Reviewerly/exHarmony huggingface dataset:
Optimizing quantum gates within decoherence-free subspaces

A scheme for optimizing quantum gates in quantum computation on decoherence-free subspaces using optimal control theory is presented. Compared with the previous scheme, this scheme possesses the following properties: (i) efficiency in finding control sequences, specifically in a system without controllable exchange interaction where the previous method is difficult to handle; (ii) robustness against control errors; and (iii) showing better performance than the robust GRAPE method under stochastic noise. As an application, we work out the control sequences for the not, quantum phase, and cnot gates.
Nasolabial Wrinkle Segmentation Based on Nested Convolutional Neural Network

Wrinkles one of the common structures on human faces. Their detection is often challenging to effectively cope with skin images and can be an important step for many different applications. Skin wrinkle segmentation play an important role in face-feature analysis and assessing the beneficial effects of dermatological and cosmetic anti-aging treatments. Existing approaches of the image-based analysis of wrinkle extraction performance, which usually decreased because of weakness of wrinkle edges and similarity to the surrounding skin. In this paper, nested convolution neural network is applied to extract nasolabial wrinkles from facial images. In addition we applied a structure of deep encoder - decoder style network suitable for nasolabial wrinkle extraction. The proposed nested network, shows state-of-the-art results obtained an accuracy of 98.9%, which demonstrate novelness of this method
Heterogeneous Neural Collaborative Filtering for a Business Recommender System

In recent years, lots of techniques applied and optimized to make the recommender system better. Most of them mainly focus on the target interaction between users and items or a part of auxiliary information but hardly leverage other useful information relevant to customer transactions. In this paper, we want to propose a new model named Heterogeneous Neural Collaborative Filtering (HNCF) for learning recommender systems from two important parts: multi-aspects and multi-behaviors. The HNCF algorithm proposed is divided into four parts: Commuting similarity matrix, Multi-Layer Perceptron, Fusion by the Attention Mechanism, Multi-behavior Prediction. The model exploits characteristics of customers and properties of products from different aspects besides the aspect of purchase by building meta paths then commuting similarity matrices. Aspect-level latent factors fusion gives results as factors representing each user and item. These factors synthesizing with multi-behaviors prediction layers is the highlight of the model to make a better recommender system.
Quantum Coherence of Atoms with Dipole–Dipole Interaction and Collective Damping in the Presence of an Optical Field

We investigate the effect of the interatomic distances and thermal reservoir on the coherence dynamics of the atoms considering the dipole–dipole interaction (DDI) and collective damping effect (CDE). We show that the control and protection of the coherence are very sensitive to the interatomic distances and reservoir temperature. Furthermore, we explore the distance effect between atoms and reservoir temperature on the time evolution of the total quantum correlation between the two atoms. The obtained results could be useful to execute these quantum phenomena and also considered as a good indication to implement realistic experiments with optimal conditions.
FaceNet with RetinaFace to Identify Masked Face

The use of masks due to the Covid-19 pandemic reduces the accuracy of facial recognition systems applied to camera-based security systems. The use of the mask by the people covers most of the facial featureswhich is located from middle to bottom area. In addition, the area which are still visible are the upper face which are eyes and forehead. This paper proposes a masked face recognition using a combination of RetinaFace as a face detector and FaceNet as a face recognizer. The MFR2 dataset with 53 identities was used to train and test this method. The test data in this study are only images of masked faces. Cosine Distance was implemented to measure the face similarity. Based on the experiment results, the proposed method obtained 98.2&#x0025; of detection accuracy. The proposed method provided 78&#x0025; accurate performance with 3.63 s for processing a single frame in terms of face recognition. The performance indicates that our system can potentially be applied in security systems with many different identities.
Improve the Accuracy of Software Project Effort and Cost Estimates in COCOMO II Using GWO

Most of the software industry uses the Constructive Cost Model to be able to estimate the effort and cost of software projects, such as COCOMO II which has a dependence on cost drivers. Value of cost driver can affect the accuracy of the project effort and cost estimate. However, the COCOMO II accuracy value is considered to be less accurate because there is still a large difference between the actual project effort and the cost estimated value. To improve the accuracy of COCOMO II, Grey Wolf Optimization (GWO) method is used which is based on the behavior of wolves in catching prey. In this study, COCOMO II GWO is used to obtain a higher and more accurate level of estimation accuracy and reduce the total error value or Mean Magnitude Relative Error (MMRE) of software projects. From the test result when compared to MMRE produced by previous study COCOMO II BCO (Bee Colony Optimization) was 12.92%. Meanwhile, MMRE by proposed method COCOMO II GWO IS 1.731%. It means the proposed method can reduce the error value in MMRE by 11.19%.
Topic Recognition and Correlation Analysis of Articles in Computer Science

Topic identification and similarity detection are two related essential task in data mining, information retrieval, and bibliometric data analysis, which aims to identify significant topics and to find similarity between text collections.It is an essential activity to identify research papers according to their research topics to enhance their retrievability, help create smart analytics, and promote a range of approaches to evaluating the research environment and making sense of it.The proposed frame work deals with three main steps: text extraction, topic identification, and similarity detection.The PyPDF2 module is used to extract text from pdf file. CSO classifier is used for topic identification and similarity between documents is calculated using different models, such as Tf-Idf, Bert, Glove, Word2vec, and Doc2vec.and compared these models with respect to cosine similarity and Eucleadian distance obtained from these models.
A hybrid clustering algorithm based on improved GWO and KHM clustering

To solve the problem that the K-means algorithm is sensitive to the initial clustering centers and easily falls into local optima, we propose a new hybrid clustering algorithm called the IGWOKHM algorithm. In this paper, we first propose an improved strategy based on a nonlinear convergence factor, an inertial step size, and a dynamic weight to improve the search ability of the traditional grey wolf optimization (GWO) algorithm. Then, the improved GWO (IGWO) algorithm and the K-harmonic means (KHM) algorithm are fused to solve the clustering problem. This fusion clustering algorithm is called IGWOKHM, and it combines the global search ability of IGWO with the local fast optimization ability of KHM to both solve the problem of the K-means algorithm’s sensitivity to the initial clustering centers and address the shortcomings of KHM. The experimental results on 8 test functions and 4 University of California Irvine (UCI) datasets show that the IGWO algorithm greatly improves the efficiency of the model while ensuring the stability of the algorithm. The fusion clustering algorithm can effectively overcome the inadequacies of the K-means algorithm and has a good global optimization ability.
Testing ASTER and Sentinel-2 MSI Images to Discriminate Igneous and Metamorphic Rock Units in the Chadormalu Paleocrater, Central Iran

In the last fifty years, satellite images have been used to map the Earth’s surface at a variety of scales. Two satellite multispectral sensors (Sentinel-2 MSI and ASTER) have great utility for lithological discrimination in areas of good rock exposures. This study was conducted in order to test the ability of these sensors to discriminate igneous and metamorphic lithologies in the Chadormalu paleocrater and evaluate the image types and processing methodologies. The MNF (Minimum Noise Fraction) transform, Mathematical Evaluation Method (MEM), Spectral Angle Mapper (SAM), Mixture Tuned Matched Filter (MTMF), and band ratios were performed on near and short wave infrared ASTER and Sentinel-2 bands. Comparison of the results from several methods demonstrates that the MEM method can detect lithological units with very low false detection and better matching with ground truth data. Moreover, this study indicates that the results produced by the MEM algorithm on Sentinel-2 MSI data are more accurate than the results produced with ASTER data in the same area. Therefore, the MEM algorithm seems to be well suited for image classification involving multispectral databases such as ASTER and Sentinel-2 images.
Coarse-to-Fine Hand–Object Pose Estimation with Interaction-Aware Graph Convolutional Network

The analysis of hand-object poses from RGB images is important for understanding and imitating human behavior and acts as a key factor in various applications. In this paper, we propose a novel coarse-to-fine two-stage framework for hand-object pose estimation, which explicitly models hand-object relations in 3D pose refinement rather than in the process of converting 2D poses to 3D poses. Specifically, in the coarse stage, 2D heatmaps of hand and object keypoints are obtained from RGB image and subsequently fed into pose regressor to derive coarse 3D poses. As for the fine stage, an interaction-aware graph convolutional network called InterGCN is introduced to perform pose refinement by fully leveraging the hand-object relations in 3D context. One major challenge in 3D pose refinement lies in the fact that relations between hand and object change dynamically according to different HOI scenarios. In response to this issue, we leverage both general and interaction-specific relation graphs to significantly enhance the capacity of the network to cover variations of HOI scenarios for successful 3D pose refinement. Extensive experiments demonstrate state-of-the-art performance of our approach on benchmark hand-object datasets.
Obstacle avoidance in leader-follower formation using artificial potential field algorithm

In this paper, a formation control method for two non-holonomic mobile robots is presented. The mobile robot formation achieved the goal while avoiding the obstacles. The control algorithm presented consists in 1) avoiding the obstacles using potential forces by the artificial potential field methodology and 2) the leader-follower technique for the formation approach. In the proposed methods, the leader of the formation plans the path applying the potential field algorithm and the follower maintains a desired distance and a desired bearing angle with respect to the leader. Moreover, the proposed algorithm avoid the local minimum problem in the formation, implying the leader will reach the goal without having to stop due to the equal forces of attraction and repulsion. The stability of the tracking problem was described using a Lyapunov function to validate the proposed method. Simulations results were showed in order to verify the theoretical method.
Embedded electronic medical record text data mining using neural network association classification algorithm

Abstract The embedded electronic medical record (EMR) information is analysed through a neural network algorithm in the field of deep learning to mine the main sentence of EMR text. The pure convolutional neural network model (PCNN) based on pooled and non‐pooled analysis is constructed based on deep learning CNN to mine EMR text. An association classification algorithm based on neural network is proposed, which integrates classification rules into the neural network and realizes the rapid establishment of neural network structure and parameter setting. Character embedding is imported and combined with a sliding window. Then, CNN is adopted to classify the Chinese character tags of the neural network model. Finally, the model is applied to the word segmentation of the actual pregnant women's EMR text. The results show that the scores of the Biomedical Engineering Society and Bioelectronics in Peking University and Microsoft Reserved Partition datasets are between 0.9516 and 0.9684 and the performance is basically the same, which shows that the model has strong stability. The performance of the max‐pooling, n‐max pooling, and mean‐pooling are all between 0.8004 and 0.8634, but the above results are not as good as the model without pooling layer. For this new fast method to obtain neural network parameters, once the classification rules are given, the parameters in PCNN can be set quickly, and provide a practical basis for the development of deep learning in the medical field.
Cryptocurrency price forecasting based on shortterm trend KNN model

Since the appearance of bitcoin, the cryptocurrency market has gradually prospered. The price of cryptocurrency often changes rapidly and fluctuates greatly, which makes many investors rush into the market, ignoring the risk behind it. The price prediction of cryptocurrency can not only provide a reference for investors' investment but also reveal its financial laws to a certain extent and improve the risk early warning mechanism as well as improve its stability and security. In this paper, the KNN algorithm is used to forecast the cryptocurrency price, and the KNN model is improved based on the short-term price trend before the trading day. The experimental results show that the improved KNN model has a more accurate prediction result than the logistic regression model and traditional KNN model.
MultiVTrain: Collaborative Multi-View Active Learning for Segmentation in Connected Vehicles

While deep learning has brought promising advances to semantic segmentation tasks for autonomous vehicles, the performance strongly depends on the coverage of collected data and the quality of annotations. To overcome the barrier of insufficient high quality training data covering a complex range of vehicular scenarios, in this paper, we propose a multi-view-based active learning framework (MultiVTrain), which enables the vehicles to collaboratively generate training data and accurate labels without querying remote human annotators. As images captured by RGB cameras are vulnerable to occlusion and limited field-of-view, a novel multi-view prediction transfer scheme is introduced to leverage sensor data fusion and transfer predictions of one view to another. This allows information from different views to be aggregated, which improves the quality of the generated annotations. Extensive evaluation results demonstrate that our proposed MultiVTrain framework outperforms other active learning baselines by ∼ 9%, and passive supervised learning baselines trained with ground truth labels by ∼ 2.5%, for the same training set size.
Goal Modeling and MDSE for Behavior Assistance

Systems providing their end-users behavior assistance must be customized to meet those users’ needs and behavior goals. We investigate how it is possible to improve the engineering process of such systems and provide humans better support by using human behavior goals not only for analysis but also in the design and run-time of a system. Current research focuses either on the analysis phase of a system or uses goals at the run-time of an application. They do not consider the mapping of human behavior goals from one phase to the other one needed for generative approaches. Within this paper, we present our vision towards the use of goals and goal modeling for human behavior assistance in generated systems. We show its application by adding assistive functionalities to an existing, full-size real-world information system. For the engineering of this system, we follow a model-driven, generative software engineering approach. Our prototypical implementation towards the vision of using goal models for human behavior assistance shows the feasibility of the approach and provides first insights into how it could improve the assistive abilities of systems towards end-user goals.
Quasi Oppositional—Manta Ray Foraging Optimization and Its Application to PID Control of a Pendulum System

This paper presents an improved version of Manta Ray Foraging Optimization (MRFO). MRFO is relatively a single objective optimization algorithm. It was inspired from the behavior of a cartilaginous fish called Manta Ray. Manta Ray applies three strategies in searching foods which are chain, cyclone and somersault foraging. From the study, MRFO is a relatively new developed algorithm and has low convergence rate. However, MRFO has potential to be improved in that aspect. In the meanwhile, Opposition-based Learning (OBL) is a well-known technique in increasing the convergence rate. Therefore, a type of OBL namely Quasi Oppositional-based Learning will be adopted into MRFO in order to increase the possibility of finding the solution by considering the opposite individual location of fitness. This version of MRFO is called as Oppositional-based MRFO (OMRFO). Further, OMRFO was performed on several benchmark function. A statistical non-parametric Wilcoxon Test was conducted to analyze the accuracy of MRFO and OMRFO. Furthermore, the proposed algorithm was applied to an inverted pendulum system. Result from shows that performance of OMRFO is significantly outperformed MRFO after tested in the benchmark functions.
Ensemble Machine Learning Model for Mortality Prediction Inside Intensive Care Unit

Intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early mortality prediction is a crucial issue in intensive care. If a patient's likelihood of survival or mortality is predicted early enough, the patient will give proper and timely care to save the patient's life. In recent decades various severity scores and machine learning models have been developed for mortality prediction. By contrast, mortality prediction is still an open challenge. The main objective of this study is to provide a new framework for mortality prediction based on an ensemble classifier. The proposed ensemble classifier amalgamates five different classifiers: Linear discriminant analysis, decision tree, multilayer perceptron, K-nearest neighbor, and logistic regression. Data is vertically divided according to expert medical opinion into six feature sets, choosing the most accurate classifier for each subset of features. Framework evaluated benchmark data from Medical Information Mart for Intensive Care (MIMIC III) database using the first 24 h collected for each patient. The performance was validated using standard metrics include Precision, Recall, F-score, Area under the roc curve. Results show F-score 91.02% with precision 92.34% recall 90.14% for MLP classifier, achieved F-score of 93.7%, precision of 96.4%, recall of 91.1%, and AUROC of 93.3% for proposed ensemble classifiers which outperforms using classical classifiers. The results show the validity of the proposed system to be an assistant system for physicians in ICU.
Construction and application of knowledge graph of domestic operating system testing

Aiming at the problems of poor reusability of domestic operating system test cases and insufficient sharing of test case design experience at this stage, a method for constructing knowledge graphs in the field of domestic operating system testing is proposed, and ontology construction and natural language processing technologies are applied to the field of software testing. Use the strong correlation of the knowledge graph to mine the experience knowledge in the design of historical test cases, select and reuse test cases that meet the test requirements for testers, and help them design test cases more efficiently. Through empirical research, this method gives full play to the advantages of knowledge graphs in relational network analysis and retrieval, and the coverage rate of reused test cases reaches 71%, which can greatly save test costs and improve test efficiency, and has strong engineering application value.
Safety Helmet Detection Based On YOLOV3N

The YOLOv3 algorithm is widely used in the industry due to its high speed and high precision. Aiming at the problem of low detection accuracy and slow detection rate of wearing helmets in intelligent monitoring, a detection algorithm YOLOv3N based on improved YOLOv3 (You Only Look Once) is proposed. Improve the network structure on the basis of the YOLOv3 algorithm, replace the Darknet-53 traditional convolution with a convolution structure with fewer parameters, reduce model parameters, and increase the detection rate; in order to screen out the required detection frames more reasonably, the NMS is optimized. Experimental results show that compared with YOLOv3, YOLOv3N improves the number of frames per second (FPS) by 64%, and achieves an accuracy of 93.8%.
Auditing and Debugging Deep Learning Models via Flip Points: Individual-Level and Group-Level Analysis

Abstract Deep learning models have been criticized for their lack of easy interpretation, which undermines confidence in their use for important applications. Nevertheless, they are consistently utilized in many applications, consequential to humans’ lives, usually because of their better performance. Therefore, there is a great need for computational methods that can explain, audit, and debug such models. Here, we use flip points to accomplish these goals for deep learning classifiers used in social applications. A trained deep learning classifier is a mathematical function that maps inputs to classes. By way of training, the function partitions its domain and assigns a class to each of the partitions. Partitions are defined by the decision boundaries which are expected to be geometrically complex. This complexity is usually what makes deep learning models powerful classifiers. Flip points are points on those boundaries and, therefore, the key to understanding and changing the functional behavior of models. We use advanced numerical optimization techniques and state-of-the-art methods in numerical linear algebra, such as rank determination and reduced-order models to compute and analyze them. The resulting insight into the decision boundaries of a deep model can clearly explain the model’s output on the individual level, via an explanation report that is understandable by non-experts. We also develop a procedure to understand and audit model behavior towards groups of people. We show that examining decision boundaries of models in certain subspaces can reveal hidden biases that are not easily detectable. Flip points can also be used as synthetic data to alter the decision boundaries of a model and improve their functional behaviors. We demonstrate our methods by investigating several models trained on standard datasets used in social applications of machine learning. We also identify the features that are most responsible for particular classifications and misclassifications. Finally, we discuss the implications of our auditing procedure in the public policy domain.