Reviewerly/exHarmony huggingface dataset:
Attribute-based Proxy Re-encryption with Direct Revocation Mechanism for Data Sharing in Clouds

Cloud computing, which provides adequate storage and computation capability, has been a prevalent information infrastructure. Secure data sharing is a basic demand when data was outsourced to a cloud server. Attribute-based proxy re-encryption has been a promising approach that allows secure encrypted data sharing on clouds. With attribute-based proxy re-encryption, a delegator can designate a set of shared users through issuing a re-encryption key which will be used by the cloud server to transform the delegator’s encrypted data to the shared users’. However, the existing attribute-based proxy re-encryption schemes lack a mechanism of revoking users from the sharing set which is critical for data sharing systems. Therefore, in this paper, we propose a concrete attribute-based proxy re-encryption with direct revocation mechanism (ABPRE-DR) for encrypted data sharing that enables the cloud server to directly revoke users from the original sharing set involved in the re-encryption key. We implemented the new schemes and evaluated its performance. The experimental results show that the proposed ABPRE-DR scheme is efficient and practical.
Single-Object Detection from Video Streaming

Recent advances in the discovery of face masks have made object detection a hot topic for research. In the computer vision field, the object detection is the basic but a very intricate task. With its ability to extract powerful features, it is essential for the accretion to detect an object. Deep learning experts have learned their own areas of expertise in the discipline during the previous several years, and it was successful after that. The methods that can be used and the circumstances that determine whether a face has a mask on it or not provide unique and complex problems like real-time detection, changing climate, and complex lighting conditions. In this research work, the primary focus is on single-object detection in video, which aims to confirm whether the person wears a mask or not by using an object detection method. To contribute to public health, the aim of this chapter is to explore a more accurate and real-time approach that can better visualize a non-masked face in public which has become an important task in helping the international community. To detect its efficiency, diverse algorithms, methods, and techniques are proposed by researchers across the world, but YOLOv4 stands out from all the different gifts nowadays. Custom datasets are used to understand face masks for detection and monitoring. It really works for evaluating skill model, accuracy, memory, and accuracy converted to computation and compares the ground truth bounding box to the detected box and returns an accuracy rating of 91.12% in the long run. The parameters contained in each bounding box are the center coordinates of the box (tx, ty), the width (tw) and height (th) of the target, the confidence score (P0) that the box has a target, and the probability (Pc) that the target in the array of belongs to each object type. Anchor boxes and edge regression are used to preset boundaries to help determine the heights and widths of common targets. When making predictions, targeting a target in possible locations, these preset boundaries are then fine-tuned by panning and scaling, resulting in a fine-tuned window closer to the ground truth. Therefore, the direct selection of the size of the anchor box affects the accuracy and speed of the model. The higher the accuracy score could be, the better version is there for the detection of items.
FREDDY’S WEIGHT AND NEWTON’S LAWS

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Paul Hewitt; FREDDY’S WEIGHT AND NEWTON’S LAWS. Phys. Teach. 1 October 2023; 61 (7): 547. https://doi.org/10.1119/10.0021080 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Multi-object Detection: A Social Distancing Monitoring System

Disease transmission can occur in a variety of ways, but the most unexpected way is through air transmission. There are very few diseases that can spread through air. In December 2019, the COVID-19 spread across the world. So, this chapter mainly focuses on monitoring the people who maintain social distancing from the video using pre-trained YOLOv4 algorithm. YOLOv4 algorithm is useful to detect multi-objects from the real-time videos. So, using deep learning algorithm, the chance of spreading the virus and air disease will be reduced. In this chapter, YOLOv4 algorithm is utilized that detects multiple humans and calculates the distance between two objects, and if two humans are violating the rules of social distancing as per the government rules, then the alert will be triggered. In addition, predefined YOLOv4 models are compared with proposed techniques on parameters like confidence score with YOLOv3 model for better accuracy.
Lightweight image steganalysis with block-wise pruning

Abstract Image steganalysis is the task of detecting a secret message hidden in an image. Deep steganalysis using end-to-end deep learning has been successful in recent years, but previous studies focused on improving detection performance rather than designing a lightweight model for practical applications. This caused a deep steganalysis model to be heavy and computationally costly, making the model infeasible to deploy in real-world applications. To address this issue, we study an effective model design strategy for lightweight image steganalysis. Considering the domain-specific characteristics of steganalysis, we propose a simple yet effective block removal strategy that progressively removes a sequence of blocks from deep classification networks. This method involves the gradual removal of convolutional neural network blocks, starting from deeper ones. By doing so, the number of parameters and FLOPs are decreased without compromising the detection performance. Experimental results show that our removal strategy makes the EfficientNet-B0 variants 9.58 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> smaller and has 2.16 $$\times$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mo>×</mml:mo> </mml:math> fewer FLOPs than the baseline while retaining detection accuracy of 90.73% and 82.40% that are on par with the baseline on BOSSBase and ALASKA#2 datasets, respectively. Backed by our in-depth analyses, the results indicate that only a few early layers are sufficient for effective image steganalysis.
Semantic guidance network for video captioning

Abstract video captioning is a more challenging task that aims to generate abundant natural language descriptions, and it has become a promising direction for artificial intelligence. However, most existing methods are prone to ignore the problems of visual information redundancy and scene information omission due to the limitation of the sampling strategies. To address this problem, a semantic guidance network for video captioning is proposed. More specifically, a novel scene frame sampling strategy is first proposed to select key scene frames. Then, the vision transformer encoder is applied to learn visual and semantic information with a global view, alleviating information loss of modeling long-range dependencies caused in the encoder’s hidden layer. Finally, a non-parametric metric learning module is introduced to calculate the similarity value between the ground truth and the prediction result, and the model is optimized in an end-to-end manner. Experiments on the benchmark MSR-VTT and MSVD datasets show that the proposed method can effectively improve the description accuracy and generalization ability.
Cluster-based Dual-branch Contrastive Learning for unsupervised domain adaptation person re-identification

Unsupervised domain adaptation (UDA) person re-identification (Re-ID) is to enhance the discriminability of Re-ID tasks in the target domain by leveraging labeled source domain data and unlabeled target domain data. Employing clustering as a mean to generate pseudo labels has emerged as a successful approach for addressing UDA Re-ID tasks. However, clustering may be disturbed by data noise when generating pseudo labels, resulting in unstable quality of pseudo labels. In addition, persons with similar colored clothing can also affect the recognition accuracy of the model. To address these aforementioned issues in UDA Re-ID, we propose a Cluster-based Dual-branch Contrastive Learning (CDCL) approach. In CDCL, we design partially grayed images as input and utilize two-branch network structure to reduce the effect of upper-body clothing color on model recognition. Based on the two-branch network architecture, we additionally propose a cluster refinement method to improve the confidence of pseudo labels, which selects samples with high similarity, i.e., samples with weighted distance between subclass samples less than a threshold, and thus makes pseudo labels more reliable. The proposed CDCL has shown its effectiveness through experimental results, achieving an mAP accuracy of 81.5%.
Graph Attention Networks and Track Management for Multiple Object Tracking

Multiple object tracking (MOT) constitutes a critical research area within the field of computer vision. The creation of robust and efficient systems, which can approximate the mechanisms of human vision, is essential to enhance the efficacy of multiple object-tracking techniques. However, obstacles such as repetitive target appearances and frequent occlusions cause considerable inaccuracies or omissions in detection. Following the updating of these inaccurate observations into the tracklet, the effectiveness of the tracking model, employing appearance features, declines significantly. This paper introduces a novel method of multiple object tracking, employing graph attention networks and track management (GATM). Utilizing a graph attention network, an attention mechanism is employed to capture the relationships of nodes within the graph as well as node-to-node correlations across graphs. This mechanism allows selective focus on the features of advantageous nodes and enhances discriminability between node features, subsequently improving the performance and robustness of multiple object tracking. Simultaneously, we categorize distinct tracklet states and introduce an efficient track management method, which employs varying processing techniques for tracklets in diverse states. This method can manage occluded tracks in crowded scenes and improves tracking accuracy. Experiments conducted on three challenging public datasets (MOT16, MOT17, and MOT20) demonstrate that our method could deliver competitive performance.
RobustMat: Neural Diffusion for Street Landmark Patch Matching Under Challenging Environments

For autonomous vehicles (AVs), visual perception techniques based on sensors like cameras play crucial roles in information acquisition and processing. In various computer perception tasks for AVs, it may be helpful to match landmark patches taken by an onboard camera with other landmark patches captured at a different time or saved in a street scene image database. To perform matching under challenging driving environments caused by changing seasons, weather, and illumination, we utilize the spatial neighborhood information of each patch. We propose an approach, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RobustMat</i> , which derives its robustness to perturbations from neural differential equations. A convolutional neural ODE diffusion module is used to learn the feature representation for the landmark patches. A graph neural PDE diffusion module then aggregates information from neighboring landmark patches in the street scene. Finally, feature similarity learning outputs the final matching score. Our approach is evaluated on several street scene datasets and demonstrated to achieve state-of-the-art matching results under environmental perturbations.
Cluster-Induced Mask Transformers for Effective Opportunistic Gastric Cancer Screening on Non-contrast CT Scans

Gastric cancer is the third leading cause of cancer-related mortality worldwide, but no guideline-recommended screening test exists. Existing methods can be invasive, expensive, and lack sensitivity to identify early-stage gastric cancer. In this study, we explore the feasibility of using a deep learning approach on non-contrast CT scans for gastric cancer detection. We propose a novel cluster-induced Mask Transformer that jointly segments the tumor and classifies abnormality in a multi-task manner. Our model incorporates learnable clusters that encode the texture and shape prototypes of gastric cancer, utilizing self- and cross-attention to interact with convolutional features. In our experiments, the proposed method achieves a sensitivity of 85.0% and specificity of 92.6% for detecting gastric tumors on a hold-out test set consisting of 100 patients with cancer and 148 normal. In comparison, two radiologists have an average sensitivity of 73.5% and specificity of 84.3%. We also obtain a specificity of 97.7% on an external test set with 903 normal cases. Our approach performs comparably to established state-of-the-art gastric cancer screening tools like blood testing and endoscopy, while also being more sensitive in detecting early-stage cancer. This demonstrates the potential of our approach as a novel, non-invasive, low-cost, and accurate method for opportunistic gastric cancer screening.
Elliptic curve matrices over group ring to improve elliptic curve–discrete logarithm cryptosystems

An elliptic curve matrix (ECM) is created randomly based on an elliptic curve group to modify the elliptic curve-discrete logarithm (EC-DL) cryptosystems, which are elliptic Diffie-Hellman key exchange (DHKE) and elliptic ElGamal public key cryptosystem (EEPKC), and to increase the security level in comparison with the original EC-DL schemes. In proposed schemes, the keys and ciphertext are computed using the ECMs. The security of trust schemes depended on the difficulty of solving the elliptic curve discrete logarithm problem (ECM-DLP). New experimental results on proposed schemes are discussed. The ECM-DL schemes consider new insights for more secure communications.
PERANCANGAN DAN IMPLEMENTASI APLIKASI SISTEM INFORMASI DOKUMENTASI DAN PELAPORAN DOKUMEN BORANG AKREDITASI PROGRAM STUDI PADA UNIVERSITAS MEDAN AREA PROGRAM PKM DIYA 2019

Medan Area University (UMA) is one of the oldest private universities in northern Sumatra, which was founded by Haji Agus Salim Siregar in 1983. Especially at Medan Area University the document collection processing model for LKPS-APS is currently still not organized with neat and there is still a loss of LKPS-APS documents, especially when the deadline for LKPS-APS preparation is nearing the upload period. To improve performance in document processing, a system will be built to facilitate lecturers and staff in managing LKPS-APS documents properly and in detail, and reduce the reason lecturers or staff cannot attend meetings in the processing and collection of LKPS-APS document forms that can be submitted into the online system and online discussion per document in the LKPS-APS information system according to 9 standard criteria according to the Instrument Akreditasi Program Studi 4.0 (IAPS 4.0) for Study Program Accreditation.
The optimized Diffie-Hellman key exchange using the graphical method

In this paper, we describe a new contribution that uses the optimization problem to create a new version of the Diffie-Hellman key exchange (DHKE). The proposed version depended on the optimized discrete logarithm functions (ODLFs) that are given as a new definition in this work. A graphical method considers a fundamental tool to compute a shared secret key (SSK) in the proposed optimized Diffie-Hellman key exchange (ODHKE). The ODHKE is a more secure protocol for computing the SSK in comparison with the original DHKE.
Python DB-API

The standard for accessing a database in Python is the Python DB-API. This specifies a set of standard interfaces for modules that wish to allow Python to access a specific database. This chapter introduces the Python DB-API.
User Authentication Mechanisms Based on Immersive Technologies: A Systematic Review

Immersive technologies are revolutionary technological advancements that offer users unparalleled experiences of immersion in a virtual or mixed world of virtual and real elements. In such technology, user privacy, security, and anonymity are paramount, as users often share private and sensitive information. Therefore, user authentication is a critical requirement in these environments. This paper presents a systematic literature review of recently published research papers on immersive technology-based user authentication mechanisms. After conducting the literature search in September 2023 using Scopus, the selection process identified 36 research publications that were further analyzed. The analysis revealed three major types of authentications related to immersive technologies, consistent with previous works: knowledge-based, biometric, and multi-factor methods. The reviewed papers are categorized according to these groups, and the methods used are scrutinized. To the best of our knowledge, this systematic literature review is the first that provides a comprehensive consolidation of immersive technologies for user authentication in virtual, augmented, and mixed reality.
MC-DC: An MLP-CNN Based Dual-path Complementary Network for Medical Image Segmentation

Fusing the CNN and Transformer in the encoder has recently achieved outstanding performance in medical image segmentation. However, two obvious limitations require addressing: (1) The utilization of Transformer leads to heavy parameters, and its intricate structure demands ample data and resources for training, and (2) most previous research had predominantly focused on enhancing the performance of the feature encoder, with little emphasis placed on the design of the feature decoder. To this end, we propose a novel MLP-CNN based dual-path complementary (MC-DC) network for medical image segmentation, which replaces the complex Transformer with a cost-effective Multi-Layer Perceptron (MLP). Specifically, a dual-path complementary (DPC) module is designed to effectively fuse multi-level features from MLP and CNN. To respectively reconstruct global and local information, the dual-path decoder is proposed which is mainly composed of cross-scale global feature fusion (CS-GF) module and cross-scale local feature fusion (CS-LF) module. Moreover, we leverage a simple and efficient segmentation mask feature fusion (SMFF) module to merge the segmentation outcomes generated by the dual-path decoder. Comprehensive experiments were performed on three typical medical image segmentation tasks. For skin lesions segmentation, our MC-DC network achieved 91.69% Dice and 9.52mm ASSD on the ISIC2018 dataset. In addition, the 91.6% Dice and 94.4% Dice were respectively obtained on the Kvasir-SEG dataset and CVC-ClinicDB dataset for polyp segmentation. Moreover, we also conducted experiments on the private COVID-DS36 dataset for lung lesion segmentation. Our MC-DC has achieved 87.6% [87.1%, 88.1%], and 92.3% [91.8%, 92.7%] on ground-glass opacity, interstitial infiltration, and lung consolidation, respectively. The experimental results indicate that the proposed MC-DC network exhibits exceptional generalization capability and surpasses other state-of-the-art methods in higher results and lower computational complexity.
Elemental distribution patterns in rock samples from Egypt using neutron activation and complementary X-ray fluorescence analyses

The study utilized instrumental neutron activation analysis (INAA) and X-ray fluorescence (XRF) to accurately analyze the elemental composition of 28 felsite (rhyolite), rock samples. Statistical approaches, including bivariate and multivariate analysis, were employed to characterize the rocks and determine their origin. Major findings include significantly high levels of silicon (297000 ± 4000) mg/kg and low levels were noticed for gold (0.10 ± 0.01) mg/kg. The dominant major elements in the rocks were ranked as follows: silicon > aluminum > potassium > sodium > zirconium > calcium > zinc > manganese. A comparison with the upper continental crust (UCC) revealed higher levels for most elements, except for a few. The study also identified substantial amounts of uranium and thorium. Variations in elemental composition were observed both between different profiles and within felsite (rhyolite) rock samples, indicating heterogeneity and varying origins of the rocks. The findings contribute valuable baseline data for the area and highlight its economic significance for Egypt. Additionally, the study addresses the integration of results from different analytical methods, providing a comprehensive answer to this issue.
Counterfactual Explainable Conversational Recommendation

Conversational Recommender Systems (CRSs) fundamentally differ from traditional recommender systems by interacting with users in a conversational session to accurately predict their current preferences and provide personalized recommendations. Although current CRSs have achieved favorable recommendation performance, the explainability is still in its infancy stage. Most of the CRSs tend to provide coarse explanations and fail to explore the impact of minimal alterations on the recommendation decisions on items. In this paper, we are the first to incorporate the counterfactual techniques into CRS and propose a Counterfactual Explainable Conversational Recommender (CECR) to enhance the recommendation model from a counterfactual perspective. Counterfactual explanations can offer fine-grained reasons to explain users' real-time intentions, meanwhile generating counterfactual samples for augmenting the training dataset to enhance recommendation performance. Specifically, CECR adaptively learns users' preferences based on the conversation context and effectively responds to users' real-time feedback during multiple rounds of conversation. Furthermore, CECR actively generates counterfactual samples to augment the training set and thus leading to a constant improvement in recommendation performance. Empirical experiments carried out on three benchmark datasets show that our CECR outperforms state-of-the-art CRSs in terms of recommendation performance and explainability