Reviewerly/exHarmony huggingface dataset:
Complementarity in quantum walks

We study discrete-time quantum walks on $d$-cycles with a position and coin-dependent phase-shift. Such a model simulates a dynamics of a quantum particle moving on a ring with an artificial gauge field. In our case the amplitude of the phase-shift is governed by a single discrete parameter $q$. We solve the model analytically and observe that for prime $d$ there exists a strong complementarity property between the eigenvectors of two quantum walk evolution operators that act in the $2d$-dimensional Hilbert space. Namely, if $d$ is prime the corresponding eigenvectors of the evolution operators obey $|\langle v_q|v'_{q'} \rangle| \leq 1/\sqrt{d}$ for $q\neq q'$ and for all $|v_q\rangle$ and $|v'_{q'}\rangle$. We also discuss dynamical consequences of this complementarity. Finally, we show that the complementarity is still present in the continuous version of this model, which corresponds to a one-dimensional Dirac particle.
Application of subspace-based CNN speech noise reduction model in medical field

In order to enhance the noise reduction capability and recognition efficiency of speech recognition technology applied in medical devices, this paper adopts an improved subspace CNN based noise reduction model, adding a subspace projection module with two orthogonal self-attentive mechanisms to the original model, inputting the feature vectors extracted from the convolutional layer to the self-attentive layer, and the two self-attentive layers learn and project orthogonally to each other to obtain the noise embedding vector and speech embedding vectors to extract cleaner speech. In this paper, the improved subspace CNN noise reduction model is compared with the CNN noise reduction model by comparing the distortion degree, segmental signal-to-noise ratio and quality at different signal-to-noise ratios to demonstrate the reliability of the model. The results show that the overall distortion level of the model is reduced by about 7.27%, the overall segmental SNR performance is increased by about 15.27% and the quality is improved by about 8.7%, which is a certain improvement in performance compared with the traditional CNN noise reduction model.
Optimization of landslide detection method based on improved mask R-CNN

Aiming at the problems of difficult feature extraction, low detection accuracy and long detection time in traditional landslide detection methods, considering the diversity and complexity of landslide and its background environment, a landslide detection method based on mask region-based convolutional networks (Mask R-CNN) and Squeeze-andExcitation (SE) attention mechanism is proposed. The training set with 2050 samples and the test set with 600 samples were established for landslide detection, which verified the effectiveness of the improved method. The precision, recall and F1-score of the improved recognition are 89.2 %, 98.0 % and 93.4 %, respectively. Precision, recall, and F1-score all rose in comparison to the model before the improvement by 2.9%, 3.9%, and 3.4%, respectively. The findings demonstrate that the enhanced method can effectively increase the accuracy of landslide identification., and the landslide boundary segmentation results are closer to the real landslide boundary, so as to realize automatic and efficient landslide detection.
A quadrilateral distorted image correction algorithm based on block triangle

In the process of image shooting, due to the shooting angle or the shooting reason, the original image has geometric deformation problems in the geometric position, shape, size and orientation, which brings many inconvenience and difficulties to the following image processing fields such as image fusion, image denoising, image recognition, and image edge detection. In order to further improve the image processing ability and recognition accuracy of distorted graphics, this paper proposes a distortion correction algorithm based on block mapping for quadrilateral target images. This algorithm completes the transformation of the image from irregular to regular by dividing the quadrilateral image into two triangle images and performing homography mapping on the pixel points of the triangle image respectively. A large number of experiments show that the algorithm in this paper is superior to traditional methods such as hough and radon transform, and solves the limitations of traditional mapping algorithm in the process of quadrilateral image correction, improves the correction effect of distorted images, and provides a strong support for the subsequent processing of distorted images.
Multi-scale feature fusion facial expression recognition based on attention mechanism

Aiming at the problem that the traditional convolutional neural network is easy to lose a lot of useful information in the expression feature extraction stage, and cannot extract the highly discriminative expression feature, which leads to the low expression recognition rate, a method of facial expression recognition based on multi-scale feature fusion attention mechanism is proposed. First, ResNet50 is used to extract the convolutional feature. In order to avoid the loss of expression feature information, multi-scale feature fusion is performed on the output feature images of different convolution layers in the network, and more abundant expression feature information is extracted while introducing context information; In order to focus on the key expression features, an improved hybrid attention mechanism is introduced into the network to learn the weight information from channel and spatial domain, obtain the attention map, enhance the expression ability of features, and suppress the impact of redundant information; In order to further improve the discriminability of the extracted expression features, pyramid convolution is introduced to improve the residual network structure. The experimental results show that the average accuracy of the model on the RAF-DB dataset is 83.67%.
Spotlights: Faculty, Alumni, Course

An update on Mr. Edmand Pace, an introduction to Sarah Hodge-Wetherbe, and news about LIS 654: E-Resources Management.
IAMonSense: multi-level handwriting classification using spatiotemporal information

Online handwriting classification has become an open research problem as it serves as a preliminary step for handwriting recognition systems and applications in several other fields. This paper aims to extend the current trends and knowledge with multiple contributions in handwriting classification using spatiotemporal information. Firstly, it enriches the annotations of several publicly available online handwriting datasets, SenseThePen, IAM-onDB, and IAMonDo, for online handwriting classification and recognition tasks. The said datasets are updated with three distinguished levels of annotations, i.e., stroke, sequence, and line levels. The enriched annotations of these datasets extend their functionality for online handwriting classification at different levels for further research analysis. In addition to enrichment, it also unifies the annotation levels across the datasets, which enables the research community to benchmark proposed methods for comparative analysis using multiple datasets. All the datasets with enriched annotations are made publicly available for the research community as part of the IAMonSense dataset. Moreover, this paper presents a comprehensive benchmark of these datasets using multiple deep neural networks such as traditional convolutional neural networks (CNNs), graph convolutional networks(GCNs), attention-based neural networks, and transformers. These benchmarks can be used later on for further development in online handwriting classification.
Task context transformer and GCN for few-shot learning of cross-domain

Cross-Domain Few-Shot Learning (CD-FSL) to recognize new categories in a new domain with few samples has attracted significant attention. Recently, task-specific CD-FSL emerges as promising research for its great generalization. However, the existing task-specific CD-FSL methods are not robust enough in task context modeling and task-specific feature learning, especially under the cross-domain setting. To tackle this problem, a Task Context Transformer and Graph Convolutional Network (TCT-GCN) method for CD-FSL is proposed. The proposed TCT-GCN constructs three modules: 1) Multi-Level Feature Fusion, which fuses domain-shared low-level features into domain-unshared high-level features; 2) Transformer-based Task Context Encoder, which models sample order-independent task context; 3) Graph Convolutional Network-based Adaptive Feature Learner, which adaptively learns task-specific features. Experiments on eight CD-FSL datasets reveal the effectiveness of our TCT-GCN.
Cascade attentional fusion for unsupervised domain adaptation on multi-modal egocentric video analysis

Supervised learning models require a large-scale dataset for more effective model training. However, a large-scale dataset requires time-consuming data collection and highly complicated preprocessing, posing a significant challenge for researchers. Therefore, creating a large-scale dataset for each practical application is unachievable in a real-world setting. In this case, unsupervised domain adaptation is crucial for practical applications, as it can enable models to enhance their performance on unlabeled data by training on labeled data. For multi-modal egocentric video analysis, some models have used unsupervised domain adaptation and achieved outstanding performance. However, they use either early or late fusion, ignoring the correlation in and between multi-modal inputs. Therefore, this paper investigates different fusion architecture and proposes a cascade attentional fusion to improve the feature representation for unsupervised domain adaptation on multi-modal egocentric video analysis. First, we propose a cascade fusion architecture for increased audio signal reuse. Then, we propose a temporal-spatial attention mechanism for highlighting spatio-temporal feature representations. Third, we propose a novel cascade attentional fusion method for multi-modal egocentric video data fusion by incorporating the architecture and attention model described previously. In addition, we study the ways for integrating different attentions. Finally, we propose an adversarial domain alignment model that incorporates the proposed fusion for unsupervised domain adaptation on multi-modal egocentric video analysis, reaching state-of-the-art performance on the public dataset.
Understanding adversarial robustness on the hypersphere

Adversarial examples have raised public concern about the robustness of deep neural networks (DNNs). One universal approach to enhance the robustness is adversarial training which essentially augments the training data. However, adversarial training succeeded only to a very limited extent. This limited progress is partly due to the lack of interpretation and understanding of the robustness of DNNs. In this context, we try to explain the adversarial robustness by embedding the sample points onto a hypersphere, which naturally provides an interpretable metric for the distance between sample points. Different from the empirical intuition, it is observed that adversarial trained models show complex patterns when facing different datasets and training configurations. Observations and explanations about robustness and model behavior are made from the aspect of the distances between samples points. Lastly, we discuss the degradation of standard accuracy in the adversarial trained models and provide possible solutions.
Multiform stamp image pose correction algorithm based on perspective transformation

The combination of perspective transformation technology and Hough transformation can achieve good image correction effect. In the application process, in order to determine the perspective transformation parameters, for the rectangular seal, the four vertices of the seal can be selected as the reference points of the transformation. However, the above method can not be directly applied to oval and circular seals. Therefore, this paper puts forward a simple and effective solution. Firstly, the minimum circumscribed frames of circular and elliptical seals are made respectively, and the vertex position coordinates are calculated. Using the constructed external frame, the above problem is transformed into the correction of a rectangular or square image, so that the perspective transformation technology can be extended to the correction of other shapes such as ellipse and circle. At the same time, BM3D filtering is used to reduce the impact of noise. In the basic estimation, K-means clustering is used to search the matching block in the neighborhood of the reference block, and the generated weight matrix is used to improve the matching degree of the module and the accuracy of subsequent aggregation. The preliminary experimental results show that the idea proposed in this paper is simple and effective, and can realize the unified correction of seal image posture of different shapes.
Simulator Based mission optimization for swarm UAVs with Minimum Safety Distance Between Neighbors

The increasing prominence of Unmanned Aerial Vehicles (UAVs) in research has resulted in a heightened focus on their applications in both civilian and military sectors. As UAVs persist in playing an essential role in the future, understanding the minimum safety distances required for swarms during transportation between locations becomes crucial. Maintaining optimal safety distances is vital for preventing collisions and maximizing the efficient use of airspace. This paper investigates a Machine Learning (ML) approach to address this concern for fleets comprising ten or more swarm vehicles, employing a PX4/ROS/Gazebo-based digital simulation system framework to determine the minimum safety distance in various UAV transportation settings.
Innovative solutions of public libraries offered to business

Today, in order to remain viable and meet the needs and expectations of a global, entrepreneurial and learning society, public libraries must change and reinvent their roles in society, find new solutions for business, and actively engage in partnerships between education, business and local government. This paper presents the findings of qualitative research using an interview method and uncovers the possibilities of innovative services offered by public libraries to the business community. The qualitative analysis of the experiences of business representatives revealed that in order to become competitive organizations, public libraries need to focus not only on satisfying the needs of different communities, but also on providing innovative services to the business community by playing the roles of an information facilitator, an organizer of cultural and lifelong learning activities, a project partner and a developer of virtual social network.
Performance of Grover’s search algorithm with diagonalizable collective noises

Grover’s search algorithm (GSA) is known to experience a loss of its quadratic speedup when exposed to quantum noise. In this study, we partially agree with this result and present our findings. First, we examine different typical diagonalizable noises acting on the oracles in GSA and find that the success probability decreases and oscillates around 1/2 as the number of iterations increases. Secondly, our results show that the performance of GSA can be improved by certain types of noise, such as bit flip and bit-phase flip noise. Finally, we determine the noise threshold for bit-phase flip noise to achieve a desired success probability and demonstrate that GSA with bit-phase flip noise still outperforms its classical counterpart. These results suggest new avenues for research in noisy intermediate-scale quantum computing, such as evaluating the feasibility of quantum algorithms with noise and exploring their applications in machine learning.
Correlations Between Achievements in Mathematics, Student Characteristics, Classroom Climate and Self-Efficacy

This study examines a model for evaluating the influence of student characteristics and classroom climate on achievements in mathematics among Druze high-school students. The sample consisted of 400 students who were randomly chosen from all Druze high schools in northern Israel. The participants (202 girls and 198 boys) studied four-point (mid-level) mathematics for matriculation. Student characteristics examined include background (gender, number of siblings, and parents’ education), attitudes toward mathematics, and mathematical self-efficacy. Classroom climate during mathematics lessons was also examined. The extent to which the model and the variables’ direct and indirect effects influence mathematical achievements were examined. Results showed that mathematical self-efficacy had a positive effect on mathematical achievements. Students’ attitudes toward mathematics and the classroom climate were significantly positively associated with achievements in mathematics. Furthermore, it was observed that attitude toward mathematics significantly positively affected mathematical self-efficacy.
Rotorcraft takeoff analysis and classification to detect outlier operations that could present a safety risk

Various reports from entities such as the Federal Aviation Administration (FAA) and the National Transportation Safety Board (NTSB) have shown a recent increase in the number of incidents involving helicopters. The versatility of rotorcraft operations makes the establishment of safety metrics challenging. Yet, flight data monitoring (FDM) programs enable the implementation of data-based models and analyses that can contribute to improving the safety of helicopter operations. Traditionally FDM programs have featured exceedance-based data analyses by defining safety thresholds. However, recent advances in data science, and more particularly in deep learning techniques, have paved the way for a more reliable definition of safety thresholds via the use of outlier detection algorithms. This paper focuses on the implementation of an anomaly detection model for the takeoff phase which represents a large portion of incidents in rotorcraft operations. After generating training data and augmenting the dataset, the takeoff segment is extracted from each flight data record. Then, the type of takeoff performed is identified through a classification algorithm, and finally, a recurrent neural network composed of long short term memory cells is implemented to detect anomalies or outliers in the input takeoff data.
Joint Learning of Anchor Graph-Based Fuzzy Spectral Embedding and Fuzzy K-Means

As one of the classical clustering techniques, spectral embedding boasts extensive applicability across numerous domains. Traditional spectral embedding techniques entail the mapping of graph models to low-dimensional vector spaces (indicator vectors) to facilitate hard partitioning. However, data boundaries occasionally exhibit ambiguity, thereby constraining the utility of hard partitioning. In this article, we introduce an innovative spectral embedding method, namely, joint learning of anchor graph-based fuzzy spectral embedding model and fuzzy K-means (AFSEFK). Drawing inspiration from fuzzy logic, our method employs a membership vector in lieu of the conventional indicator vector for spectral embedding, amalgamating it with fuzzy K-means to concurrently optimize membership, thereby simultaneously learning the local and global structures inherent in the data. Moreover, to enhance the quality of similarity graphs and augment clustering performance, we implement the balanced K-means-based hierarchical K-means technique to generate representative anchors. Subsequently, an anchor-based similarity graph is devised through a parameter-free neighbor assignment strategy. Comprehensive extensive experimentation with synthetic and real-world datasets substantiates the efficacy of the AFSEFK algorithm.
Evolutionary combination of connected event schemas into meaningful plots

Abstract Many of the stories we are exposed to are built from small schemas of connected events involving a set of characters– boy meets girl leads to a relationship or crime leads to revenge . The present paper proposes an evolutionary solution to the task of putting together a story by combining a set of such schemas. This approach presents three challenges: how to mix up the elements in the different schemas, how to instantiate the characters across the schemas and how to tell acceptable combinations from the rest. The present paper applies an evolutionary solution that relies on a genetic representation for these combinations of schemas, and applies as fitness functions a set of metrics on compatibility constraints across schema combinations. Outputs of this procedure are evaluated by human judges in comparison with baseline solutions in which the values for genes are assigned at random. The proposed solution generates a population of story drafts that resemble plot descriptions for simple stories. The results of the comparative evaluation by human judges are positive. The genetic representation of pattern combinations and the metrics on compatibility across pattern pairs provide a valid evolutionary solution for constructing simple plots.
Perhitungan Kebutuhan Rak Penyimpanan Berkas Rekam Medis (BRM) 5 Tahun Kedepan Di Gedung Y Rumah Sakit X

The hospital is a health service institution that provides complete individual health services by providing inpatient, outpatient and emergency services. Plenary Health Services, namely health services that include promotive, preventive, curative, and rehabilitative. The availability of medical record file storage racks in health care facilities is very important in maintaining security and avoiding damage to patient data. The file rack is a place to store archives or medical record documents which aim to facilitate storage and retrieval of medical record documents in the filing room and maintain the confidentiality of medical record documents. The storage of medical record files can exceed the capacity due to the large number of new patient visits and the disproportionate file shelves. Planning or procuring storage racks based on the number of medical record documents stored, the shape of the racks and the size of the storage racks according to ergonomic standards, namely storage racks can be in the form of roll o'pack racks and taking into account the area of an available room. Calculation of the need for shelves is done by using the smallest quadrant calculation. Calculations are made based on the number of new patient visits in the previous 5 years, namely 2018 to 2022. Based on the calculation results, the results show that the need for shelves for the next 5 years is 3 shelves.