Reviewerly/exHarmony huggingface dataset:
A Novel Deep Multifeature Extraction Framework Based on Attention Mechanism Using Wearable Sensor Data for Human Activity Recognition

Human activity recognition (HAR) utilizing wearable sensors is a prominent topic in academia and has been widely used in health monitoring, medical treatment, motion analysis, and other fields. Although HAR technology based on deep learning has made some progress, there are still some problems, such as incomplete feature extraction and low utilization rate of features, which may result in false recognition. To settle the problems, we propose a novel deep multifeature extraction framework based on attention mechanism (DMEFAM), which includes the temporal attention feature extraction layer (TAFEL), the channel and spatial attention feature extraction layer (CSAFEL), and the output layer. The TAFEL is comprised of bidirectional gated recurrent unit (Bi-GRU) and self-attention (SA) mechanism, and the CSAFEL is comprised of the convolutional block attention module (CBAM) and the residual network-18 (ResNet-18). In this framework, the combination of deep learning neural networks, SA mechanism, and CBAM assigns different important degrees to features, increases the diversities of feature extraction, and improves the accuracy of HAR. To improve the practicability of the proposed framework, a daily and aggressive activity dataset (DAAD) is collected by our laboratory for performance evaluation. The experiments are conducted on the wireless sensor data mining laboratory (WISDM) dataset, the University of California Irvine-HAR (UCI-HAR) dataset, and the collected DAAD dataset. The results show that the proposed DMEFAM can upgrade the recognition performance effectually compared to other advanced HAR frameworks, with the recognition accuracies of 97.9%, 96.0%, and 99.2% on the WISDM dataset, the UCI-HAR dataset, and the DAAD dataset, respectively.
A novel encryption algorithm using multiple semifield S-boxes based on permutation of symmetric group

Abstract There is a need of secure and robust encryption algorithm that can be implemented on each and diverse software and hardware platforms. In this paper, we investigate semifield substitution boxes using permutation of symmetric group on a set of size 8 $$S_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mn>8</mml:mn> </mml:msub> </mml:math> and establish an effective procedure for generating $$S_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mn>8</mml:mn> </mml:msub> </mml:math> semifield substitution boxes having same algebraic properties. Further, the strength analysis of the generated substitution boxes is carried out using the well-known standards, namely bijectivity, non-linearity, strict avalanche criterion, bit independence criterion, XOR table and differential invariant. As application, an encryption algorithm is proposed that can be employed to strengthen any kind of secure communication. The presented algorithm is mainly based on the Shannon idea of substitution–permutation network where the process of substitution is performed by the proposed $$S_8$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>S</mml:mi> <mml:mn>8</mml:mn> </mml:msub> </mml:math> semifield substitution boxes and permutation operation is performed by the binary cyclic shift of substitution box transformed data. In addition, the proposed encryption algorithm utilizes two different chaotic maps. To ensure the appropriate utilization of these chaotic maps, we carry out in-depth analyses of their behavior in the context of secure communication and apply the pseudo-random sequences of chaotic maps in the proposed image encryption algorithm accordingly.
Global Shared Learning Classroom Model: A Pedagogical Strategy for Sustainable Competencies Development in Higher Education

One of the most popular strategies to develop skills such as collaborative work, critical thinking, and problem-solving is the application of COIL (Collaborative Online International Learning) through a Global Classroom in which Professors from at least two universities from different countries and cultures develop a period known as "Global Classroom" in which, through the Challenge Based Learning didactic technique, they solve a real-life challenge but from a distance, using digital communication tools. In this study, 4-week global courses were held between groups from the Tecnológico de Monterrey in Mexico and groups from the UNIMINUTO University in Colombia. The challenges developed were related to two fundamental issues: 1) Management of natural resources and climate change and 2) Biomimetics. Students were able to develop skills to communicate effectively through online interaction with people from different cultures and disciplines and use technological tools that facilitate communication and distance learning in multicultural virtual environments. The use of Global Classroom in their respective areas of study is made available to those interested as a valuable tool to provide students with the opportunity to live sustainable international experiences and promote the Sustainable Development Goals. Current teaching models involve active and experiential learning and developing soft and hard skills. The Global Classroom experience is a tool that allowed continuity in the preparation of students during the COVID-19 pandemic.
SR-PEKS: Subversion-Resistant Public Key Encryption With Keyword Search

Public key encryption with keyword search (PEKS) provides secure searchable data encryption in cloud storage. Users can outsource encrypted data and keywords to a cloud server, and search target one without disclosing sensitive information. To achieve resistance against off-line keyword guessing attacks, existing practical PEKS schemes employ independent key server(s) to assist users in producing keywords to be encrypted (called server-derived keywords) in an online manner. In this article, we analyze server-aided PEKS schemes and reveal a potential threat: vulnerability against subversion attacks, where algorithms in server-aided PEKS might be maliciously implemented to undermine security. In a subverted encryption implementation, a subliminal channel is established to control randomness generation such that biased ciphertexts covertly leak plaintext information. We further present a specific subversion attack against generation of server-derived keywords to violate keywords’ confidentiality. To address these issues, we propose SR-PEKS, a subversion-resistant PEKS scheme based on cryptographic reverse firewalls (CRF). In SR-PEKS, CRF sanitizes messages transmitted in server-derived keyword generation to resist the presented subversion attack. CRF also participates in a collaborative randomness generation protocol to yield unbiased randomness for encryption, thereby eliminating the subliminal channel. Provable security and high efficiency of SR-PEKS are demonstrated by comprehensive analyses and performance evaluations.
Multi-view unsupervised feature selection with tensor robust principal component analysis and consensus graph learning

Recently, multi-view unsupervised feature selection has attracted much attention due to its efficiency and better interpretability in processing high-dimensional multi-view datasets. Most existing methods rely on the constructed similarity matrices to obtain reliable pseudo labels to guide the feature selection. However, the considerable adverse noise in the raw data inevitably impedes the exploration of true underlying similarity structures. Besides, the inter-view correlations are often ignored during the common representation learning, which limits the effective fusion of the essential information from multiple views. To solve these issues, we design a novel robust multi-view unsupervised feature selection framework. Specifically, our method seeks a set of noise-free view-specific similarity matrices by leveraging tensor robust principal component analysis, where the high-order connections among different views are well exploited through the constructed low-rank tensor. Meanwhile, a high-quality consensus similarity matrix is adaptively learned from the view-specific representations within the same unified framework to capture the shared local structures. To enhance the discriminative ability of the feature selection matrix, we further impose a rank constraint on the consensus similarity matrix to obtain reliable pseudo cluster indicators. We present an efficient optimization algorithm ground on the alternating direction method of multipliers to solve the proposed model. Experimental results on six multi-view datasets confirm the superiority of our method.
A Medically Assisted Model for Precise Segmentation of Osteosarcoma Nuclei on Pathological Images

Osteosarcoma is the most common malignant bone tumor with a high degree of malignancy and misdiagnosis rates. Pathological images are crucial for its diagnosis. However, underdeveloped regions currently lack sufficient high-level pathologists, leading to uncertain diagnostic accuracy and efficiency. Existing research on pathological image segmentation often neglects the differences in staining styles and lack of data, without considering medical backgrounds. To alleviate the difficulty in diagnosing osteosarcoma in underdeveloped areas, an intelligent assisted diagnosis and treatment scheme for osteosarcoma pathological images, ENMViT, is proposed. ENMViT utilizes KIN to achieve normalization of mismatched images with limited GPU resources and uses traditional data enhancement methods, such as cleaning, cropping, mosaic, Laplacian sharpening, and other techniques to alleviate the issue of insufficient data. A multi-path semantic segmentation network combining Transformer and CNN is used to segment images, and the degree of edge offset in the spatial domain is introduced into the loss function. Finally, noise is filtered according to the size of the connecting domain. This article experimented on more than 2000 osteosarcoma pathological images from Central South University. The experimental results demonstrate that this scheme performs well in each stage of the osteosarcoma pathological image processing, and the segmentation results' IoU index is 9.4% higher than the comparative models, demonstrating its significant value in the medical industry.
A Comprehensive Survey of Deep Learning Techniques Natural Language Processing

In NLP research, unsupervised or semi-supervised learning techniques are increasingly getting more attention. These learning techniques are capable of learning from data that has not been manually annotated with the necessary answers or by combining non-annotated and annotated data. This essay presents a survey of various natural language processing methods. The discipline of natural language processing, which integrates linguistics, artificial intelligence, and computer science, was established to make it easier for computers and human language to communicate with one another. It is, as we can say, relevant psychopathology for the study of computer-human interaction. The understanding of natural language, which entails enabling machines to naturally interpret human language, is one of the many challenges this area faces. Discourse analysis, morphological separation, machine translation, production and understanding of NLP, part-of-speech tagging, recognition of optical characters, speech recognition, and sentiment analysis are some of the most frequent NLP tasks. As opposed to learning, which is supervised and typically yields few correct results for a given amount of input data, this job is typically quite difficult. However, there is a sizable amount of data available that is unannotated in nature, i.e. the entire contents are available on the internet, and it typically yields less accurate findings.&#x0D;
#RoeOverturned: Twitter Dataset on the Abortion Rights Controversy

On June 24, 2022, the United States Supreme Court overturned landmark rulings made in its 1973 verdict in Roe v. Wade. The justices by way of a majority vote in Dobbs v. Jackson Women's Health Organization, decided that abortion wasn't a constitutional right and returned the issue of abortion to the elected representatives. This decision triggered multiple protests and debates across the US, especially in the context of the midterm elections in November 2022. Given that many citizens use social media platforms to express their views and mobilize for collective action, and given that online debate provides tangible effects on public opinion, political participation, news media coverage, and the political decision-making, it is crucial to understand online discussions surrounding this topic. Toward this end, we present the first large-scale Twitter dataset collected on the abortion rights debate in the United States. We present a set of 74M tweets systematically collected over the course of one year from January 1, 2022 to January 6, 2023.
Real-Time Vehicle Detection from UAV Aerial Images Based on Improved YOLOv5

Aerial vehicle detection has significant applications in aerial surveillance and traffic control. The pictures captured by the UAV are characterized by many tiny objects and vehicles obscuring each other, significantly increasing the detection challenge. In the research of detecting vehicles in aerial images, there is a widespread problem of missed and false detections. Therefore, we customize a model based on YOLOv5 to be more suitable for detecting vehicles in aerial images. Firstly, we add one additional prediction head to detect smaller-scale objects. Furthermore, to keep the original features involved in the training process of the model, we introduce a Bidirectional Feature Pyramid Network (BiFPN) to fuse the feature information from various scales. Lastly, Soft-NMS (soft non-maximum suppression) is employed as a prediction frame filtering method, alleviating the missed detection due to the close alignment of vehicles. The experimental findings on the self-made dataset in this research indicate that compared with YOLOv5s, the mAP@0.5 and mAP@0.5:0.95 of YOLOv5-VTO increase by 3.7% and 4.7%, respectively, and the two indexes of accuracy and recall are also improved.
Cross-View Geo-Localization via Learning Disentangled Geometric Layout Correspondence

Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks, in which the training and testing data are captured from two different regions. We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set. In this paper, we propose GeoDTR which explicitly disentangles geometric information from raw features and learns the spatial correlations among visual features from aerial and ground pairs with a novel geometric layout extractor module. This module generates a set of geometric layout descriptors, modulating the raw features and producing high-quality latent representations. In addition, we elaborate on two categories of data augmentations, (i) Layout simulation, which varies the spatial configuration while keeping the low-level details intact. (ii) Semantic augmentation, which alters the low-level details and encourages the model to capture spatial configurations. These augmentations help to improve the performance of the cross-view geo-localization models, especially on the cross-area benchmarks. Moreover, we propose a counterfactual-based learning process to benefit the geometric layout extractor in exploring spatial information. Extensive experiments show that GeoDTR not only achieves state-of-the-art results but also significantly boosts the performance on same-area and cross-area benchmarks. Our code can be found at https://gitlab.com/vail-uvm/geodtr.
Secure medical image transmission using deep neural network in e‐health applications

Abstract Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high‐level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi‐final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.
When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the difficulty of encoding a wealth of world knowledge in their parameters.This paper aims to understand LMs' strengths and limitations in memorizing factual knowledge, by conducting large-scale knowledge probing experiments on two open-domain entity-centric QA datasets: POPQA, our new dataset with 14k questions about long-tail entities, and EntityQuestions, a widely used opendomain QA dataset.We find that LMs struggle with less popular factual knowledge, and that retrieval augmentation helps significantly in these cases.Scaling, on the other hand, mainly improves memorization of popular knowledge, and fails to appreciably improve memorization of factual knowledge in the long tail.Based on those findings, we devise a new method for retrieval augmentation that improves performance and reduces inference costs by only retrieving non-parametric memories when necessary. 1
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis

To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison. A good review needs to be knowledgeable, namely that the comments should be constructive and informative to help improve the paper; and explainable by providing detailed evidence. ReviewRobot achieves these goals via three steps: (1) We perform domain-specific Information Extraction to construct a knowledge graph (KG) from the target paper under review, a related work KG from the papers cited by the target paper, and a background KG from a large collection of previous papers in the domain. (2) By comparing these three KGs, we predict a review score and detailed structured knowledge as evidence for each review category. (3) We carefully select and generalize human review sentences into templates, and apply these templates to transform the review scores and evidence into natural language comments. Experimental results show that our review score predictor reaches 71.4%-100% accuracy. Human assessment by domain experts shows that 41.7%-70.5% of the comments generated by ReviewRobot are valid and constructive, and better than human-written ones for 20% of the time. Thus, ReviewRobot can serve as an assistant for paper reviewers, program chairs and authors.
Heuristic Search and Receding-Horizon Planning in Complex Spacecraft Orbit Domains

Spacecraft missions to small celestial bodies face sensitive, strongly non-Keplerian dynamics that motivate the employment of automated sampling-based trajectory planning. However, the scarcity of onboard computing resources necessitates careful formulation of heuristics for efficiently searching the reachable sets, which exhibit complex and finely-detailed structure. We examine a global search heuristic that combines aspects of simulated annealing and hill-climbing to locate sparse regions of the planning domain that simultaneously satisfy numerous geometric and timing constraints associated with remote sensing objectives for points of interest on the central body surface. Subsequently, we demonstrate the use of a receding-horizon implementation of this maneuver-planning strategy to produce mission profiles that fulfill sets of such goals.
Learning to Learn: Algorithmic Inspirations from Human Problem Solving

We harness the ability of people to perceive and interact with visual patterns in order to enhance the performance of a machine learning method. We show how we can collect evidence about how people optimize the parameters of an ensemble classification system using a tool that provides a visualization of misclassification costs. Then, we use these observations about human attempts to minimize cost in order to extend the performance of a state-of-the-art ensemble classification system. The study highlights opportunities for learning from evidence collected about human problem solving to refine and extend automated learning and inference.
Visual Saliency Estimation through Manifold Learning

Saliency detection has been a desirable way for robotic vision to find the most noticeable objects in a scene. In this paper, a robust manifold-based saliency estimation method has been developed to help capture the most salient objects in front of robotic eyes, namely cameras. In the proposed approach, an image is considered as a manifold of visual signals (stimuli) spreading over a connected grid, and local visual stimuli are compared against the global image variation to model the visual saliency. With this model, manifold learning is then applied to minimize the local variation while keeping the global contrast, and turns the RGB image into a multi-channel image. After the projection through manifold learning, histogram-based contrast is then computed for saliency modeling of all channels of the projected images, and mutual information is introduced to evaluate each single-channel saliency map against prior knowledge to provide cues for the fusion of multiple channels. In the last step, the fusion procedure combines all single-channel saliency maps according to their mutual information score, and generates the final saliency map. In our experiment, the proposed method is evaluated using one of the largest publicly available image datasets. The experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art unsupervised saliency detection methods, yielding higher precision and better recall rates. Furthermore, the proposed method is tested on a video-type test dataset where a moving camera is trying to catch up with the walking person---a salient object in the video sequence. Our experimental results show that the proposed approach can successful accomplish this task, revealing its potential use for similar robotic applications.