Reviewerly/exHarmony huggingface dataset:
Robust Finger Interactions with COTS Smartwatches via Unsupervised Siamese Adaptation

Wearable devices like smartwatches and smart wristbands have gained substantial popularity in recent years. However, their small interfaces create inconvenience and limit computing functionality. To fill this gap, we propose ViWatch, which enables robust finger interactions under deployment variations, and relies on a single IMU sensor that is ubiquitous in COTS smartwatches. To this end, we design an unsupervised Siamese adversarial learning method. We built a real-time system on commodity smartwatches and tested it with over one hundred volunteers. Results show that the system accuracy is about 97% over a week. In addition, it is resistant to deployment variations such as different hand shapes, finger activity strengths, and smartwatch positions on the wrist. We also developed a number of mobile applications using our interactive system and conducted a user study where all participants preferred our un-supervised approach to supervised calibration. The demonstration of ViWatch is shown at https://youtu.be/N5-ggvy2qfI.
Object Counting from Aerial Remote Sensing Images: Application to Wildlife and Marine Mammals

Anthropogenic activities pose threats to wildlife and marine fauna, prompting the need for efficient animal counting methods.This research study utilizes deep learning techniques to automate counting tasks.Inspired by previous studies on crowd and animal counting, a UNet model with various backbones is implemented, which uses Gaussian density maps for training, bypassing the need of training a detector.The new model is applied to the task of counting dolphins and elephants in aerial images.Quantitative evaluation shows promising results, with the EfficientNet-B5 backbone achieving the best performance for African elephants and the ResNet18 backbone for dolphins.The model accurately locates animals despite complex image background conditions.By leveraging artificial intelligence, this research contributes to wildlife conservation efforts and enhances coexistence between humans and wildlife through efficient object counting without detection from aerial remote sensing.
Consistent Along Track Sharpness in a Push-Broom Imaging System

The along track spatial resolution in push broom imaging systems depends on platform parameters and imaging settings. This paper present a method for achieving a consistent spatial resolution for varying exposure times. We consider the geometry of a simplified push broom imaging system to derive relationships between spatial resolution, platform parameters (altitude, speed, focal length, slit width) and recording settings (exposure time, frame rate). The method is tested and verified using data from the HYPerspectral Smallsat for Ocean observation-1 (HYPSO-1). The spatial resolution is consistent over the exposure time range of 4.41ms to 49.3ms for low off-nadir angles when choosing frame rates according to the method.
Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. We introduce a novel representation learning for histopathology images to identify representative normal keys. These keys facilitate the selection of salient instances within WSIs, forming bags with high tumor-to-normal ratios. Finally, an attention mechanism is employed for slide-level classification based on formed bags. Our results show that salient instance inference can improve the tumor-to-normal area ratio in the tumor WSIs. As a result, SiiMIL achieves 0.9225 AUC and 0.7551 recall on the Camelyon16 dataset, which outperforms the existing MIL models. In addition, SiiMIL can generate tumor-sensitive attention heatmaps that is more interpretable to pathologists than the widely used attention-based MIL method. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times.
Intelligent Escape of Robotic Systems: A Survey of Methodologies, Applications, and Challenges

Intelligent escape is an interdisciplinary field that employs artificial intelligence (AI) techniques to enable robots with the capacity to intelligently react to potential dangers in dynamic, intricate, and unpredictable scenarios. As the emphasis on safety becomes increasingly paramount and advancements in robotic technologies continue to advance, a wide range of intelligent escape methodologies has been developed in recent years. This paper presents a comprehensive survey of state-of-the-art research work on intelligent escape of robotic systems. Four main methods of intelligent escape are reviewed, including planning-based methodologies, partitioning-based methodologies, learning-based methodologies, and bio-inspired methodologies. The strengths and limitations of existing methods are summarized. In addition, potential applications of intelligent escape are discussed in various domains, such as search and rescue, evacuation, military security, and healthcare. In an effort to develop new approaches to intelligent escape, this survey identifies current research challenges and provides insights into future research trends in intelligent escape.
A comprehensive review on federated learning based models for healthcare applications

A disease is an abnormal condition that negatively impacts the functioning of the human body. Pathology determines the causes behind the disease and identifies its development mechanism and functional consequences. Each disease has different identification methods, including X-ray scans for pneumonia, covid-19, and lung cancer, whereas biopsy and CT-scan can identify the presence of skin cancer and Alzheimer's disease, respectively. Early disease detection leads to effective treatment and avoids abiding complications. Deep learning has provided a vast number of applications in medical sectors resulting in accurate and reliable early disease predictions. These models are utilized in the healthcare industry to provide supplementary assistance to doctors in identifying the presence of diseases. Majorly, these models are trained through secondary data sources since healthcare institutions refrain from sharing patients' private data to ensure confidentiality, which limits the effectiveness of deep learning models due to the requirement of extensive datasets for training to achieve optimal results. Federated learning deals with the data in such a way that it doesn't exploit the privacy of a patient's data. In this work, a wide variety of disease detection models trained through federated learning have been rigorously reviewed. This meta-analysis provides an in-depth review of the federated learning architectures, federated learning types, hyperparameters, dataset utilization details, aggregation techniques, performance measures, and augmentation methods applied in the existing models during the development phase. The review also highlights various open challenges associated with the disease detection models trained through federated learning for future research.
Impact of transformational leadership on leadership effectiveness: the mediating effect of emotional skills in the Lebanese small and medium-sized enterprises context

Purpose This study aims to examine the impact of transformational leadership on leadership effectiveness and the mediating effect of emotional skills in this relationship. Design/methodology/approach The study used quantitative methodology, collecting data from 350 respondents from the Lebanese context. The data were analysed using AMOS for structural equation modelling. Findings Results indicated that transformational leadership positively impacts leadership effectiveness, and this relationship is partially mediated by emotional skills. Specifically, adaptability, assertiveness and relationship management partially mediate the relationship of transformational leadership to leadership effectiveness. The findings suggest that leaders who possess emotional skills, especially those related to adaptability, assertiveness and relationship management, can be more effective in their roles by inspiring and motivating their followers through transformational leadership. Research limitations/implications The study relies on self-reported data, which can introduce potential biases such as social desirability bias and subjectivity. The study uses a cross-sectional design, which hinders establishing causal relationships or examining changes over time. Practical implications This study highlights the significance of transformational leadership on leadership effectiveness and its potential benefits on emotional skills as a mediator in this relationship. Originality/value The research is unique and provides potential contribution to the Lebanese context.
A Novel Approach for Recognition and Classification of Hand Gesture Using Deep Convolution Neural Networks

Sign language is a means of communication between individuals, whether they are hard of hearing or other people who do not speak the language of the host country. While it is a means of communication, few people know it and it has no universal patterns. For example, there is a set of signs unique to each country, resulting from the customs and traditions of each country. Currently, much research is being done in this area to solve the problems of sign language translation using computer vision and artificial intelligence. The importance of this topic is due to the possibility of using Deep Convolutional Neural Networks (CNNs), embedded in deep learning technology, to recognize hand gestures in real time and to translate sign language. This article presents two new datasets containing 54,049 of images of the ArSL-2018 (Arabic Sign Language) alphabet taken by more than 40 people, as well as 15,200 images in our personal datasets, categorized into 32 classes of standard Arabic characters, and classified, normalized, and detected using a VGGNet model and ResNet50. In this study, the success of two different training and testing exercises performed without fine-tuning and with fine-tuning enhancers was compared. The optimized weights of each VGGNet layer were achieved as the network was pre-trained on two large datasets of alphabet sign language. In addition, the ResNet50 classifier was trained using 40 epochs and fine-tuned with 40 plus epochs to ensure optimal classification performance. The high accuracy levels achieved in comparison with other studies support the effectiveness of this approach. Specifically, the ArSL alphabets dataset achieved accuracies of 99,05%, 99,99%, and 98,50%, using VGG16, VGG19, and ResNet50 Models respectively, demonstrating the effectiveness of the proposed method for hand gesture recognition tasks.
Daily global solar radiation time series prediction using variational mode decomposition combined with multi-functional recurrent fuzzy neural network and quantile regression forests algorithm

Global solar radiation (GSR) prediction capability with a reliable model and high accuracy is crucial for comprehending hydrological and meteorological systems. It is vital for the production of renewable and clean energy. This research aims to evaluate the performance of combined variational mode decomposition (VMD) with a multi-functional recurrent fuzzy neural network (MFRFNN) and quantile regression forests (QRF) models for GSR prediction in daily scales. The hybrid VMD-MFRFNN and QRF models were compared with standalone MFRFNN, random forest (RF), extreme gradient boosting (XGB), and M5 tree (M5T) models across the Lund and Växjö meteorological stations in Sweden. The meteorological data from 2008 to 2017 were used to train the models, while the prediction accuracy was verified by using the data from 2018 to 2021 under five different input combinations. The various meteorological-based scenarios (including the input are air temperatures (Tmin, Tmax, T), wind speed (WS), relative humidity (RH), sunshine duration (SSH), and maximum possible sunshine duration (N)) were considered as input of predictor models. The current study resulted that the M5T model exhibited higher accuracy than RF and XGB models, while the QRF model showed equivalent performance with the M5T model at both study sites. The MFRFNN model outperformed QRF and M5T models across all input combinations at both study sites. The hybrid VMD-MFRFNN model showed the best performance when fewer input variables (Tmin, Tmax, T, WS at Lund station and Tmin, Tmax, T, WS, SSH, RH at Växjö station) were used for GSR prediction. We conclude that the MFRFNN model best predicts average daily GSR when combining all meteorological variables (Tmin, Tmax, T, WS, SSH, RH, N).
LEARNING MANAGEMENT USING SCHEMA THEORY FOR ENGLISH LISTENING SKILL: A CASE STUDY OF CHINESE VOCATIONAL HIGH SCHOOL STUDENTS IN ZHEJIANG PROVINCE

This study aimed to improve the English listening skills of vocational high school students and their attitudes towards applying schema theory in English listening classes. Both quantitative and qualitative methods were applied including four online lesson plans, pre-test, post-test, and semi-structured interview with the focus group of four 10th grade Chinese students. The quantitative data was analyzed using a descriptive analysis method with mean and standard deviation while the qualitative data was analyzed using a thematic analysis method. The results showed that after applying schema theory to English listening lessons, the average score of students before and after the test (x = 13.50) was higher than that of the pre-test (x = 7.50).Students' responses to interviews on interests and motivation, involvement and effectiveness showed that they all expressed a positive attitude towards the application of schema theory in English listening classes. The findings remarkably proved that the application of schema theory should be considered as an alternative and effective way of teaching listening skills in English in Zhejiang province, China.
“Do you follow me?”: A Survey of Recent Approaches in Dialogue State Tracking

While communicating with a user, a task-oriented dialogue system has to track the user’s needs at each turn according to the conversation history. This process called dialogue state tracking (DST) is crucial because it directly informs the downstream dialogue policy. DST has received a lot of interest in recent years with the text-to-text paradigm emerging as the favored approach. In this review paper, we first present the task and its associated datasets. Then, considering a large number of recent publications, we identify highlights and advances of research in 2021-2022. Although neural approaches have enabled significant progress, we argue that some critical aspects of dialogue systems such as generalizability are still underexplored. To motivate future studies, we propose several research avenues.
Application of artificial intelligence in predicting malignancy risk in breast masses on ultrasound

Abstract Objective: To evaluate the results obtained with an artificial intelligence-based software for predicting the risk of malignancy in breast masses from ultrasound images. Materials and Methods: This was a retrospective, single-center study evaluating 555 breast masses submitted to percutaneous biopsy at a cancer referral center. Ultrasonographic findings were classified in accordance with the BI-RADS lexicon. The images were analyzed by using Koios DS Breast software and classified as benign, probably benign, low to intermediate suspicion, high suspicion, or probably malignant. The histological classification was considered the reference standard. Results: The mean age of the patients was 51 years, and the mean mass size was 16 mm. The radiologist evaluation had a sensitivity and specificity of 99.1% and 34.0%, respectively, compared with 98.2% and 39.0%, respectively, for the software evaluation. The positive predictive value for malignancy for the BI-RADS categories was similar between the radiologist and software evaluations. Two false-negative results were identified in the radiologist evaluation, the masses in question being classified as suspicious by the software, whereas four false-negative results were identified in the software evaluation, the masses in question being classified as suspicious by the radiologist. Conclusion: In our sample, the performance of artificial intelligence-based software was comparable to that of a radiologist.
HARE: Unifying the Human Activity Recognition Engineering Workflow

Sensor-based human activity recognition is becoming ever more prevalent. The increasing importance of distinguishing human movements, particularly in healthcare, coincides with the advent of increasingly compact sensors. A complex sequence of individual steps currently characterizes the activity recognition pipeline. It involves separate data collection, preparation, and processing steps, resulting in a heterogeneous and fragmented process. To address these challenges, we present a comprehensive framework, HARE, which seamlessly integrates all necessary steps. HARE offers synchronized data collection and labeling, integrated pose estimation for data anonymization, a multimodal classification approach, and a novel method for determining optimal sensor placement to enhance classification results. Additionally, our framework incorporates real-time activity recognition with on-device model adaptation capabilities. To validate the effectiveness of our framework, we conducted extensive evaluations using diverse datasets, including our own collected dataset focusing on nursing activities. Our results show that HARE’s multimodal and on-device trained model outperforms conventional single-modal and offline variants. Furthermore, our vision-based approach for optimal sensor placement yields comparable results to the trained model. Our work advances the field of sensor-based human activity recognition by introducing a comprehensive framework that streamlines data collection and classification while offering a novel method for determining optimal sensor placement.
DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

Predominant techniques on talking head generation largely depend on 2D information, including facial appearances and motions from input face images. Nevertheless, dense 3D facial geometry, such as pixel-wise depth, plays a critical role in constructing accurate 3D facial structures and suppressing complex background noises for generation. However, dense 3D annotations for facial videos is prohibitively costly to obtain. In this paper, first, we present a novel self-supervised method for learning dense 3D facial geometry (i.e., depth) from face videos, without requiring camera parameters and 3D geometry annotations in training. We further propose a strategy to learn pixel-level uncertainties to perceive more reliable rigid-motion pixels for geometry learning. Second, we design an effective geometry-guided facial keypoint estimation module, providing accurate keypoints for generating motion fields. Lastly, we develop a 3D-aware cross-modal (i.e., appearance and depth) attention mechanism, which can be applied to each generation layer, to capture facial geometries in a coarse-to-fine manner. Extensive experiments are conducted on three challenging benchmarks (i.e., VoxCeleb1, VoxCeleb2, and HDTF). The results demonstrate that our proposed framework can generate highly realistic-looking reenacted talking videos, with new state-of-the-art performances established on these benchmarks.
How to Train Your Dragon: Diverse Augmentation Towards Generalizable Dense Retrieval

Various techniques have been developed in recent years to improve dense retrieval (DR), such as unsupervised contrastive learning and pseudo-query generation. Existing DRs, however, often suffer from effectiveness tradeoffs between supervised and zero-shot retrieval, which some argue was due to the limited model capacity. We contradict this hypothesis and show that a generalizable DR can be trained to achieve high accuracy in both supervised and zero-shot retrieval without increasing model size. In particular, we systematically examine the contrastive learning of DRs, under the framework of Data Augmentation (DA). Our study shows that common DA practices such as query augmentation with generative models and pseudo-relevance label creation using a cross-encoder, are often inefficient and sub-optimal. We hence propose a new DA approach with diverse queries and sources of supervision to progressively train a generalizable DR. As a result, DRAGON, our Dense Retriever trained with diverse AuGmentatiON, is the first BERT-base-sized DR to achieve state-of-the-art effectiveness in both supervised and zero-shot evaluations and even competes with models using more complex late interaction.
Dissecting Recall of Factual Associations in Auto-Regressive Language Models

Transformer-based language models (LMs) are known to capture factual knowledge in their parameters. While previous work looked into where factual associations are stored, only little is known about how they are retrieved internally during inference. We investigate this question through the lens of information flow. Given a subject-relation query, we study how the model aggregates information about the subject and relation to predict the correct attribute. With interventions on attention edges, we first identify two critical points where information propagates to the prediction: one from the relation positions followed by another from the subject positions. Next, by analyzing the information at these points, we unveil a three-step internal mechanism for attribute extraction. First, the representation at the last-subject position goes through an enrichment process, driven by the early MLP sublayers, to encode many subject-related attributes. Second, information from the relation propagates to the prediction. Third, the prediction representation “queries” the enriched subject to extract the attribute. Perhaps surprisingly, this extraction is typically done via attention heads, which often encode subject-attribute mappings in their parameters. Overall, our findings introduce a comprehensive view of how factual associations are stored and extracted internally in LMs, facilitating future research on knowledge localization and editing.