Reviewerly/exHarmony huggingface dataset:
A training method for face representation models in realistic scenarios

Face recognition has been widely used in daily life, but the existing model systems use processed high-quality datasets in training, while the face pictures in real scenes usually contain the influence of blurring, lighting, obscuring and other factors, thus making the existing face recognition models cannot perform well, and secondly, the existing face datasets have less data of Asian descent, resulting in the distribution learned by the models with the actual application. There is a certain error in the actual application. We propose a method to train face recognition models for realistic scenes by image augment of local face data to improve the classification accuracy of the models for low-quality images, and we demonstrate the feasibility of our method through experiments. Our method improves 0.619% and 0.414% in classifying images with added illumination and added random squares, respectively, compared to the current state-of-the-art methods.
An optimized cloud edge server placement method based on k-mean algorithm

In cloud-based edge computing systems, edge server placement is crucial to many applications response time. Generally, edge server placement is always modeled as a multi-objective optimization problem and solved with integer programming algorithms. However, these algorithms are not well scalable, and parameters used in these algorithms depend heavily on experiences. In this paper, we propose an optimized k-mean based edge server placement method. We use k-mean to cluster system sources and application loads and sort the application load to apply the most applicable server to the application. Experimental studies over synthetic data sets validate effectiveness of the method.
What’s changing from normal aging to pathological aging during semantic memory retrieval: insights from EEG oscillations (S8.006)

<h3>Objective:</h3> To examine differences in EEG oscillations between normal cognitive aging versus pathological aging during semantic memory retrieval. <h3>Background:</h3> Semantic memory remains relatively stable with normal cognitive aging and its decline might be an indicator of a neurodegenerative disease. Studying EEG correlates of semantic retrieval could provide early markers of neurodegeneration. <h3>Design/Methods:</h3> Twenty-nine young adults (YA: 21.3 years), 22 healthy aging adults (HA: 63.9 years), and 20 patients with mild cognitive impairment (MCI: 67.8 years) participated. We recorded EEG while one completed a semantic task in which one was asked to judge whether two words led to retrieval of an object (retrieval, R) or not (non-retrieval, NR). Using Morlet wavelet time-frequency decomposition, event-related power changes within theta (4–8Hz), alpha (8–12Hz), low-beta (12–20 Hz), and high-beta (20–30 Hz) EEG frequency bands were measured. Power data contrasting R vs NR trials were analyzed separately to compare (1) YA versus HA (normal aging effects) and (2) HA versus MCI (pathological aging effects) using independent t-tests (permutations N = 2000; significance level at FDR of 0.05). <h3>Results:</h3> For normal aging effects, though no behavioral differences between the normal groups, we found theta (NR &gt; R, 628–701 ms) and alpha (R &lt; NR, 555–591 ms) power differences only in YA, and a high-beta power difference (NR &gt; R, 444–481 ms) only in HA. For pathological aging effects, accuracy was reduced in MCI. Two EEG differences were found: a different pattern of high-beta power differences (224–261 ms) in MCI (NR &gt; R) than HA (R &gt; NR), and a low-beta power difference (R &gt; NR, 444–481 ms) only in HA. <h3>Conclusions:</h3> Brain responses transition from slower (alpha, theta) to faster (beta) activity with aging during semantic memory retrieval. Retrieval accuracy declines and patterns of neural oscillation change with MCI. The findings have implications for early detection of pathological aging. <b>Disclosure:</b> The institution of Dr. Chiang has received research support from NIH/NIDCD. The institution of Dr. Chiang has received research support from Texas Alzheimer’s Research and Care Consortium. Ms. Lydon has nothing to disclose. The institution of Dr. Hart has received research support from Department of Defense. Dr. Kraut has nothing to disclose. Raksha Mudar has nothing to disclose.
Static and dynamic validation of kinect for ergonomic postural analysis using electro-goniometers as a gold standard:A preliminary study

BACKGROUND: Evaluation of the working postures and development of new techniques are paramount in reducing the awkward postures and occurrence of musculoskeletal disorders (MSDs). The Kinect sensor, a portable and cost-effective device, appears to be a promising alternative to study work postures. OBJECTIVE: The current study aimed to evaluate the validity of Kinect against the gold-standard instrument (electro-goniometers) for body joint angle measurements. METHODS: A unique software application was developed to measure the critical body joint angles for postural evaluation by using the Kinect’s skeletal tracking feature. The body joint angle data of ten volunteers were measured simultaneously by both Kinect and electro-goniometers. The validation analysis was conducted in both static and dynamic domains of application. RESULTS: Minimal variation was observed between the two techniques, and the Kinect correlated well for upper-arm joint angles of 45∘, 60∘ and 90∘; lower-arm joint angles of 30∘, 45∘, 60∘, and 90∘; straight neck position, neck joint angle at maximum possible flexion; straight trunk position, trunk bend angle at full flexion. In dynamic analysis, four out of five ICC values were &gt; 0.75 except for the upper arm. Discrepancies in the results indicated the disapproval of Kinect for only wrist measurements. CONCLUSION: The results of the static and dynamic studies gave a sufficient basis to consider the Kinect tool as an alternative to contemporary posture-based ergonomic evaluation methods.
MagicCubePose, A more comprehensive 6D pose estimation network

Most of the current mainstream 6D pose estimation methods use template or voting-based methods. Such methods are usually multi-stage or have multiple assumptions and post-correction, which will cause a certain degree of information redundancy and increase the computational cost, their real-time detection performance is poor. We point out that traditional path aggregation networks introduce new errors, therefore, we propose a loss function: MagicCubeLoss, a portable module: MagicCubeNet, and the corresponding 6D pose estimation model: MagicCubePose. MagicCubePose has good expansion performance and can build more efficient models for different calculation power and scenarios. Experiments show that our model has good real-time detection performance and the highest ADD(-S) accuracy.
Performance Art Video Action Management Oriented to 6G Wireless Transmission Technology

With the development of 5G communication networks, the development prospects, capability requirements and key technologies of the next-generation communication system 6G have become new hotspots. This article expands the coverage of the communication system through 6G, realizes the wireless transmission technology, builds the ideal of an intelligent mobile society everywhere, and analyzes the development trend of 6G technology, the challenges it faces, and the possible wireless transmission technology. Wireless transmission technology is widely used in various fields of production and life. Its various technical requirements and terminal energy limitations restrict the application and development of communications. This article focuses on the high-efficiency optimization scheme that combines 6G wireless transmission technology to improve the utilization efficiency of network resources. In addition, this thesis also discusses video action management in public performance art. Based on public performance art, the research on video action recognition can be summarized into three levels from simple to complex according to the content of the action, namely mobile vision, action vision and action vision. Video action vision is currently being studied. In the recognition process, most of the action frame information in the video is repetitive or has a low correlation with action recognition, which not only increases the complexity of calculation, but also affects the accuracy of action recognition. In the process of feature selection and understanding, the main methods include characterization fusion and research based on gesture features. Based on the introduction of 6G wireless transmission technology, this article introduces the technological development of 6G network, which promotes the rapid development of public performance art video action management.
Detection of Breast Cancer Using Deep Learning Techniques

Evaluation of Histopathology images are a vital approach that is used for the breast cancer detection. To build up the efficiency of breast cancer detection and to reduce the burden of doctors and specialists, we layout various Deep Learning algorithms to recognize most cancers with the usage of histopathology scans. This paper follows several deep learning models like Convolutional Neural network (CNN) and Vgg16 for the recognition method. The dataset we used for class manner is Breast Histopathology Images which contain positive and negative images. We examined breast Histopathology images of 2,77,524 patients of which 198,748 images are IDC (-) and 78,786 images are IDC (+). This shows the deep learning algorithms can greatly facilitate the breast cancer detection, improving the accuracy and speed of detection. One of the most common cancers is Invasive Ductal Carcinoma (IDC). To determine the aggressiveness score to whole-mount specimen, doctors typically focus on areas containing IDC. Therefore, one of the common pre-processing steps for automatic aggressive categorization is to identify the exact region of IDC along the mounting side.
Instance-based adaptive attention algorithm for human-object interaction detection

Aiming at the problem of unclear human-object interaction behavior objects in complex background, we propose an Instance-Based Adaptive Attention (IBAA) algorithm. The algorithm adaptively generates a series of attention matrices according to the objects and subjects of human-object interaction instances. These attention matrices are then used to update the feature map to reduce the interference of the complex background. In order to enrich the human object interaction information, we use the graphical model to represent the interactions between human and objects and use the graph convolutional neural network to update it. Experimental results on HICO-DET dataset show that the proposed algorithm has significantly improved accuracy and multi-scale object detection ability compared with other human object interaction algorithms.
Single-stage face detection method based on feature fusion and data enhancement

The depth face detector based on an anchor frame has demonstrated good performance with the advancement of science and technology, but tiny faces and partially occluded faces are still difficult to identify under the effect of posture, lighting, occlusion, and other factors. We present the ESSFD, an enhanced single-stage face detector, to increase the robustness of face detection in complex environments. The following are the important points: (1) Enhancing feature extraction by using an optimized ResNet-D network in the backbone network and adding Atrous Convolution Layer supplementary feature pyramids; (2) For data augmentation, use a combination of Gaussian blur and color jitter to lessen the impact of image environmental elements and increase the model’s robustness; (3) Fine-tune the training parameters for the model. In the presence of unusual postures, complicated lighting, and partial occlusion, ESSFD can increase the detection accuracy of tiny faces when compared to RetinaFace. Experiments reveal that in the easy and medium stages of the WIDER FACE dataset, the Average Accuracy (AP) of the ESSFD detector is around 1% higher than that of the advanced RetinaFace face detector (AP=95.373%). In the hard tiny face detection stage, it is around 2% higher than RetinaFace.
Few-shot relation extraction based on multi-level feature metric learning

Relationship extraction is an important task in natural language processing and knowledge mapping. Traditional entity relationship extraction methods have achieved high accuracy in practical applications. However, when faced with the task of entity relationship extraction that is not easy to obtain large-scale supervision training data sets, traditional methods cannot get good results. In this paper, a few-shot relation extraction method based on multi-level feature metric learning is proposed. This method takes the prototype network as the baseline network to generate a class prototype. Firstly, a multi-level feature extraction module is proposed. This module combines the multi-level features of the text with the multi-level attention mechanism, which can fully extract the features of the text. Secondly, a loss function based on label value and negative sample distance is proposed. This algorithm introduces an evaluation mechanism of negative sample distance on the basis of the prototype network, so that the model can adaptively allocate parameters and improve the clustering ability of small samples. Experiments are conducted on FewRel1.0 which is a small sample relational data set. Experiment results show that compared with other models, our model can improve classification accuracy.
Research on the construction and application of event based electromagnetic space big data knowledge graph

In view of the large volume and complex structure of electromagnetic space big data, it is difficult to store and retrieve spectrum data using traditional databases and knowledge graph. Due to the abstractness and space-time characteristics of electromagnetic spectrum data, the use of event forms can better represent the spectrum data, and also make people and machines better understand. Based on the knowledge graph and the concept of events, this paper constructs the spectrum event knowledge graph (EMS-DEKG) and compares several methods of spectrum data retrieval through experiments, which shows that the EMS-DEKG method improves the stability and timeliness of electromagnetic space big data storage and retrieval.
Gesture recognition fusion two-stream 3D CNN and FPN

In the direction of VR/AR human–machine interaction, natural and simple dynamic gesture recognition research has attracted much attention. For the sake of improve the accuracy of dynamic gesture recognition in Human–Machine Interaction, this paper proposes a new dynamic gesture recognition method FPN-3DResNeXt, which combines two-stream three-dimensional convolutional neural network (3DResNeXt) and feature pyramid (FPN). This method improves the structure of the 3DResNeXt network, adds feature pyramid and attention channel, optimizes the model parameters, and then improves the recognition accuracy; for the sake of improve the convergence speed and stability of the model, it is proposed to add batch normalization (BN) Further optimization of the network reduces the training time. The experimental results show that the dynamic gesture recognition rate of the method proposed in this paper is 95.30%, which is 2.1% higher than that of the gesture recognition method based on 3DResNeXt by comparing with various 3D convolution methods on the EgoGesture dataset, and it has better stability.
Review of CiteSpace-Based Research on Artificial Intelligence for Teaching Management and Evaluation

The development of artificial intelligence has always been a hotly debated topic, especially the use of artificial intelligence in the education industry has become more and more widespread. The bibliometric method and visual analysis method were adopted, and the tool used was CiteSpace 6.1.R6 (literature visual analysis software). The core journal literature and master’s and doctoral dissertations on the use of artificial intelligence in teaching management and evaluation in the Web of Science core database from 2004 to 2022 were selected as the objects of analysis. We analyzed the knowledge map by clustering keywords, co-occurrence of keywords, authors, research institutions and countries, and sorted out the hot spots, main ideas and future research trends of AI in teaching management and evaluation at home and abroad. Based on the literature and knowledge mapping analysis, it is found that the research hotspots are mainly distributed in the field of medical teaching, deep learning, Internet of Things, big data, facial recognition, knowledge base and personalized learning, which also provides a reference basis and development direction for the use of AI in teaching management and evaluation in China.
Special core tensors of multi-qubit states and the concurrency of three lines

In this work, we propose a computationally simple approach to identify the local unitary (LU) entanglement classes of multi-qubit states by higher-order singular value decomposition (HOSVD). For multipartite states, HOSVD simultaneously diagonalizes their one-body reduced density matrices (RDM) by LU actions. Therefore, the zeros of the all-orthogonality conditions due to HOSVD, also known as the core tensors, are the pure-state representations of such simultaneously diagonalized one-body RDM for a given multipartite state. By using the concurrency of three lines, we simplified the calculations and coarse-grained the classification into a finite number of families of states based on the square of their first n-mode singular values, $$\sigma _1^{(n)2}$$ . These special core tensors are genuinely entangled by default. For three and four qubits, we identified two and four families of states respectively. A generalization of the algorithm to multi-qubit states is provided.
Multi-interest Recommendation on Shopping for Others

Existing recommendation methods based on multi-interest frameworks effectively model users from multiple aspects to represent complex user interests. However, more research still needs to be done on the behavior of users shopping for others. We propose a Multi-Demander Recommendation (MDR) model to learn different people’s interests from a sequence of actions. We first decouple the feature embeddings of items to learn the static preferences of different demanders. Next, a weighted directed global graph is constructed to model the associations among item categories. We partition short sequences by time intervals and look up category embeddings from the graph to capture dynamic intents. Finally, preferences and intentions are combined with learning the interests of different demanders. The conducted experiments demonstrate that our model improves the accuracy of recommendations.
A Deep Behavior Path Matching Network for Click-Through Rate Prediction

User behaviors on an e-commerce app not only contain different kinds of feedback on items but also sometimes imply the cognitive clue of the user’s decision-making. For understanding the psychological procedure behind user decisions, we present the behavior path and propose to match the user’s current behavior path with historical behavior paths to predict user behaviors on the app. Further, we design a deep neural network for behavior path matching and solve three difficulties in modeling behavior paths: sparsity, noise interference, and accurate matching of behavior paths. In particular, we leverage contrastive learning to augment user behavior paths, provide behavior path self-activation to alleviate the effect of noise, and adopt a two-level matching mechanism to identify the most appropriate candidate. Our model shows excellent performance on two real-world datasets, outperforming state-of-the-art CTR models. Moreover, our model has been deployed on the Meituan food delivery platform and has accumulated 1.6% improvement in CTR and 1.8% improvement in advertising revenue.
3rd International Workshop on Scientific Knowledge Representation, Discovery, and Assessment (Sci-K 2023)

The International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2023) is now running its third edition. The Sci-K workshop is a venue that brings together researchers and practitioners from different disciplines (including, but not limited to, Digital Libraries, Information Extraction, Machine Learning, Semantic Web, Knowledge Engineering, Natural Language Processing, Scholarly Communication, Science of Science, Scientometrics and Bibliometrics), as well as professionals from the industry, to explore innovative solutions and ideas for the production and consumption of Scientific Knowledge Graphs and assessing the research impact. The workshop has called for high-quality submissions around the three main themes of research, related to scientific knowledge: representation, discovery, and assessment.
New ideas of precision teaching development based on the background of big data

Abstract Based on the accurate application of precision teaching can enable students to conduct efficient learning and achieve the educational goal of all-round development. In this paper, a big data precision teaching model is constructed to stage the teaching process, set the data corresponding to each stage as random variables, and apply the AIC criterion to reflect the complexity of the model. Then the evaluation model of the precision teaching algorithm is constructed by calculating the test set error based on cross-validation to find the precision teaching solution under random variables. In the last stage, it is known from the observation data of the students that the optimal decision function is derived from the variable selection, according to which the targeted development ideas of precision teaching are obtained. The experimental results showed that the practice subjects were divided into control and experimental groups, follow-up tests were conducted, and the percentage of failures in the control group was 4%, while the percentage of failures in the experimental group was 9%. The scores of the experimental class were higher when analyzed by the comprehensive attainment degree. It shows that the development and application of the platform under precision teaching can not only make up for some problems and shortcomings of the traditional classroom but also enable students to get better learning results in this precision teaching process.
Evaluation Mechanism of Teaching Staff’s Building in Local Vocational Colleges Based on Big Data Technology

Abstract With the improvement of information technology, all industries have developed rapidly. However, the education industry is lagging behind in the process of informationization. Most still follow the educational paradigm of the past, where teachers rely on personal experience to make judgments and teaching decisions for students, just like a blind man feeling like an elephant. In this paper, we design a teaching quality monitoring and evaluation strategy according to the K-modes algorithm in big data technology, which is a dynamic data collection and intelligent analysis based on the learning process and learning effectiveness, ensuring data authenticity and also realizing multi-dimensional data collection and multi-angle evaluation and analysis, and finally comparing each index of three different algorithms. It is verified that the minimum error sum of squares of the optimized K-modes algorithm is 711, and the correct rate is 0.97, while the values of this metric for the other two algorithms are 1587 and 986, and the correct rates are 0.91 and 0.92, respectively. Therefore, the designed mechanism is a system with a superior evaluation effect and a feasible prediction model for faculty development in local vocational colleges.