Reviewerly/exHarmony huggingface dataset:
Optimal Interval Type-2 Fuzzy Logic Control Based Closed-Loop Regulation of Mean Arterial Blood Pressure Using the Controlled Drug Administration

In pandemic times, the remote and automatic control/observation of physiological variables of the patients is the prime necessity for healthcare technologies. The closed-loop regulation of mean arterial pressure is essential for critically ill patients or post-surgery recovery patients. The physiological variables have serious issues such as parameter variations and uncertainties. In this paper, an interval type-2 fuzzy logic controller (IT2FLC) with footprints of uncertainty in the membership functions is presented in a two-layered design (TL-IT2FLC) that can efficiently deal with uncertainties and parameter variations. In this design, a pre-compensator IT2FLC is used, which deals with the uncertainties and parameter variations before the primary IT2FLC approach. The robustness analysis of the proposed method is investigated for external noise, and results are compared with traditional PID and T1-FLC techniques.
Contextual Proposal Network for Action Localization

This paper investigates the problem of Temporal Action Proposal (TAP) generation, which aims to provide a set of high-quality video segments that potentially contain actions events locating in long untrimmed videos. Based on the goal to distill available contextual information, we introduce a Contextual Proposal Network (CPN) composing of two context-aware mechanisms. The first mechanism, i.e., feature enhancing, integrates the inception-like module with long-range attention to capture the multi-scale temporal contexts for yielding a robust video segment representation. The second mechanism, i.e., boundary scoring, employs the bi-directional recurrent neural networks (RNN) to capture bi-directional temporal contexts that explicitly model actionness, background, and confidence of proposals. While generating and scoring proposals, such bi-directional temporal contexts are helpful to retrieve high-quality proposals of low false positives for covering the video action instances. We conduct experiments on two challenging datasets of ActivityNet-1.3 and THUMOS-14 to demonstrate the effectiveness of the proposed Contextual Proposal Network (CPN). In particular, our method respectively surpasses state-of-the-art TAP methods by 1.54% AUC on ActivityNet-1.3 test split and by 0.61% AR@200 on THUMOS-14 dataset.
A novel constrained non-negative matrix factorization method based on users and items pairwise relationship for recommender systems

Non-negative matrix factorization (NMF) is a famous method to learn parts-based representations of non-negative data. It has been used successfully in various applications such as information retrieval and recommender systems. Most of the current NMF methods only focus on how each decomposed matrices vector should be modeled and disregard the relation among pairwise feature vectors of users and items. In recommender systems, the relationship between users’ factors or items’ features provides better factorization. This paper proposes a constrained non-negative matrix factorization for recommender systems (CNMF-RS), which puts constraints on pairwise vectors of users and items by applying penalties using similarity. Two cost functions are proposed to generate the corresponding updating rules for finding unknown ratings. The computational complexity analysis confirmed that CNMF-RS does not raise time complexity using constraints compared with the basic NMF. Experiments on benchmark datasets indicate that the suggested update rules converge quickly and obtain outstanding results compared to other methods. • NMF generates low-rank data while keeping the non-negativity of matrices elements. • Update rules warrant the non-negativity of factorized matrices and the convergence. • Constrained NMF imposes constraints as regularizers in the factorizing process. • CNMF uses the Frobenius norm and the KL-divergence as the cost functions. • CNMF applies penalties to create a generic and flexible algorithm for RSs.
An Interactive and Adaptive Learning Cyber Physical Human System for Manufacturing With a Case Study in Worker Machine Interactions

An adaptive machine learning (ML) based smart manufacturing interactive cyber physical human system (ICPHS) is conceptualized, designed, and implemented. One of its significant properties is an ML model that during deployment self-evolves with the streaming data in a self-labeling manner. This automated adaptive ML system is realized by leveraging the underlying causality during human machine interactions, and initialized by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> domain knowledge and preprocessed public datasets. The system defines a causal and temporal mapping of worker and machine states where one side can label the other automatically. A case study in machines with different automation levels is conducted in a multiuser facility by using finite state machine representations for machine energy states, worker activity states, and interaction transition functions. The fully automated adaptive ML system improves its accuracy for human machine interaction detection by up to 12.5% and shows potential to recognize more fine-grained actions.
Developing Multiagent E-Learning System-Based Machine Learning and Feature Selection Techniques

Recently, artificial intelligence (AI) domain increased to contain finance, education, health, mining, and education. Artificial intelligence controls the performance of systems that use new technologies, especially in the education environment. The multiagent system (MAS) is considered an intelligent system to facilitate the e-learning process in the educational environment. MAS is used to make interaction easily among agents, which supports the use of feature selection. The feature selection methods are used to select the important and relevant features from the database that could help machine learning algorithms produce high performance. This paper aims to propose an effective and suitable system for multiagent-based machine learning algorithms and feature selection methods to enhance the e-learning process in the educational environment which predicts pass or fail results. The univariate and Extra Trees feature selection methods are used to select the essential attributes from the database. Five machine learning algorithms named Decision Tree (DT), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), and K-nearest neighbors algorithm (KNN) are applied to all features and selected features. The results showed that the learning algorithm that has been measured by the Extra Trees method has achieved the highest performance depending on the evaluation of cross-validation and testing.
Performance Comparison of Transformer-Based Models on Twitter Health Mention Classification

Health mention classification classifies a given piece of text as a health mention or not. However, figurative usage of disease words makes the classification task challenging. To address this challenge, consideration of emojis and surrounding words of the disease names in the text can be helpful. Transformer-based methods are better at capturing the meaning of a word based on its surrounding words compared to traditional methods. However, there are numerous transformer-based methods available and pretrained on natural language processing (NLP) data that are inherently different from Twitter data. Moreover, the size of these models varies in terms of the number of parameters. Hence, it is challenging to decide and choose one of these methods for fine-tuning it on the downstream tasks such as tweet classification. In this work, we experiment with nine widely used transformer methods and compare their performance on the personal health mention classification of tweet data. Furthermore, we analyze the impact of model size on the classification task and provide a brief interpretation of the classification decision made by the best performing classifier. Experimental results show that RoBERTa outperforms all other models by achieving an F1 score of 93%, while two other models perform similarly by achieving an F1 score of 92.5%.
Auto-GNAS: A Parallel Graph Neural Architecture Search Framework

Graph neural networks (GNNs) have received much attention as GNNs have recently been successfully applied on non-euclidean data. However, artificially designed graph neural networks often fail to get satisfactory model performance for a given graph data. Graph neural architecture search effectively constructs the GNNs that achieve the expected model performance with the rise of automatic machine learning. The challenge is efficiently and automatically getting the optimal GNN architecture in a vast search space. Existing search methods serially evaluate the GNN architectures, severely limiting system efficiency. To solve these problems, we develop an <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Auto</b> matic <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</b> raph <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> eural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> rchitecture <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> earch framework (Auto-GNAS) with parallel estimation to implement an automatic graph neural search process that requires almost no manual intervention. In Auto-GNAS, we design the search algorithm with multiple genetic searchers. Each searcher can simultaneously use evaluation feedback information, information entropy, and search results from other searchers based on sharing mechanism to improve the search efficiency. As far as we know, this is the first work using parallel computing to improve the system efficiency of graph neural architecture search. According to the experiment on the real datasets, Auto-GNAS obtain competitive model performance and better search efficiency than other search algorithms. Since the parallel estimation ability of Auto-GNAS is independent of search algorithms, we expand different search algorithms based on Auto-GNAS for scalability experiments. The results show that Auto-GNAS with varying search algorithms can achieve nearly linear acceleration with the increase of computing resources.
HRGAN: A Generative Adversarial Network Producing Higher-Resolution Images than Training Sets

The generative adversarial network (GAN) has demonstrated superb performance in generating synthetic images in recent studies. However, in the conventional framework of GAN, the maximum resolution of generated images is limited to the resolution of real images that are used as the training set. In this paper, in order to address this limitation, we propose a novel GAN framework using a pre-trained network called evaluator. The proposed model, higher resolution GAN (HRGAN), employs additional up-sampling convolutional layers to generate higher resolution. Then, using the evaluator, an additional target for the training of the generator is introduced to calibrate the generated images to have realistic features. In experiments with the CIFAR-10 and CIFAR-100 datasets, HRGAN successfully generates images of 64 × 64 and 128 × 128 resolutions, while the training sets consist of images of 32 × 32 resolution. In addition, HRGAN outperforms other existing models in terms of the Inception score, one of the conventional methods to evaluate GANs. For instance, in the experiment with CIFAR-10, a HRGAN generating 128 × 128 resolution demonstrates an Inception score of 12.32, outperforming an existing model by 28.6%. Thus, the proposed HRGAN demonstrates the possibility of generating higher resolution than training images.
Clustering with multi-layered perceptron

Multi-layered perceptron (MLP) is a widely used neural network architecture for supervised learning. The feed-forward network maps unknown data to a label based on prior labeled training samples. The accuracy with which test data is classified correctly depends on the number of training samples. However, in unsupervised learning hidden grouping patterns in data are learnt through various exploratory analysis without any prior knowledge of labels. Clustering using MLP architecture requires defining a different type of loss function. In this paper, we theoretically analyse the effectiveness of a loss function previously reported for this purpose, where the input features are mapped to an output cluster node based on the degree of belongingness. Thus, similar samples are grouped together to form clusters using entropy based measures. Various evaluation techniques to inspect the quality of clusters and intra-cluster distances are used to validate the clustering results on real world data sets.
Masking for better discovery: Weakly supervised complementary body regions mining for person re-identification

Person re-identification still facing several challenges related to many factors such as complex poses, occlusion, misalignment and bad detection. Recent works in the literature focus on extracting local information from the human body but most of them rely on full supervision during training. We propose in this paper a new end-to-end trainable neural network, named Attention Dropping Network (ADN), for diverse rich visual cues discovery without extra human semantic parsing. ADN aims to find fine-grained local information to address the shared person re-identification challenges. Concretely, our network consists of two branches. The Attention Global Branch learns pixel-level local regions based on a fine-grained attention mechanism while the Feature Dropping Branch learns additional missed features in a weakly supervised manner. The fine-grained attention mechanism allows our model to be robust to complex pose variations and to avoid the redundant backgrounds. Extensive experiments over three benchmark datasets (Market-1501, DukeMTMC-reID, and CUHK03) demonstrate the effectiveness and robustness of the proposed network in handling the problems of complex poses, misalignment and occlusions.
Feature-Align Network with Knowledge Distillation for Efficient Denoising

We propose an efficient neural network for RAW image denoising. Although neural network-based denoising has been extensively studied for image restoration, little attention has been given to efficient denoising for compute limited and power sensitive devices, such as smartphones and wearables. In this paper, we present a novel architecture and a suite of training techniques for high quality denoising in mobile devices. Our work is distinguished by three main contributions. (1) The Feature-Align layer that modulates the activations of an encoder-decoder architecture with the input noisy images. The auto modulation layer enforces attention to spatially varying noise that tends to be “washed away” by successive application of convolutions and non-linearity. (2) A novel Feature Matching Loss that allows knowledge distillation from large denoising networks in the form of a perceptual content loss. (3) Empirical analysis of our efficient model trained to specialize on different noise subranges. This opens an additional avenue for model size reduction by sacrificing memory for compute. Extensive experimental validation shows that our efficient model produces high quality denoising results that compete with state-of-the-art large networks, while using significantly fewer parameters and MACs. On the Darmstadt Noise Dataset benchmark, we achieve a PSNR of 48. 28dB, while using $263\times$ fewer MACs and $17.6\times$ fewer parameters than the state-of-the-art network, which achieves 49. 12dB.
A Robust Visual System for Looming Cue Detection Against Translating Motion

Collision detection is critical for autonomous vehicles or robots to serve human society safely. Detecting looming objects robustly and timely plays an important role in collision avoidance systems. The locust lobula giant movement detector (LGMD1) is specifically selective to looming objects which are on a direct collision course. However, the existing LGMD1 models cannot distinguish a looming object from a near and fast translatory moving object, because the latter can evoke a large amount of excitation that can lead to false LGMD1 spikes. This article presents a new visual neural system model (LGMD1) that applies a neural competition mechanism within a framework of separated ON and OFF pathways to shut off the translating response. The competition-based approach responds vigorously to monotonous ON/OFF responses resulting from a looming object. However, it does not respond to paired ON-OFF responses that result from a translating object, thereby enhancing collision selectivity. Moreover, a complementary denoising mechanism ensures reliable collision detection. To verify the effectiveness of the model, we have conducted systematic comparative experiments on synthetic and real datasets. The results show that our method exhibits more accurate discrimination between looming and translational events-the looming motion can be correctly detected. It also demonstrates that the proposed model is more robust than comparative models.
Ethical Challenges in AI

In the first part we address four current specific challenges through examples: (1) discrimination (e.g., facial recognition, justice, sharing economy, language models); (2) stupid models (e.g., lack of semantic and context understanding); (3) physiognomy (e.g., facial bio-metrics based predictions); and (4) indiscriminate use of computing resources (e.g., large language models). These examples do have a personal bias but set the context for the second part where we address four generic challenges: (1) too many principles, (2) cultural differences; (3) regulation and (4) our cognitive biases. We finish discussing what we can do to address these challenges in the near future.
Fight Detection from Still Images in the Wild

Detecting fights from still images shared on social media is an important task required to limit the distribution of violent scenes in order to prevent their negative effects. For this reason, in this study, we address the problem of fight detection from still images collected from the web and social media. We explore how well one can detect fights from just a single still image. We also propose a new dataset, named Social Media Fight Images (SMFI), comprising real-world images of fight actions. Results of the extensive experiments on the proposed dataset show that fight actions can be recognized successfully from still images. That is, even without exploiting the temporal information, it is possible to detect fights with high accuracy by utilizing appearance only. We also perform cross-dataset experiments to evaluate the representation capacity of the collected dataset. These experiments indicate that, as in the other computer vision problems, there exists a dataset bias for the fight recognition problem. Although the methods achieve close to 100% accuracy when trained and tested on the same fight dataset, the cross-dataset accuracies are significantly lower, i.e., around 70% when more representative datasets are used for training. SMFI dataset is found to be one of the two most representative datasets among the utilized five fight datasets.
A simulated annealing-particle swarm optimization algorithm for UAV multi-target path planning

In the application of data collection of the Internet of Things by unmanned aerial vehicle (UAV), UAV multi-target path planning (MPP) has an important impact. Aiming at the disadvantage of classic particle swarm optimization algorithm that is easy to fall into local optimum when solving the problem of UAV-MPP, this paper introduces the annealing mechanism and Metropolis criterion to update the particle state through simulated annealing probability jump strategy. The simulated annealing-particle swarm optimization (SA-PSO) algorithm reduces the probability of particles falling into a local optimum and improves the performance of the particle swarm algorithm in dealing with the UAV-MPP problem. The simulation results indicate that the proposed SA-PSO algorithm has the characteristics of strong ability to avoid local optima and the faster convergence speed.
A Multipurpose Wearable Sensor-Based System for Weight Training

In recent years, there has been growing interest in automated tracking and detection of sports activities. Researchers have shown that providing activity information to individuals during their exercise routines can greatly help them in achieving their exercise goals. In particular, such information would help them to maximize workout efficiency and prevent overreaching and overtraining. This paper presents the development of a novel multipurpose wearable device for automatic weight detection, activity type recognition, and count repetition in sports activities such as weight training. The device monitors weights and activities by using an inertial measurement unit (IMU), an accelerometer, and three force sensors mounted in a glove, and classifies them by utilizing developed machine learning models. For weight detection purposes, different classifiers including Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), and Multi-layer Perceptron Neural Networks (MLP) were investigated. For activity recognition, the K nearest neighbor (KNN), Decision Tree (DT), Random Forest (RF), and SVM models were trained and examined. Experimental results indicate that the SVM classifier can achieve the highest accuracy for weight detection whereas RF can outperform other classifiers for activity recognition. The results indicate feasibility of developing a wearable device that can provide in-situ accurate information regarding the lifted weight and activity type with minimum physical intervention.
Towards Unsupervised Online Domain Adaptation for Semantic Segmentation

In recent years, there has been significant progress in overcoming the negative effects of domain shift in semantic segmentation. Yet, existing unsupervised domain adaptation methods operate in an offline fashion, which imposes multiple restrictions on their deployment in real world scenarios. In this paper, we introduce a problem of online domain adaptation for semantic segmentation, which involves producing predictions for and, at the same time, continuously adapting a model to new frames of target domain videos. To tackle this problem, we propose a novel method which utilizes unsupervised structure-from-motion cues as the primary source of domain adaptation. By optimizing online the representation shared between depth and semantics networks, our geometry-guided algorithm achieves semantic segmentation performance comparable to state-of-the-art offline methods, without using target domain training data whatsoever.
Embedded Face Recognition for Personalized Services in the Assistive Robotics

Recently, the field of assistive robotics has drawn much attention in the health care sector. In combination with modern machine learning-supported person recognition systems, they can deliver highly personalized services. However, common algorithms for person recognition such as convolutional neural networks (CNNs) consume high amounts of power and show low energy efficiency when executed on general-purpose computing platforms.