Reviewerly/exHarmony huggingface dataset:
Decentralized Gradient-Quantization Based Matrix Factorization for Fast Privacy-Preserving Point-of-Interest Recommendation

With the rapidly growing of location-based social networks, point-of-interest (POI) recommendation has been attracting tremendous attentions. Previous works for POI recommendation usually use matrix factorization (MF)-based methods, which achieve promising performance. However, existing MF-based methods suffer from two critical limitations: (1) Privacy issues: all users’ sensitive data are collected to the centralized server which may leak on either the server side or during transmission. (2) Poor resource utilization and training efficiency: training on centralized server with potentially huge low-rank matrices is computational inefficient. In this paper, we propose a novel decentralized gradient-quantization based matrix factorization (DGMF) framework to address the above limitations in POI recommendation. Compared with the centralized MF methods which store all sensitive data and low-rank matrices during model training, DGMF treats each user’s device (e.g., phone) as an independent learner and keeps the sensitive data on each user’s end. Furthermore, a privacy-preserving and communication-efficient mechanism with gradient-quantization technique is presented to train the proposed model, which aims to handle the privacy problem and reduces the communication cost in the decentralized setting. Theoretical guarantees of the proposed algorithm and experimental studies on real-world datasets demonstrate the effectiveness of the proposed algorithm.
A Comprehensive Study on Blockchain: Transforming the World

We are going towards digitization, and while discussing it, “money” is the most common word that comes to everyone’s mind. We have “Bitcoins” to support this. The Blockchain, a shared ledger system, allows for the trading of Bitcoin and other types of digital money. In essence, bitcoins are long sequences of code that have been transformed into currency. Digital money that is created and held electronically is called bitcoin. Bitcoin transfers employ a shared ledger system called a blockchain. It is also being used in a number of other fields, including the government, the healthcare industry, and e-voting systems. These days, people are incredibly concerned about transaction security. Due to its comprehensive security characteristics, blockchain networks are increasingly being used. Throughout the world, people are embracing the blockchain network since it has complete security features. In our work, we have also covered some of the unique features of blockchain besides security. Every invention, including blockchains, must overcome numerous difficulties, as we all very well know. Some of the obstacles that the adoption of blockchain technology faces have been briefly discussed. We have covered the fundamentals of current blockchain technology, as well as its applications, features, and difficulties.
Classification method for imbalanced LiDAR point cloud based on stack autoencoder

&lt;abstract&gt;&lt;p&gt;The existing classification methods of LiDAR point cloud are almost based on the assumption that each class is balanced, without considering the imbalanced class problem. Moreover, from the perspective of data volume, the LiDAR point cloud classification should be a typical big data classification problem. Therefore, by studying the existing deep network structure and imbalanced sampling methods, this paper proposes an oversampling method based on stack autoencoder. The method realizes automatic generation of synthetic samples by learning the distribution characteristics of the positive class, which solves the problem of imbalance training data well. It only takes the geometric coordinates and intensity information of the point clouds as the input layer and does not need feature construction or fusion, which reduces the computational complexity. This paper also discusses the influence of sampling number, oversampling method and classifier on the classification results, and evaluates the performance from three aspects: true positive rate, positive predictive value and accuracy. The results show that the oversampling method based on stack autoencoder is suitable for imbalanced LiDAR point cloud classification, and has a good ability to improve the effect of positive class. If it is combined with optimized classifier, the classification performance of imbalanced point cloud is greatly improved.&lt;/p&gt;&lt;/abstract&gt;
Formal Models of Memory Based on Temporally-Varying Representations

The idea that memory behavior relies on a gradually changing internal state has a long history in mathematical psychology. This chapter traces this line of thought from statistical learning theory in the 1950s, through distributed memory models in the latter part of the twentieth century and early part of the twenty-first century through to modern models based on a scale-invariant temporal history. We discuss the neural phenomena consistent with this form of representation and sketch the kinds of cognitive models that can be constructed and connections with formal models of various memory tasks.
Fovea-UNet: Detection and Segmentation of Lymph Node Metastases in Colorectal Cancers with Deep Learning

Abstract Objective: Colorectal cancer is one of the most serious malignant tumors, and lymph node metastasis (LNM) from colorectal cancer is a major factor for patient management and prognosis. Accurate image detection of LNM is an important task to help pathologists diagnose cancer. However, effective image detection with the whole slide image (WSI) can only be performed by patch-based classification method, which are far from enough for cancer region segmentation and location due to a small patch image has less non-local contextual information. Recently, the U-Net architecture has been widely used to segment image to accomplish more precise cancer diagnosis. In this work, we aggregate the detailed and non-local contextual information into a U-Net baseline to segment the important region with high diagnostic value. Method: Inspired by the working principle of Fovea in visual neuroscience, a novel network framework based on U-Net for cancer segmentation named Fovea-UNet is proposed to adaptively adjust the resolution according to the importance-aware of information and selectively focuses on the region most relevant to colorectal LNM. Specifically, we design an effective adaptively optimized pooling operation called Fovea Pooling (FP), which dynamically aggregate the detailed and non-local contextual information according to pixel-level feature importance. In addition, the improved lightweight backbone network based on GhostNet is adopted to reduce the computational cost caused by FP pooling. Results &amp; Conclusions: Experimental results show that our proposed framework can achieve higher performance than other state-of-the-art segmentation networks with 92.82% sensitivity and 88.51% F1 score on the LNM dataset. Clinical impact: The proposed framework can provide a valid tool for cancer diagnosis, especially for LNM of colorectal cancer.
Security Risk Assessment of Blockchain-Based Patient Health Record Systems

Blockchain technology is receiving greater attention for enhancing the security of patient records systems; however, it is not a panacea, as many security risks have been found in these healthcare applications. This study conducts a state-of-the-art analysis of emerging risks in blockchain-based patient health record systems, their severity level, impact, and the corresponding countermeasures against them. In addition, we conclude our observations and indicate how blockchain security vulnerabilities may develop in the future. This study aims to promote more research on blockchain security challenges by offering researchers insights into future security and privacy developments in blockchain-based patient health record systems.
The application of genetic algorithm and fuzzy evaluation method in Teachers' performance appraisal

Research on the application of genetic algorithm and fuzzy evaluation method in Teachers' performance appraisal? Genetic algorithm (GA) is a search technique that uses heuristic rules to solve problems. It has been widely used in artificial intelligence, engineering, economics, finance and medicine. GA also evaluates teacher performance evaluation by using fuzzy logic system to evaluate teacher performance. In this research work, we developed a GA to find the best teacher from all available candidates according to the test results and evaluate them using a fuzzy logic system. This is based on the concept that teachers evaluate students' academic performance. We propose an automatic system that uses fuzzy logic as a tool to evaluate teachers at different stages of the educational process through multiple measurement methods such as scores, examination scores, and final examination scores.
FlexBoard: A Flexible Breadboard for Interaction Prototyping on Curved and Deformable Surfaces

We present FlexBoard, an interaction prototyping platform that enables rapid prototyping with interactive components such as sensors, actuators and displays on curved and deformable objects. FlexBoard offers the rapid prototyping capabilities of traditional breadboards but is also flexible to conform to different shapes and materials. FlexBoard's bendability is enabled by replacing the rigid body of a breadboard with a flexible living hinge that holds the metal strips from a traditional breadboard while maintaining the standard pin spacing. In addition, FlexBoards are also shape-customizable as they can be cut to a specific length and joined together to form larger prototyping areas. We discuss FlexBoard's mechanical design and present a technical evaluation of its bendability, adhesion to curved and deformable surfaces, and holding force of electronic components. Finally, we show the usefulness of FlexBoard through 3 application scenarios with interactive textiles, curved tangible user interfaces, and VR.
Bursaries Reimagined: Addressing Digital Inequity through a Library-Led, University-Wide Laptop Bursary Program

The rapid switch to online learning in early 2020 exacerbated problems students were already having with obtaining and maintaining up-to-date devices and a reliable internet connection. MacEwan University Library began offering 4-month term laptop loans at the beginning of the pandemic, but it was clear this was not fully meeting student needs. In response to conversations with faculty and students, the library secured funds from the university’s Student Technology Fee to launch a laptop bursary pilot in Winter 2022, which in turn expanded to a university-wide bursary in Fall 2022. This article discusses why an in-kind laptop bursary was the right approach at the right time in this setting; how this initiative contributes to equity and accessibility; and finally, perceptions of the value of this work, its fit within the scope of the library, and how the unique position of the library as a student-focused service and academic unit positioned it well to successfully offer this bursary. Challenges and opportunities for improvement are also discussed.
Data-Driven Solution to Identify Sentiments from Online Drug Reviews

With the proliferation of the internet, social networking sites have become a primary source of user-generated content, including vast amounts of information about medications, diagnoses, treatments, and disorders. Comments on previously used medicines, contained within these data, can be leveraged to identify crucial adverse drug reactions, and machine learning (ML) approaches such as sentiment analysis (SA) can be employed to derive valuable insights. However, given the sheer volume of comments, it is often impractical for consumers to manually review all of them before determining a purchase decision. Therefore, drug assessments can serve as a valuable source of medical information for both healthcare professionals and the general public, aiding in decision making and improving public monitoring systems by revealing collective experiences. Nonetheless, the unstructured and linguistic nature of the comments poses a significant challenge for effective categorization, with previous studies having utilized machine and deep learning (DL) algorithms to address this challenge. Despite both approaches showing promising results, DL classifiers outperformed ML classifiers in previous studies. Therefore, the objective of our study was to improve upon earlier research by applying SA to medication reviews and training five ML algorithms on two distinct feature extractions and four DL classifiers on two different word-embedding approaches to obtain higher categorization scores. Our findings indicated that the random forest trained on the count vectorizer outperformed all other ML algorithms, achieving an accuracy and F1 score of 96.65% and 96.42%, respectively. Furthermore, the bidirectional LSTM (Bi-LSTM) model trained on GloVe embedding resulted in an even better accuracy and F1 score, reaching 97.40% and 97.42%, respectively. Hence, by utilizing appropriate natural language processing and ML algorithms, we were able to achieve superior results compared to earlier studies.
Weakly-Supervised 3D Medical Image Segmentation Using Geometric Prior and Contrastive Similarity

Medical image segmentation is almost the most important pre-processing procedure in computer-aided diagnosis but is also a very challenging task due to the complex shapes of segments and various artifacts caused by medical imaging, (i.e., low-contrast tissues, and non-homogenous textures). In this paper, we propose a simple yet effective segmentation framework that incorporates the geometric prior and contrastive similarity into the weakly-supervised segmentation framework in a loss-based fashion. The proposed geometric prior built on point cloud provides meticulous geometry to the weakly-supervised segmentation proposal, which serves as better supervision than the inherent property of the bounding-box annotation (i.e., height and width). Furthermore, we propose the contrastive similarity to encourage organ pixels to gather around in the contrastive embedding space, which helps better distinguish low-contrast tissues. The proposed contrastive embedding space can make up for the poor representation of the conventionally-used gray space. Extensive experiments are conducted to verify the effectiveness and the robustness of the proposed weakly-supervised segmentation framework. The proposed framework are superior to state-of-the-art weakly-supervised methods on the following publicly accessible datasets: LiTS 2017 Challenge, KiTS 2021 Challenge and LPBA40. We also dissect our method and evaluate the performance of each component.
Finding Nemo’s Giant Cousin: Keypoint Matching for Robust Re-Identification of Giant Sunfish

The Giant Sunfish (Mola alexandrini) has unique patterns on its body, which allow for individual identification. By continuously gathering and matching images, it is possible to monitor and track individuals across location and time. However, matching images manually is a tedious and time-consuming task. To automate the process, we propose a pipeline based on finding and matching keypoints between image pairs. We evaluate our pipeline with four different keypoint descriptors, namely ORB, SIFT, RootSIFT, and SuperPoint, and demonstrate that the number of matching keypoints between a pair of images is a strong indicator for the likelihood that they contain the same individual. The best results are obtained with RootSIFT, which achieves an mAP of 75.91% on our test dataset (TinyMola+) without training or fine-tuning any parts of the pipeline. Furthermore, we show that the pipeline generalizes to other domains, such as re-identification of seals and cows. Lastly, we discuss the impracticality of a ranking-based output for real-life tasks and propose an alternative approach by viewing re-identification as a binary classification. We show that the pipeline can be easily modified with minimal fine-tuning to provide a binary output with a precision of 98% and recall of 44% on the TinyMola+ dataset, which basically eliminates the need for time-consuming manual verification on nearly half the dataset.
MovableBag: Exploring Asymmetric Interaction for Multi-user Exergame in Extended Reality

Previous research shows that virtual reality (VR) exergames motivate users’ physical activity with immersion and enjoyment. However, it limits the movement for players to perform intense exercises in a small space with a complex tracking setup. In addition, asymmetric interaction of multi-user exergame in the same physical space is also lacking in the discussion in extended reality (XR). So, we present MovableBag, a substitutional robot that can be adapted to multi-user exergames with prop. It also serves as an agent to allow users from the physical world to interact with the VR user to become an XR application. Additionally, a preliminary study has been conducted, and a multi-user boxing exergame is designed to investigate the usage of the work. It also informs future design for more use cases in XR for asymmetric interaction between VR and non-VR users.
Leprosy Diagnosis using Explainable Artificial Intelligence Techniques

LRprosy, also known as Hansen’s Disease is a chronic curable infection that seldom causes skin lesion and nerve damage. The World Health organization reported 127558 new leprosy cases detected globally in 2020. National Leprosy Eradication Program (NLEP) initiated by India, is the largest leprosy eradication program of the world. Despite this, 53.64% of leprosy cases (120,000 to 130,000 per year) are in India. Although this disease is curable at the later stages, an early diagnosis eradicates the complications incurred by nerve involvement. Existing research predicts leprosy on basis of electronic health records which require complex contact based techniques for procuration. Image based predictors lack credibility as it is hard to determine what features are being taken into consideration for prediction. This paper proposes an optimal AXI-CNN architecture for leprosy prediction called LeprosyNet. Deep Learning models are seldom black box in nature, in order to understand the area of interest in the images, explainable AI techniques like Activation Layer Visualization, Occlusion Sensitivity and Grad-CAM are used. The proposed model is compared with famous state-of-art architectures AlexNet and ResNet. Evaluation of the model has been undertaken using a ROC curve and confusion matrix. Accuracy obtained on LeprosyNet is 98%.
Introduction to the Special Issue on Cognitive Computing for Internet of Medical Things in Smart Healthcare

introduction Share on Introduction to the Special Issue on Cognitive Computing for Internet of Medical Things in Smart Healthcare Authors: Syed Hassan A. Shah California State University, Fullerton, USA California State University, Fullerton, USA 0000-0002-1381-5095View Profile , Shahid Mumtaz Instituto de Telecomunicações, Portugal Instituto de Telecomunicações, Portugal 0000-0001-6364-6149Search about this author , Wei Wei Xi'an University of Technology, China Xi'an University of Technology, China 0000-0002-8751-9205Search about this author Authors Info & Claims ACM Transactions on Sensor NetworksVolume 19Issue 3Article No.: 48epp 1–3https://doi.org/10.1145/3584742Published:25 April 2023Publication History 0citation69DownloadsMetricsTotal Citations0Total Downloads69Last 12 Months69Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Template matching based on convolution neural network for UAV visual localization

Currently, unmanned aerial vehicle (UAVs) are used in many fields, among which UAV localization is the key to UAV autonomous flight capability, and how to locate UAVs in the absence of GNSS signals is a difficult task. In recent years, deep learning has developed rapidly, among which convolutional neural networks are widely used in visual images. In this paper, we apply the convolutional neural network DenseNet to the task of matching between UAV images and satellite remote sensing images for visual localization of UAVs. We make some improvements to address the problems in practice. We propose a quality-aware template matching method based on adaptive adjustment of convolutional feature weights to enhance the feature extraction capability of the model, and introduce a fusion mechanism of multi-scale feature layers. The feature maps of UAV images and satellite images are quality scored so as to locate the position of UAV in satellite images. Qualitative and quantitative experiments are conducted, and the results show the effectiveness and superiority of the method.
Mutually-paced Knowledge Distillation for Cross-lingual Temporal Knowledge Graph Reasoning

This paper investigates cross-lingual temporal knowledge graph reasoning problem, which aims to facilitate reasoning on Temporal Knowledge Graphs (TKGs) in low-resource languages by transfering knowledge from TKGs in high-resource ones. The cross-lingual distillation ability across TKGs becomes increasingly crucial, in light of the unsatisfying performance of existing reasoning methods on those severely incomplete TKGs, especially in low-resource languages. However, it poses tremendous challenges in two aspects. First, the cross-lingual alignments, which serve as bridges for knowledge transfer, are usually too scarce to transfer sufficient knowledge between two TKGs. Second, temporal knowledge discrepancy of the aligned entities, especially when alignments are unreliable, can mislead the knowledge distillation process. We correspondingly propose a mutually-paced knowledge distillation model MP-KD, where a teacher network trained on a source TKG can guide the training of a student network on target TKGs with an alignment module. Concretely, to deal with the scarcity issue, MP-KD generates pseudo alignments between TKGs based on the temporal information extracted by our representation module. To maximize the efficacy of knowledge transfer and control the noise caused by the temporal knowledge discrepancy, we enhance MP-KD with a temporal cross-lingual attention mechanism to dynamically estimate the alignment strength. The two procedures are mutually paced along with model training. Extensive experiments on twelve cross-lingual TKG transfer tasks in the EventKG benchmark demonstrate the effectiveness of the proposed MP-KD method.