Reviewerly/exHarmony huggingface dataset:
Object Detection with YOLO Model on NAO Humanoid Robot

Object detection is an important part in the field of robotics as it enables robots to understand their surroundings. The NAO humanoid robot is extensively used in human-robot interaction research. In this study a novel approach combining the VGG16 network and You Only Look Once (YOLO) algorithm is used for object detection using the NAO robot. YOLOv7 is selected for its best balanced information retention, quick inference, accurate localization, and identification of objects as compared to several bounding box algorithms. VGG16 network is adopted as a feature extractor to optimize the performance of object detection for NAO low-resolution camera images. Once feature extraction is completed then it’s output layer is combined with our fine tuned YOLOv7 model for object detection. The fine-tuned YOLOv7 model is proposed with some pre-processing techniques such as image augmentation, angle movement, and scale resizing for the performance improvement. The efficiency of the proposed model is compared with the performance of other state-of-the-art models.
Quantum Image Teleportation Based on Probabilistic Bit-Encoding Strategy

Quantum teleportation of images has been a long-awaited and necessary operation in the domain of Quantum Information Processing. Various Quantum Image Representations have been proposed to suit the different requirements of Quantum Image processing, such as edge detection, inversion, color operation, geometric transformation, etc. To meet the Quantum Teleportation of an Image, the existing algorithm is not capable of teleporting the Quantum Image. Instead, the algorithm can only transport classical bits via quantum channels, making it a superdense coding algorithm rather than actual quantum teleportation. In this paper, we demonstrate a simulation of quantum image teleportation to overcome the challenge mentioned above. To do this, we have presented a new Quantum Image Representation technique so that Quantum Teleportation can be simulated using Quantum Simulator. This technique converts the classical image into a superposition state of $$\alpha $$ and $$\beta $$ components, generated using a probabilistic bit encoding strategy. The measurement was performed 50000 times on the superposition state to retrieve the classical image. To verify the correctness of this method, the proposed algorithm was tested using different parametric tests between the classical image and the images after measurement. The results indicate the correctness of the proposed method, paving the path for applications of Quantum Image Teleportation.
Elephant Swarm Water Search Algorithm (LESWSA) for Solving Constrained Optimization Problems: A List Based Randomized Approach

Elephant Swarm Water Search Algorithm (ESWSA) is a Swarm Intelligence-based metaheuristic algorithm that follows the water search strategy of groups of elephants. This paper has introduced LESWSA (List based Elephant Swarm Water Search Algorithm), newly version of ESWSA. It uses two lists ( $${P}_{list}$$ and $${G}_{list}$$ ) which enables the search process to explore search space in a more efficient way and make a balance between exploration and exploitation. These two lists store different local and global best solutions having equal cost. Moreover, a randomized move with the student’s t-distributed random numbers is used to enhance the exploration. The proposed variant has outperformed ESWSA on 23 benchmark functions out of 30 (CEC 2014). Results have been verified by comparing with the results obtained by existing meta-heuristic techniques like GSA, Firefly, CL-PSO, DE, ESWSA, and CM-ESWSA on CEC 2014 benchmark functions. It has also been used to find solutions to constrained optimization problems existing in real-world domains, and its performance is superior to that of other promising metaheuristics in most of the cases.
An Efficient Approach for Findings Document Similarity Using Optimized Word Mover’s Distance

We introduce Optimized Word Mover’s Distance (OWMD), a similarity function that compares two sentences based on their word embeddings. The method determines the degree of semantic similarity between two sentences considering their interdependent representations. Within a sentence, all the words may not be relevant for determining contextual similarity at the aspect level with another sentence. To account for this fact, we designed OWMD in two ways: first, it decreases system’s complexity by selecting words from the sentence pair according to a predefined set of dependency parsing criteria; Second, it applies the word mover’s distance (WMD) method to previously chosen words. When comparing the dissimilarity of two text sentences, the WMD method is used because it represents the minimal “journey time” required for the embedded words of one sentence to reach the embedded words of another sentence. Finally, adding an exponent function to the inverse of the OWMD dissimilarity score yields the resulting similarity score, called Optimized Word Mover’s Similarity (OWMS). Using STSb-Multi-MT dataset, the OWMS measure decreases MSE, RMSE, and MAD error rates by $$66.66\%$$ , $$40.70\%$$ , and $$37.93\%$$ respectively than previous approaches. Again, OWMS reduces MSE, RMSE, and MAD error rates on Semantic Textual Similarity (STS) dataset by $$85.71\%$$ , $$62.32\%$$ , and $$60.17\%$$ respectively. For STSb-Multi-MT and STS datasets, the suggested strategy reduces run-time complexity by $$33.54\%$$ and $$49.43\%$$ , respectively, compared to the best of existing approaches.
Predictive Modeling of Diseases with Explainable Artificial Intelligence Using LightGBM

The continuous exploration of the intricate connections among symptoms, patient attributes, and diseases within the intricate landscape of human health represents an ongoing pursuit. Data-driven methodologies have ushered in novel opportunities for comprehending these intricate relationships. Especially with the COVID-19 pandemic, the paradigms of disease understanding, diagnosis, and treatment management have assumed unprecedented significance. This study, powered by LightGBM and SHAP, has the potential to provide invaluable support to experts in decision support systems, early diagnosis of diseases, personalized treatment plan applications, strengthening medical interventions with case-oriented treatment predictions by producing advanced diagnosis and treatment strategies at demographic scales and analyzing risk factors, developing evidence-based public health policies and proactive health services, researchers. Furthermore, this research can be effectively leveraged in epidemiological investigations to ascertain the correlations and emerging trends between various diseases and the influencing health determinants all with an impressive 81% accuracy.
Improved Genetic Algorithm Based on Oscillation Factor to Solve the AUV Path Planning Problem

Abstract In this paper, the bi-objective AUV path planning problem considering the shortest path and the minimum path smoothness is studied. Firstly, a mathematical model of the problem is established and a two-dimensional Cartesian coordinate system model is used to model the AUV working environment. Then the dual objective is normalized and transformed into an adaptation function by dimensionality reduction. An improved Genetic Algorithm is proposed to solve the problem according to its characteristics. An initial solution generation strategy for the n-1 equipartition method based on the improved oscillation factor is designed, and an elite retention strategy with simulated annealing operation is introduced to improve the genetic strategy. To verify the effectiveness of the improved algorithm in this paper, simulation experiments were conducted by the Microsoft Visual Studio platform. After the comparison and analysis of the three algorithms, the algorithm proposed in this paper has shorter and smoother paths, faster convergence speed, and more obvious advantages in the quality of the solution.
Temporal-difference Adaptive Graph Convolutional Network for Skeleton-based Action Recognition

Abstract In action recognition based on the skeleton, graph convolutional networks (GCNs) have shown great superiority based on the design of making the skeleton in the video a spatiotemporal map and extracting features from the spatiotemporal map. However, the topology of the skeleton in GCN-based methods is pre-designed according to prior knowledge, which limits the capacity of the network to learn high-level topology about the skeleton. To improve this deficiency, we design a temporal-difference adaptive graph convolutional network (TDA-GCN) that can learn the potential topology of the human skeleton from the input data, which is augmented using the channel attention module. Experiments show that TDA-GCN achieves state-of-the-art performance on two large-scale skeleton datasets, NTU-RGBD and Kinetics-Skeleton.
Pelatihan Pemanfaatan Fitur Dasar Microsoft 365 Sebagai Media Pembelajaran

The use of Microsoft 365 as a learning media is very possible to increase students' understanding in implementing learning media carried out by teachers in the classroom, based on the basic features provided by Microsoft 365 which supports the learning process in the classroom and on assignments. Teachers at SMK Negeri 9 Jeneponto are still not able to make good use of Microsoft 365, such as Microsoft 365 features. It can be seen that there is a lot of interest in using Microsoft 365 but there is a lack of understanding so its use is not yet optimal. This community service activity is carried out by utilizing Microsoft Teams as one of the features in Microsoft 365 which can be used for virtual meets like this PKM implementation method. Problems experienced by partners such as those above can be overcome with PKM solutions in the form of efforts to increase teacher professionalism by providing training on the use of Microsoft 365 as a learning medium. Training on the use of Microsoft 365 as a learning medium in partner schools is considered appropriate so that teachers not only know how to use Microsoft 365, but also practice it.
The system of tasks for educational (introductory elementary mathematics) practice as a source of preparation of future teachers of mathematics to work in engineering classes

The article presents a system of tasks for educational (introductory elementary mathematics) practice. Its role in the preparation of future teachers of mathematics for the organization of teaching mathematics in engineering classes is substantiated. An example of an online course of practice support, designed on the basis of a system of tasks, is given.
A Novel Multi-view Fuzzy Clustering Algorithm Based on Fuzzy C-Means

Fuzzy c-means (i.e., FCM) is a representative clustering method that is widely used in machine learning and pattern recognition. It can describe the degree of fuzziness of objects to clusters using memberships, but it cannot assess the degree of fuzziness of the clustering results. Motivated by such issues, in this paper, we propose a multi-view fuzzy clustering method based on FCM and a fuzzy assessment is introduced to assess the fuzziness of clustering results. Specifically, we constructed a multi-view FCM to obtain a reasonable membership matrix by fully integrating multi-view information. Based on this, we propose a fuzzy assessment method to evaluate the fuzziness of the clustering results and mark the objects between the different clusters (i.e., the object has the feature of multiple clusters). The experimental result shows that the proposed multi-view fuzzy clustering method achieves better performance than several related clustering methods.
Dual Channel Graph Neural Network for Fraud Detection

Fraud detection based on graph neural networks has received wide attention in recent years. In fraud detection, there exists a class imbalance problem because the number of fraudulent nodes is often much smaller than the number of normal nodes. Besides, fraudulent nodes usually camouflage themselves feature-wise and structure-wise, making the fraudulent information hidden from the normal information. This also introduces inconsistency to graphs. There must be edges connecting nodes with different labels, which violates the homophily assumption of vanilla GNNs. To address the above problems, we propose a Dual-Channel Graph Neural Network (DCGNN) for fraud detection. Firstly, we use the Class-balanced Node Sample Module for sampling so that the model can better learn the patterns of minority class nodes. Then, we design the Attribute-Structure Dual-Channel Hybrid Module so that the model adaptively combines the representations of two channels for detection. A graph disparity convolutional network is also introduced to model the dissimilarity between nodes to solve the inconsistency problem. Experiment results on the benchmark datasets demonstrate the effectiveness of our model.
Nonlinear Manifold Learning via Graph Curvature

With the rapid increase of the data, not only the scale of data is very large, but also the dimensionality of the initial data is very high. It is increasingly difficult to perform feature extraction on high-dimensional datasets, called curse of dimensionality. Traditional manifold learning algorithms propose to explore the underlying local or global geometric structure of the high-dimensional datasets for feature extraction. However, such algorithms can only mine a set of local linear structures of the datasets, and it is extremely limited to uncover the higher-order non-Euclidean geometric structure. In this paper, we propose a new nonlinear manifold learning algorithm to exploit the curvature information of the dataset, which can quantify the spatial similarity between data points more accurately and improve the accuracy of feature extraction. We perform a group of clustering experiments on several types of image datasets, and the related experimental results show that our algorithm outperforms the existing manifold learning algorithms.
Operator-dependence of methods for obtaining metric characteristics of a face with real measurements and digital images of a face

Backgraund: there are no studies in the scientific literature that establish the operator-dependence of portrait identification with the manual method of measuring facial parameters in subjects in comparison with measurements from digital images. The labor costs and accuracy of measurements with these methods have not been established.&#x0D; Aims: to establish the operator-dependence of methods of real face measurement and measurement on digital images.&#x0D; Materials and methods: facial parameters were instrumentally measured in 24 Caucasian women aged 19-20 years old, who are studying at the prof. VF Voyno-Yasenetsky Krasnoyarsk State Medical University, four researchers independently of each other. The same type of standard digital photography of the face of each subject was taken in five projections, followed by a comparison of the results of real measurements with digital ones.&#x0D; Results: when comparing the results of measurements of the face obtained from digital images and those obtained by the manual method, an error was observed both in the measurements of one particular parameter by one researcher with two different methods, and excellent results in other researchers. But the greatest deviations were observed with the manual method of research, which may be due to the dependence of measurements on the "operator".&#x0D; Conclusions: with a streamlined and methodically verified approach to working with digital facial images, this technique is more accurate and less labor-intensive than with real measurements, due to the absence of operator-dependence, which can be used in the practice of investigating crimes.
Sampling Graphs without Forbidden Subgraphs and Unbalanced Expanders with Negligible Error

Suppose that you wish to sample a random graph over vertices and edges conditioned on the event that does not contain a “small” -size graph (e.g., clique) as a subgraph. Assuming that most such graphs are -free, the problem can be solved by a simple rejected-sampling algorithm (that tests for -cliques) with an expected running time of . Is it possible to solve the problem in a running time that does not grow polynomially with ? In this paper, we introduce the general problem of sampling a “random looking” graph with a given edge density that avoids some arbitrary predefined -size subgraph . As our main result, we show that the problem is solvable with respect to some specially crafted -wise independent distribution over graphs. That is, we design a sampling algorithm for -wise independent graphs that supports efficient testing for subgraph-freeness in time , where is a function of and the constant in the exponent is independent of . Our solution extends to the case where both and are -uniform hypergraphs. We use these algorithms to obtain the first probabilistic construction of constant-degree polynomially unbalanced expander graphs whose failure probability is negligible in (i.e., ). In particular, given constants , we output a bipartite graph that has left nodes and right nodes with right-degree of so that any right set of size at most expands by factor of . This result is extended to the setting of unique expansion as well. We observe that such a negligible-error construction can be employed in many useful settings and present applications in coding theory (batch codes and low-density parity-check codes), pseudorandomness (low-bias generators and randomness extractors), and cryptography. Notably, we show that our constructions yield a collection of polynomial-stretch locally computable cryptographic pseudorandom generators based on Goldreich’s one-wayness assumption resolving a central open problem in the area of parallel-time cryptography (e.g., Applebaum, Ishai, and Kushilevitz [SIAM J. Comput., 36 (2006), pp. 845–888] and Ishai et al. [Proceedings of the 40th Annual ACM Symposium on Theory of Computing, ACM, 2008, pp. 433–442]).
Innovation Data Sprint in Citizen Social Sciences

The concept of data sprint is tested and implemented in the citizen social science project "Exploring Society Together" (GINGER), to explore the issue of how to provide co-interpretation of big data sets with citizen scientists and what infrastructure is needed to design research in dialogue with citizens. Therefore we tested in GINGER the participatory research format of a public data sprint in which big data sets are analyzed and interpreted in cooperation between researchers and citizen scientists according to scientific standards. Public data sprints are innovative formats in the transitive field of science communication and citizen sciences. The innovation of public data sprints in citizen social science formats lies in the short and intensively research collaboration between citizens and scientists who explore a research topic and data set together within just multiple consecutive days. Each data print focuses on a specific topic, in this case digital social and political network analysis and polarization tendencies of politicians on X (formerly Twitter). The data and digital infrastructure in this discussed format is provided by „Exploration of Political Information Networks“ (EPINetz). With the support of extremely low-threshold applications to analyze data digitally, as by EPINetz, data sprints provide excellent opportunities for broadening perspectives in social science disciplines by having many people working and thinking in a concentrated way on one or more small topic-related problems or puzzles of social phenomena analyzed, based on big data information.
Incorporate Service Into Your Tailgate Events

Special Events GaloreVolume 23, Issue 12 p. 7-7 A New Twist to Tailgates Incorporate Service Into Your Tailgate Events Kerry Nenn, Kerry NennSearch for more papers by this author Kerry Nenn, Kerry NennSearch for more papers by this author First published: 14 November 2023 https://doi.org/10.1002/speg.32087Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume23, Issue12December 2023Pages 7-7 RelatedInformation
DHNB2023 Conference Proceedings

The DHNB2023 conference proceedings combine 25 full papers from the 7th Digital Humanities in the Nordic and Baltic Countries conference and an editorial summarising the event. The conference theme was Sustainability: Environment, Community, Data and it was jointly arranged by the University of Oslo Library, the University of Bergen Library, and The Greenhouse Center for Environmental Humanities at the University of Stavanger from March 8th to 10th, 2023.
Spiking Reinforcement Learning for Weakly-Supervised Anomaly Detection

Weakly-supervised Anomaly Detection (AD) has achieved significant performance improvement compared to unsupervised methods by harnessing very little additional labeling information. However, most existing methods ignore anomalies in unlabeled data by simply treating the whole unlabeled set as normal; that is, they fail to resist such noise that may considerably disturb the learning process, and more importantly, they cannot extract key anomaly features from these unlabeled anomalies, which are complementary to those labeled ones. To solve this problem, a spiking reinforcement learning framework for weakly-supervised AD is proposed, named ADSD. Compared with artificial neural networks, the spiking neural network can effectively resist input perturbations due to its unique coding methods and neuronal characteristics. From this point of view, by using spiking neurons with noise filtering and threshold adaptation, as well as a multi-weight evaluation method to discover the most suspicious anomalies in unlabeled data, ADSD achieves end-to-end optimization for the utilization of a few labeled anomaly data and rare unlabeled anomalies in complex environments. The agent in ADSD has robustness and adaptability when exploring potential anomalies in the unknown space. Extensive experiments show that our method ADSD significantly outperforms four popular baselines in various environments while maintaining good robustness and generalization performance.
CInvISP: Conditional Invertible Image Signal Processing Pipeline

Standard RGB (sRGB) images processed by the image signal processing (ISP) pipeline of digital cameras have a nonlinear relationship with the scene irradiance. Therefore, the low-level vision tasks which work best in a linear color space are not suitable to be carried out in the sRGB color space. To address this issue, this paper proposes an approach called CInvISP to provide a bidirectional mapping between the nonlinear sRGB and linear CIE XYZ color spaces. To ensure a fully invertible ISP, the basic building blocks in our framework adopt the structure of invertible neural network. As camera-style information is embedded in sRGB images, it is necessary to completely remove it during backward mapping, and properly incorporate it during forward mapping. To this end, a conditional vector is extracted from the sRGB input and inserted into each invertible building block. Experiments show that compared to other mapping approaches, CInvISP achieves a more accurate bidirectional mapping between the two color spaces. Moreover, it is also verified that such a precise bidirectional mapping facilitates low-level vision tasks including image denoising and retouching well.