior on temporal domain, temporal dark prior.An input image is decomposed into specular and diffuse components using a deep network.Due to AC powered lights, both incident and reflected lights are time-varying.We exploit the periodic variation property of the specular and diffuse reflections as a prior for dichromatic model based image decomposition.In addition, we propose a new temporal dark prior as a pseudo-diffuse reflection.Unlike the conventional prior in the spatial domain, to the best of our knowledge, this is the first study to utilize a diffuse prior on the temporal domain for highlight removal.The blurred version of the temporal dark prior is additionally fed to the network to alleviate hole artifacts.It is demonstrated through diverse experiments that these temporal priors can strongly contribute to accurate image decomposition, leading to better highlight removal.INDEX TERMS Dichromatic model, highlight removal, high
Group Tracking for Video Monitoring Systems: A Spatio-Temporal Query Processing Approach

Recently, many video monitoring systems utilize deep learning technologies to recognize locations and trajectories of people in video data. In video monitoring systems, a fast discovery of human groups is an important task for several applications, for example, crime surveillance, contact tracing, and customer behavior analysis. To tackle the demand, we propose a group tracking method. First, we propose a spatial proximity definition and define a novel query type, a group tracking query that considers characteristics of video data. A group tracking query retrieves the groups that travel for more than a certain amount of video frame within a certain distance. We propose an efficient query processing method that exploits the spatio-temporal characteristics of groups. Through extensive experiments using real-world datasets, we verify the efficiency and effectiveness of our query definition and query processing method.
Recommendation System Using Deep Learning

Recommendation systems are a commom feature of many services nowadays. Using a significant quantity of user history that has been stored, this system forecasts what a user will use next. Recommendation systems are frequently used in a variety of industries, including e-commerce, social services and movies. We are attempting to compare three distinct deep learning models—RBM, Autoencoder, and DNN and comparing their efficiency in order to determine which model will perform best given the supplied dataset. We conducted experiments on real world data like MovieLens, which has four csv files to compare the efficacy of different Deep Learning models. These experimental results show that the query features and item features which Deep Neural Network can readily incorporate may help identify a user’ s interests and increase the relevancy of recommendations.