Reviewerly/exHarmony huggingface dataset:
Taylor-ChOA: Taylor-Chimp Optimized Random Multimodal Deep Learning-Based Sentiment Classification Model for Course Recommendation

Course recommendation is a key for achievement in a student’s academic path. However, it is challenging to appropriately select course content among numerous online education resources, due to the differences in users’ knowledge structures. Therefore, this paper develops a novel sentiment classification approach for recommending the courses using Taylor-chimp Optimization Algorithm enabled Random Multimodal Deep Learning (Taylor ChOA-based RMDL). Here, the proposed Taylor ChOA is newly devised by the combination of the Taylor concept and Chimp Optimization Algorithm (ChOA). Initially, course review is done to find the optimal course, and thereafter feature extraction is performed for extracting the various significant features needed for further processing. Finally, sentiment classification is done using RMDL, which is trained by the proposed optimization algorithm, named ChOA. Thus, the positively reviewed courses are obtained from the classified sentiments for improving the course recommendation procedure. Extensive experiments are conducted using the E-Khool dataset and Coursera course dataset. Empirical results demonstrate that Taylor ChOA-based RMDL model significantly outperforms state-of-the-art methods for course recommendation tasks.
Efficient Neural Ranking using Forward Indexes

Neural document ranking approaches, specifically transformer models, have achieved impressive gains in ranking performance. However, query processing using such over-parameterized models is both resource and time intensive. In this paper, we propose the Fast-Forward index – a simple vector forward index that facilitates ranking documents using interpolation of lexical and semantic scores – as a replacement for contextual re-rankers and dense indexes based on nearest neighbor search. Fast-Forward indexes rely on efficient sparse models for retrieval and merely look up pre-computed dense transformer-based vector representations of documents and passages in constant time for fast CPU-based semantic similarity computation during query processing. We propose index pruning and theoretically grounded early stopping techniques to improve the query processing throughput. We conduct extensive large-scale experiments on TREC-DL datasets and show improvements over hybrid indexes in performance and query processing efficiency using only CPUs. Fast-Forward indexes can provide superior ranking performance using interpolation due to the complementary benefits of lexical and semantic similarities.
PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start Recommendation

User cold-start recommendation is a serious problem that limits the performance of recommender systems (RSs). Recent studies have focused on treating this issue as a few-shot problem and seeking solutions with model-agnostic meta-learning (MAML). Such methods regard making recommendations for one user as a task and adapt to new users with a few steps of gradient updates on the meta-model. However, none of those methods consider the limitation of user representation learning imposed by the special task setting of MAML-based RSs. And they learn a common meta-model for all users while ignoring the implicit grouping distribution induced by the correlation differences among users. In response to the above problems, we propose a pretrained network modulation and task adaptation approach (PNMTA) for user cold-start recommendation. In the pretraining stage, a pretrained model is obtained with non-meta-learning methods to achieve better user representation and generalization, which can also transfer the learned knowledge to the meta-learning stage for modulation. During the meta-learning stage, an encoder modulator is utilized to realize the memorization and correction of prior parameters for the meta-learning task, and a predictor modulator is introduced to condition the model initialization on the task identity for adaptation steps. In addition, PNMTA can also make use of the existing non-cold-start users for pretraining. Comprehensive experiments on two benchmark datasets demonstrate that our model can achieve significant and consistent improvements against other state-of-the-art methods.
Usability Evaluation of Online Learning Management System: Comparison between Blackboard and Canvas

A learning management system is a convenient software that improves and enhances educators' and students' educational engagement and learning. This learning management system organizes the different course materials, provides accessibility to the learning materials, easily traces the students' performance, and integrates the social learning engagement. The study aims to compare the usability of Blackboard and Canvas based on user satisfaction of the students, evaluate the usability of the two learning management systems, and determine the acceptability score of Blackboard and Canvas. The researchers used a system usability scale questionnaire to assess the usability of Blackboard and Canvas based on the computed acceptability score from the data collected. This study also used other statistical tools such as descriptive statistics and t-test analysis to identify a significant difference between Blackboard and Canvas. Through this tool, the researchers concluded that there is no significant difference between the usability and satisfaction of the two learning management systems.
ROC Curves, Loss Functions, and Distorted Probabilities in Binary Classification

The main purpose of this work is to study how loss functions in machine learning influence the “binary machines”, i.e., probabilistic AI models for predicting binary classification problems. In particular, we show the following results: (i) Different measures of accuracy such as area under the curve (AUC) of the ROC curve, the maximal balanced accuracy, and the maximally weighted accuracy are topologically equivalent, with natural inequalities relating them; (ii) the so-called real probability machines with respect to given information spaces are the optimal machines, i.e., they have the highest precision among all possible machines, and moreover, their ROC curves are automatically convex; (iii) the cross-entropy and the square loss are the most natural loss functions in the sense that the real probability machine is their minimizer; (iv) an arbitrary strictly convex loss function will also have as its minimizer an optimal machine, which is related to the real probability machine by just a reparametrization of sigmoid values; however, if the loss function is not convex, then its minimizer is not an optimal machine, and strange phenomena may happen.
Augmented Reality in Radiology for Education and Training—A Design Study

Education is an important component of every healthcare system. Patients need to be educated about their planned procedures; healthcare professionals need to be trained in their respective profession. Both patient education and the training of healthcare professionals are often completed in person, which requires resources and is bound to certain times and places. Virtual educational environments can potentially save human and monetary resources, increase learner engagement, and enable users to learn according to their own schedules. This design study describes proofs of concept for two augmented reality-enabled (AR) educational tools, utilizing a Microsoft HoloLens head-mounted display. In the first use case, we demonstrate an AR application which could be used to educate cancer patients about their radiotherapy treatment and potentially reduce patient anxiety. The second use case demonstrates an AR training environment, which could complement the practical training of undergraduate radiography students. Two prototypes-VIPER, for patient education, and ARTUR for the training of radiography students-were developed and tested for viability and usability, both based on individual user tests. Both patient and student education were evaluated as viable and usable additions to conventional educational methods, despite being limited in terms of accessibility, usability, and fidelity. Suitable hardware is becoming more accessible and capable, and higher-fidelity holograms, better utilization of real-world objects, and more intuitive input methods could increase user immersion and acceptance of the technology.
Research on Performance Optimization and Application in Smart Home for Hyperledger Fabric

With the popularity of smart home services, smart home devices are also increasing significantly. At the same time, security problems of smart home services are becoming more and more important. With the characteristics of non-tampering and multi-party consensus mechanism, Blockchain technology provides powerful capabilities in security protection. In this paper, we introduce the widely used permission Blockchain Fabric for smart home services. As the high requirements of performance, we firstly study the methods of performance optimization for Fabric. Then, a Fabric-based smart home security control system is designed. Based on this system, the smart home system is able to provide access control and security control for smart home devices. The experimental results show that the system works best when the number of concurrent registrations of smart home device is under 6000.
Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding

Learning low-dimensional representations for Heterogeneous Information Networks (HINs) has drawn increasing attention recently for its effectiveness in real-world applications. Compared with homogeneous networks, HINs are characterized by meta-paths connecting different types of nodes with semantic meanings. Existing methods mainly follow the prototype of independently learning meta-path-based embeddings and integrating them into a unified embedding. However, meta-paths in a HIN are inherently correlated since they reflect different perspectives of the same object. If each meta-path is treated as an isolated semantic data resource and the correlations among them are disregarded, sub-optimality in the both the meta-path based embedding and final embedding will be resulted. To address this issue, we make the first attempt to explicitly model the correlation among meta-paths by proposing Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding (CKD). More specifically, we model the knowledge in each meta-path with two different granularities: regional knowledge and global knowledge. We learn the meta-path-based embeddings by collaboratively distill the knowledge from intra-meta-path and inter-meta-path simultaneously. Experiments conducted on six real-world HIN datasets demonstrates the effectiveness of the CKD method.
A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images

Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement techniques can improve the visual appearance of COVID-19 CT scans and speed-up the process of diagnosis. In this study, we analyze some state-of-the-art image enhancement techniques for their suitability in enhancing the CT scans of COVID-19 patients. Six quantitative metrics, Entropy, SSIM, AMBE, PSNR, EME, and EMEE, are used to evaluate the enhanced images. Two experienced radiologists were involved in the study to evaluate the performance of the enhancement techniques and the quantitative metrics used to assess them.
AR-BERT: Aspect-relation enhanced Aspect-level Sentiment Classification with Multi-modal Explanations

Aspect level sentiment classification (ALSC) is a difficult problem with state-of-the-art models showing less than 80% macro-F1 score on benchmark datasets. Existing models do not incorporate information on aspect-aspect relations in knowledge graphs (KGs), e.g. DBpedia. Two main challenges stem from inaccurate disambiguation of aspects to KG entities, and the inability to learn aspect representations from the large KGs in joint training with ALSC models. We propose AR-BERT, a novel two-level global-local entity embedding scheme that allows efficient joint training of KG-based aspect embeddings and ALSC models. A novel incorrect disambiguation detection technique addresses the problem of inaccuracy in aspect disambiguation. We also introduce the problem of determining mode significance in multi-modal explanation generation, and propose a two step solution. The proposed methods show a consistent improvement of 2.5 − 4.1 percentage points, over the recent BERT-based baselines on benchmark datasets.
Federated learning‐based IoT: A systematic literature review

Summary The Internet of Things (IoT) has a significant impact on our daily lives as applications, services, devices, and industries become more intelligent. Artificial intelligence (AI) is expected to significantly influence machine learning training on IoT devices without data sharing. Federated learning (FL) is a distributed machine learning method used in many IoT smart devices; however, FL ensures IoT security and privacy. The systematic literature review (SLR) method is used in this paper to review recently published articles in the FL‐based IoT domain. We analyzed 39 papers that were published between 2018 and March 2022. According to the evaluation factors, the accuracy factor has a high percentage in the FL‐based IoT domain by 29%, and the epoch has 23%, the time has 18%, the energy consumption has 9%, the delay has 9%, the communication overhead has 6%, and the privacy has 6%. Finally, we discuss future research challenges and open issues in the context of FL‐based IoT.
FPGA Implementation for the Sigmoid with Piecewise Linear Fitting Method Based on Curvature Analysis

The sigmoid activation function is popular in neural networks, but its complexity limits the hardware implementation and speed. In this paper, we use curvature values to divide the sigmoid function into different segments and employ the least squares method to solve the expressions of the piecewise linear fitting function in each segment. We then adopt an optimization method with maximum absolute errors and average absolute errors to select an appropriate function expression with a specified number of segments. Finally, we implement the sigmoid function on the field-programmable gate array (FPGA) development platform and apply parallel operations of arithmetic (multiplying and adding) and range selection at the same time. The FPGA implementation results show that the clock frequency of our design is up to 208.3 MHz, while the end-to-end latency is just 9.6 ns. Our piecewise linear fitting method based on curvature analysis (PWLC) achieves recognition accuracy on the MNIST dataset of 97.51% with a deep neural network (DNN) and 98.65% with a convolutional neural network (CNN). Experimental results demonstrate that our FPGA design of sigmoid function can obtain the lowest latency, reduce absolute errors, and achieve high recognition accuracies, while the hardware cost is acceptable in practical applications.
Federated Learning Via Inexact ADMM

One of the crucial issues in federated learning is how to develop efficient optimization algorithms. Most of the current ones require full device participation and/or impose strong assumptions for convergence. Different from the widely-used gradient descent-based algorithms, in this article, we develop an inexact alternating direction method of multipliers (ADMM), which is both computation- and communication-efficient, capable of combating the stragglers' effect, and convergent under mild conditions. Furthermore, it has high numerical performance compared with several state-of-the-art algorithms for federated learning.
Robot Navigation Based on Potential Field and Gradient Obtained by Bilinear Interpolation and a Grid-Based Search

The original concept of the artificial potential field in robot path planning has spawned a variety of extensions to address its main weakness, namely the formation of local minima in which the robot may be trapped. In this paper, a smooth navigation function combining the Dijkstra-based discrete static potential field evaluation with bilinear interpolation is proposed. The necessary modifications of the bilinear interpolation method are developed to make it applicable to the path-planning application. The effect is that the strategy makes it possible to solve the problem of the local minima, to generate smooth paths with moderate computational complexity, and at the same time, to largely preserve the product of the computationally intensive static plan. To cope with detected changes in the environment, a simple planning strategy is applied, bypassing the static plan with the solution of the A* algorithm to cope with dynamic discoveries. Results from several test environments are presented to illustrate the advantages of the developed navigation model.
Video frame interpolation via down–up scale generative adversarial networks

Frame interpolation finds many applications in video applications, including frame rate up-conversion and video compression. Deep learning-based methods have been proposed for frame interpolation, but a long runtime is typically required to achieve good visual quality. In this paper, we introduce an efficient frame interpolation method based on a modified generative adversarial network. The proposed framework consists of a generator with a pair of down–up scale modules, where the down-scaled-input module attempts to capture the overall structure of the scene while the original-scale-input module aims to restore finer textures. Skip connections and an input processing block are further incorporated into the minimal two-scale generator design to expedite processing without losing image features. The difference between the synthesized frame and the ground truth is measured by a combined loss function, including one adversarial loss and three reconstruction losses. Compared to the state-of-the-art motion compensation and deep-learning based frame interpolation approaches, the proposed framework achieves the most satisfactory trade-off between the synthesis quality and runtime.
Adaptive Hermite spectral methods in unbounded domains

A novel adaptive spectral method has been recently developed to numerically solve partial differential equations (PDEs) in unbounded domains. To achieve accuracy and improve efficiency, the method relies on the dynamic adjustment of three key tunable parameters: the scaling factor, a displacement of the basis functions, and the spectral expansion order. In this paper, we perform the first numerical analysis of the adaptive spectral method using generalized Hermite functions in both one- and multi-dimensional problems. Our analysis reveals why adaptive spectral methods work well when a “frequency indicator” of the numerical solution is controlled. We then investigate how the implementation of the adaptive spectral methods affects numerical results, thereby providing guidelines for the proper tuning of parameters. Finally, we further improve performance by extending the adaptive methods to allow bidirectional basis function translation, and the prospect of carrying out similar numerical analysis to solving PDEs arising from realistic difficult-to-solve unbounded models with adaptive spectral methods is also briefly discussed.
WebFace260M: A Benchmark for Million-Scale Deep Face Recognition

Face benchmarks empower the research community to train and evaluate high-performance face recognition systems. In this paper, we contribute a new million-scale recognition benchmark, containing uncurated 4M identities/260M faces (WebFace260M) and cleaned 2M identities/42M faces (WebFace42M) training data, as well as an elaborately designed time-constrained evaluation protocol. First, we collect 4M name lists and download 260M faces from the Internet. Then, a Cleaning Automatically utilizing Self-Training (CAST) pipeline is devised to purify the tremendous WebFace260M, which is efficient and scalable. To the best of our knowledge, the cleaned WebFace42M is the largest public face recognition training set and we expect to close the data gap between academia and industry. Referring to practical deployments, Face Recognition Under Inference Time conStraint (FRUITS) protocol and a new test set with rich attributes are constructed. Besides, we gather a large-scale masked face sub-set for biometrics assessment under COVID-19. For a comprehensive evaluation of face matchers, three recognition tasks are performed under standard, masked and unbiased settings, respectively. Equipped with this benchmark, we delve into million-scale face recognition problems. A distributed framework is developed to train face recognition models efficiently without tampering with the performance. Enabled by WebFace42M, we reduce 40% failure rate on the challenging IJB-C set and rank 3rd among 430 entries on NIST-FRVT. Even 10% data (WebFace4M) shows superior performance compared with the public training sets. Furthermore, comprehensive baselines are established under the FRUITS-100/500/1000 milliseconds protocols. The proposed benchmark shows enormous potential on standard, masked and unbiased face recognition scenarios. Our WebFace260M website is https://www.face-benchmark.org.
A Dynamic Reweighting Strategy For Fair Federated Learning

Federated learning is an emerging machine learning framework where models are trained using heterogeneous datasets collected by a large number of edge clients. Standard methods to aggregate local training models weigh each model by a fraction of data size at that client. However, such approaches result in unfairness to clients with small and unique datasets, leading to inferior accuracy of the global model at these clients. In this work, we propose a novel optimization framework called DRFL that dynamically adjusts the weight assigned to each client, and we combine it with a biased client selection strategy, both of which encourage fairness in federated training. We validate the effectiveness of our proposed method on a suite of both synthetic and real federated datasets, revealing the proposed method outperforms existing baselines in terms of resulting fairness.
Dynamic Point Cloud Interpolation

Dense photorealistic point clouds can depict real-world dynamic objects in high resolution and with a high frame rate. Frame interpolation of such dynamic point clouds would enable the distribution, processing, and compression of such content. In this work, we propose a first point cloud interpolation framework for photorealistic dynamic point clouds. Given two consecutive dynamic point cloud frames, our framework aims to generate intermediate frame(s) between them. The proposed deep learning framework has three major components: the encoder module, the fusion network, and the multi-scale point cloud synthesis module. The encoder module extracts multi-scale features from two consecutive frames. The fusion network employs a novel 4D feature learning technique to merge the multi-scale features from consecutive frames. Finally, the multi-scale point cloud synthesis module hierarchically reconstructs the interpolated point cloud intermediate frame at different resolutions. We evaluate our framework on high-resolution point cloud datasets used in MPEG, JPEG Pleno, and AVS standards. The quantitative and qualitative results demonstrate the effectiveness of the proposed method.