Reviewerly/exHarmony huggingface dataset:
The Role of International Research Collaboration and Faculty Related Factors in Publication Citations: Evidence from Lebanon

The importance of international collaboration in the research field is well-documented, but its impact on the number of citations received by research publications is not fully understood. This paper aims to fill this gap by examining the relationship between international collaboration and other factors and the number of citations for research publications. Using data from the Lebanese American University, the authors employ ordinary least squares estimation to analyze the impact of international collaboration on the number of citations while controlling for various publications, authorship, university, and socioeconomic factors. The results show that international collaboration has a statistically significant positive effect on the number of citations. Based on these findings, we recommend that institutions and researchers seeking to increase the number of citations for their publications should focus on building and maintaining strong international collaborations and publishing in high-quality journals. This paper provides evidence of the importance of international collaboration for researchers seeking to increase the reach and impact of their publications. It also provides insights into the relevance of other factors, such as the number of publications, age, and journal rank.
AI Coach: A Motor Skill Training System using Motion Discrepancy Detection

Spatial and temporal clues found in a professional’s motion are essential for designing a training system for learning a motor skill. We investigate the potential of using neural networks to learn spatial and temporal features of advanced players in sports and to detect the fine-grained differences between motions. As a training system, we implement an AI Coach prototype application that finds the differences between two input motions and visualizes a recommendation motion for the users to correct their forms. In the user study, we investigate the effects of the proposed AI Coach and discuss the findings based on quantitative questionnaires and qualitative interviews. In the study, the proposed system can help the user better understand the difference between them and the coach. The study also reveals the necessity of coaching beginners in the early learning phases.
Federated Learning for Vision-based Obstacle Avoidance in the Internet of Robotic Things

Deep learning methods have revolutionized mobile robotics, from advanced perception models for an enhanced situational awareness to novel control approaches through re-inforcement learning. This paper explores the potential of federated learning for distributed systems of mobile robots enabling collaboration on the Internet of Robotic Things. To demonstrate the effectiveness of such an approach, we deploy wheeled robots in different indoor environments. We analyze the performance of a federated learning approach and compare it to a traditional centralized training process with a priori aggregated data. We show the benefits of collaborative learning across heterogeneous environments and the potential for sim-to-real knowledge transfer. Our results demonstrate significant performance benefits of FL and sim-to-real transfer for vision-based navigation, in addition to the inherent privacy-preserving nature of FL by keeping computation at the edge. This is, to the best of our knowledge, the first work to leverage FL for vision-based navigation that also tests results in real-world settings.
FLIoDT: A Federated Learning Architecture from Privacy by Design to Privacy by Default over IoT

The Internet of Things (IoT) realized exponential growth of smart devices with decent capabilities, promising an era of Edge Intelligence. This paradigm creates a timely need to shift many computations closer to the data source at the network's edge. Data privacy is paramount, as security breaches can severely impact such an environment with its vast attack surface. The advent of Federated learning (FL), a privacy-by-design with decentralized machine learning (ML), enables participants to collaboratively train a model without sharing their sensitive data. Nevertheless, privacy implications are a glaring concern and perrier for widening the utilization of FL approaches and their mass adoption over IoT applications. This paper introduces the notion of FL over the Internet of Disconnected Things (FLIoDT), a functionality separation of concerns following the air-gapped networks. FLIoDT provides a practical methodology to mitigate Data threats/attacks in the FL domain. FLIoDT proves a practical architectural approach to mitigate several attacks in an Edge environment. Data dredging and adversarial attacks, like data poisoning, to name some. This study investigates human activity recognition of health monitoring patient data over edge computing to validate FLIoDT.
Edge-MultiAI: Multi-Tenancy of Latency-Sensitive Deep Learning Applications on Edge

Smart IoT-based systems often desire continuous execution of multiple latency-sensitive Deep Learning (DL) applications. The edge servers serve as the cornerstone of such IoT based systems, however, their resource limitations hamper the continuous execution of multiple (multi-tenant) DL applications. The challenge is that, DL applications function based on bulky “neural network (NN) models” that cannot be simultaneously maintained in the limited memory space of the edge. Accordingly, the main contribution of this research is to overcome the memory contention challenge, thereby, meeting the latency constraints of the DL applications without compromising their inference accuracy. We propose an efficient NN model management framework, called Edge-MultiAI, that ushers the NN models of the DL applications into the edge memory such that the degree of multi-tenancy and the number of warm-starts are maximized. Edge-MultiAI leverages NN model compression techniques, such as model quantization, and dynamically loads NN models for DL applications to stimulate multi-tenancy on the edge server. We also devise a model management heuristic for Edge-MultiAI, called iWS-BFE, that functions based on the Bayesian theory to predict the inference requests for multi-tenant applications, and uses it to choose the appropriate NN models for loading, hence, increasing the number of warm-start inferences. We evaluate the efficacy and robustness of Edge-MultiAI under various configurations. The results reveal that Edge-MultiAI can stimulate the degree of multi-tenancy on the edge by at least 2× and increase the number of warm-starts by ≈60% without any major loss on the inference accuracy of the applications.
FedCD: Personalized Federated Learning via Collaborative Distillation

Federated learning enables the creation of a centralized global model by aggregating updates from the locally trained models from multiple clients. While powerful, such an architecture is limited to applications where the needs of heterogeneous clients can be served by a single global model. It does not cater to the scenarios where each client independently designs its own model. Task and data heterogeneity inherent to such scenarios demand each client to specialize in the local setting while still being able to collaborate and transfer the acquired knowledge to the rest of the federation without sharing the data or the model. In this work, we utilize ensemble and collaborative learning to design a framework that enables the training of personalized models for heterogeneous clients with different learning capacities using federated learning. Empirical evaluations performed on the CIFAR100 dataset demonstrate that our framework is able to consistently improve the performance of all the participating models and outperform the independently trained models on the complete training set without collaboration. We analyze that all participants benefit from collaborative distillation and boast an average 1.4% increase in performance. Moreover, a comparison with the state-of-the-art approaches demonstrates that our framework outperforms the Federated Learning and Federation Distillation methods by up to a 2$\times$ increase in the average global accuracy.
Protect Trajectory Privacy in Food Delivery with Differential Privacy and Multi-agent Reinforcement Learning

Today, multiple food delivery companies work globally in different regions, and this expansion could expose users' data to danger. This data could be stored by a third party and could be used in further analysis. The stored data needs to be stored in a proper way to prevent any other from identifying the real data if this data is disclosed. This work considers this issue to maintain the data privacy of stored customer data by leveraging differential privacy and multi-agent reinforcement learning. In the beginning, the agent delivers the food to the customer. Then the agent constructs N of obfuscated trajectories with different privacy budgets. The multi-agent reinforcement learning then chooses one trajectory from the constructed trajectories. The trajectory is then evaluated by considering three factors: the similarity between the selected trajectory and the original trajectory, the sensitivity of destination location and the frequency of the number of orders by the customer. We implemented our experiment on meal delivery data sets in Iowa City, USA.
Federated Learning of Predictive Models from Real Data on Diabetic Patients

Today Federated Learning is gaining more and more notoriety in the medical field, especially since it can guarantee the privacy of sensitive data, unlike Machine Learning. Furthermore, training a model on a single patient does not always guarantee acceptable results as we are different from each other. Starting from this assumption, the idea would therefore be to create a system that allows diabetic patients to share their data on the cloud (also using the Cloud/Edge Continuum technology and clearly going to respect all the constraints related to privacy by differentiating the use of the public cloud from the private one), collected via smartwatch, and, through Federated Learning, to train a centralized model that allows them to go to predict when one of the patients may need to inject insulin into his/her body. In particular, this document represents a part of a larger work in which various Time Series Analysis and Deep Learning algorithms were tested on real data from a diabetic patient, making predictions to understand, among those analyzed, which was the best algorithm on a single patient and then generalize by applying a multi-client approach.
Using Multimodal Contrastive Knowledge Distillation for Video-Text Retrieval

Cross-modal retrieval aims to enable a flexible bi-directional retrieval experience across different modalities (e.g., searching for videos with texts). Many existing efforts tend to learn a common semantic representation embedding space in which items of different modalities can be directly compared, wherein the positive global representations of video-text pairs are pulled close while the negative ones are pushed apart via pair-wise ranking loss. However, such a vanilla loss would unfortunately yield ambiguous feature embeddings for texts of different videos, causing inaccurate cross-modal matching and unreliable retrievals. Toward this end, we propose a multimodal contrastive knowledge distillation method for instance video-text retrieval, called MCKD, by adaptively using the general knowledge of self-supervised model (teacher) to calibrate mixed boundaries. Specifically, the teacher model is tailored for robust (less-ambiguous) visual-text joint semantic space by maximizing mutual information of co-occurred modalities during multimodal contrastive learning. This robust and structural inter-instance knowledge is then distilled, with the help of explicit discrimination loss, to a student model for improved matching performance. Extensive experiments on four public benchmark video-text datasets (MSR-VTT, TGIF, VATEX, and Youtube2Text) demonstrate that our MCKD can achieve at most 8.8%, 6.4%, 5.9%, and 5.3% improvement in text-to-video performance by the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{R}\text{@}1$ </tex-math></inline-formula> metric, compared with 14 SoTA baselines.
Innovative sensorless dual-axis solar tracking system using particle filter

Renewable energy (RE), particularly solar photovoltaic (PV) systems, is a promising alternative to traditional energy sources for worldwide electricity generation. In recent years, PV generation capacity has increased rapidly due to a considerable reduction in installation costs. Nevertheless, in PV systems, energy generation is significantly less than the installed capacity due to the PV modules' orientation. Hence, this study proposes an innovative solar tracking system to enhance PV energy generation. Previous research classified solar tracking systems into two categories: sensor-based and sensorless solar tracking systems. However, tracking performance inevitably deteriorates when implementing sensor-based solar tracking systems in low-visibility conditions. Conversely, sensorless solar tracking systems require substantial historical meteorological data based on geographic location and complex mathematical models estimating the sun's position, increasing computational complexity. This study aims to overcome these difficulties by proposing a sensorless dual-axis solar tracking system that does not require historical meteorological data, complex mathematical models, and sun position sensors to track the sun's position. Particle filter (PF), a robust sampling-based tracking algorithm, is applied to develop an innovative solar tracking strategy. Initially, a set of samples (also called particles) is generated as inputs to PF corresponding to the proposed tracking system's orientation angles: daily and elevation angles. Next, PV power is captured as a measurement of each particle, and a weight associated with the PV power is estimated to represent each particle's relative significance. Furthermore, each particle is estimated and updated recursively, considering its measurement to determine the sun's probable position. The tracking strategy is terminated once the particles' weights are equal, indicating that the optimal position has been achieved (convergence). A comparative study is conducted over 60 days under various weather conditions to evaluate the proposed tracking system's performance compared with that of a fixed flat-plate system. The experimental results indicate that the proposed tracking system improves energy generation performance (after accounting for operational energy consumption) by 20.1% compared with the fixed flat-plate system.
A privacy-preserving and efficient data sharing scheme with trust authentication based on blockchain for mHealth

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Efficient Neural Network for Text Recognition in Natural Scenes Based on End-to-End Multi-Scale Attention Mechanism

Text recognition in natural scenes has been a very challenging task in recent years, and rich text semantic information is of great significance for the understanding of a scene. However, text images in natural scenes often contain a lot of noise data, which leads to error detection. The problems of high error detection rate and low recognition accuracy have brought great challenges to the task of text recognition. To solve this problem, we propose a text recognition algorithm based on natural scenes. First, the task of text detection and recognition is completed in an end-to-end way in a framework, which can reduce the cumulative error prediction and calculation caused by cascading, and has higher real-time and faster speed. In addition, we integrate a multi-scale attention mechanism to obtain attention features of different scale feature maps. Finally, we use the efficient deep learning network (EE-ACNN), which combines a convolutional neural network (CNN) with an end-to-end algorithm and multi-scale attention to enrich the text features to be detected, expands its receptive field, produces good robustness to the effective natural text information, and improves the recognition performance. Through experiments on text data sets of natural scenes, the accuracy of this method reached 93.87%, which is nearly 0.96–1.02% higher than that of traditional methods, and which proves the feasibility of this method.
Machine learning driven extended matrix norm method for the solution of large-scale zero-sum matrix games

In this paper, we develop a novel machine learning-driven framework for solving large-scale zero-sum matrix games by exploiting patterns discovered from the offline extended matrix norm method. Modern game theoretic tools such as the extended matrix norm method allow rapid estimation of the game values for small-scale zero-sum games by computing norms of the payoff matrix. However, as the number of strategies in the game increases, obtaining an accurate value estimation through the extended matrix norm method becomes more difficult. In this work, we propose a novel neural network architecture for large-scale zero-sum matrix games, which takes the estimations of the extended matrix norm method and payoff matrix as inputs, and provides a rapid estimation of the game value as the output. The proposed architecture is trained over various random zero-sum games of different dimensions. Results show that the developed framework can obtain accurate value predictions, with a less than 10% absolute relative error, for games with up to 50 strategies. Also of note, after the network is trained, solution predictions can be obtained in real-time, which makes the proposed method particularly useful for real-world applications.
Towards Automatic Model Compression via a Unified Two-Stage Framework

Deep Neural Networks have become ubiquitous in various domains. Meanwhile, the problems of massive storage and computation costs have hindered the deployment of these models to real-world applications. This paper proposes a novel and unified two-stage framework for automatic model compression. To determine the compression ratio of each layer, we improve the optimization from two aspects. First, to predict the performance of each compression policy, we propose Dynamic BN, which improves the correlation significantly with little computation overhead. Second, to search for the compression ratio allocation, we propose an efficient and hyperparameter-free solving algorithm based on the proposed Hessian matrix approximation and Knapsack problem reformulation. Moreover, comprehensive experiments and analyses are conducted on the CIFAR-100&ImageNet datasets and various network architectures to demonstrate its performance advantages over existing model compression methods under the quantization-only, pruning-only, and pruning-quantization settings.
A facial geometry based detection model for face manipulation using CNN-LSTM architecture

This issue of DeepFake technique that may cause great threat to privacy, democracy, and national security has attracted the attention of deep learning researchers. DeepFake detection, therefore, has been a very hot issue in deep learning research. The face landmark feature maps are often used by many DeepFake approaches in generating fake faces. It also provides key information to help to detect manipulated face images. In this paper, we propose a detection approach for manipulated face images. To make full use of face landmark information, we propose a facial geometry prior module (FGPM) to extract facial geometry feature maps. Then the facial geometry feature maps are embedded into upsampling feature maps generated by the CNN-LSTM network. The proposed FGPM exploits facial maps and frequency domain correlation to analyze the discriminative characteristics between manipulated and non-manipulated regions by incorporating the CNN-LSTM network. Finally, a decoder is used to learn the mapping from low-resolution feature maps to pixel-wise to predict manipulation localization. Or a softmax classifier is used to predict real or fake face images. By experimenting on several popular datasets, the proposed detection model has demonstrated the capability of localizing manipulation at the pixel level and with a high prediction on real or fake face images.
A semi-supervised short text sentiment classification method based on improved Bert model from unlabelled data

Abstract Short text information has considerable commercial value and immeasurable social value. Natural language processing and short text sentiment analysis technology can organize and analyze short text information on the Internet. Natural language processing tasks such as sentiment classification have achieved satisfactory performance under a supervised learning framework. However, traditional supervised learning relies on large-scale and high-quality manual labels and obtaining high-quality label data costs a lot. Therefore, the strong dependence on label data hinders the application of the deep learning model to a large extent, which is the bottleneck of supervised learning. At the same time, short text datasets such as product reviews have an imbalance in the distribution of data samples. To solve the above problems, this paper proposes a method to predict label data according to semi-supervised learning mode and implements the MixMatchNL data enhancement method. Meanwhile, the Bert pre-training model is updated. The cross-entropy loss function in the model is improved to the Focal Loss function to alleviate the data imbalance in short text datasets. Experimental results based on public datasets indicate the proposed model has improved the accuracy of short text sentiment recognition compared with the previous update and other state-of-the-art models.
Securing Access to Internet of Medical Things Using a Graphical-Password-Based User Authentication Scheme

Digital healthcare services have seen significant growth in this decade and many new technologies have been thoroughly examined to provide efficient services through secure infrastructures. The Internet of Medical Things (IoMT) revitalizes a healthcare infrastructure by creating an interconnected, intelligent, accessible, and efficient network. While there have been many studies on possible device authentication techniques for the IoMT, there is still much work to be done in user authentication to provide sustainable IoT solutions. Graphical passwords, which use visual content such as images instead of traditional text-based passwords, can help users authenticate themselves. However, current schemes have limitations. Therefore, this paper proposes a novel graphical authentication scheme that uses multiple factors to register and authenticate users using simple arithmetic operations, machine learning for hand gesture recognition, and medical images for recall purposes. The proposed method is designed to keep the authentication process simple, memorable, and robust. To evaluate the proposed scheme, we use the Post-Study System Usability Questionnaire (PSSUQ) to compare it with PIN-based and pattern-based authentication techniques. While comparing treatment and comparison groups, system quality showed a 16.7% better score, information quality a 25% increase, interface quality a 40% increase, and overall quality showed a 25% increase. The proposed method successfully revitalizes the use of graphical passwords, specifically in the field of IoMT, by developing a user-friendly, satisfying, and robust authentication scheme.