Reviewerly/exHarmony huggingface dataset:
Secure Your Steps: A Class-Based Ensemble Framework for Real-Time Fall Detection Using Deep Neural Networks

Falls represent a significant public health concern, particularly concerning vulnerable populations such as older adults. Accurate detection and classification of falls are critical for timely interventions that can prevent injuries and enhance the quality of life of these individuals. This work proposes a class ensemble approach based on convolutional neural networks and long short-term memory networks for three-class classifications of falling processes (non-fall, pre-fall, and fall) using accelerometer and gyroscope data. The research is conducted on the SisFall and UMAFall datasets, the publicly available dataset of annotated video recordings of falls and non-falls. This approach leverages convolutional neural networks for robust feature extraction from the accelerometer and gyroscope data. In addition, long short-term memory networks model the falling process’s temporal dynamics. The proposed approach has demonstrated state-of-the-art performance in detecting falls, with accuracy rates of 96.45% and 96.12% and precision scores of 98.12% and 97.45% in identifying pre-fall and fall states, respectively.
Transformer Meets Remote Sensing Video Detection and Tracking: A Comprehensive Survey

Transformer has shown excellent performance in remote sensing field with long-range modeling capabilities. Remote sensing video (RSV) moving object detection and tracking play indispensable roles in military activities as well as urban monitoring. However, transformers in these fields are still at the exploratory stage. In this survey, we comprehensively summarize the research prospects of transformers in RSV moving object detection and tracking. The core designs of remote sensing transformers and advanced transformers are first analyzed. It mainly includes the attention mechanism evolution for specific tasks, the fitting ability design of input mapping, diverse feature representation, model optimization, etc. The architectural characteristics of RSV detection and tracking are then described across two aspects. One is moving object detection for motion-based traditional background subtractions and appearance-based deep learning models. The other is object tracking for single and multi-targets. The research difficulties mainly include the blurred foreground in RSV data, the irregular objects movement in traditional background subtraction and the severe objects occlusion in object tracking. Following that, the potential significance of transformers is discussed according to some thorny problems in RSV. Finally, we summarize ten open challenges of transformers in RSV, which may be used as a reference for promoting future research.
Smart Healthcare Hand Gesture Recognition Using CNN-Based Detector and Deep Belief Network

Gesture recognition in dynamic images is challenging in computer vision, automation and medical field.Hand gesture tracking and recognition between both human and computer must have symmetry in real world.With advances in sensor technology, numerous researchers have recently proposed RGB gesture recognition techniques.In our research paper, we introduce a reliable hand gesture tracking and recognition model that is accurate despite any complex environment, it can track and recognise RGB dynamic gestures.Firstly, videos are converted into frames.After images light intensity adjustment and noise removal, images are passed through CNN for hand gesture extraction.Then from the extracted hand, features are extracted from full hand.Neural gas and locomotion thermal mapping are extracted to make the feature vector.The feature vector are then passed through the fuzzy optimiser to reduce the uncertainties and the fuzziness.The optimised features are then passed to the classifier Deep Belief Network (DBW) for the classification of the gestures.Egogesture and Jester datasets are used for the validation of proposed systems.The experimental results over Egogesture and Jester datasets demonstrate overall accuracies of 90.73% and 89.33% respectively.The experiments proves our system readability and suitability of our proposed model with the other state of the arts model.
HTTP Cookie Covert Channel Detection Based on Session Flow Interaction Features

HTTP cookie covert channel is a covert communication method that encodes malicious information in cookie fields to escape regulatory audits. It is difficult to detect this kind of covert channel according to the cookie content because cookie fields are mainly encoded in custom modes. To effectively identify the HTTP cookie covert channel, this paper proposes a detection method based on the interaction features of the session flow. First, we split the HTTP session flow into fine-grained “interaction process” subflows to comprehensively describe the communication process of the cookie. Then, we compare and analyze the differences between HTTP cookie covert channels and normal cookie communications based on the interaction process, design three types of 7-dimensional features, and build the detection model combined with the machine learning algorithm. Experimental results show that our method can effectively detect HTTP cookie covert channels, and the detection rate can reach 99%. We also prove that our method has advantages in stability and time performance compared with the existing detection methods through experiment and analysis. In addition, our method has certain practicability in the simulation environment with imbalanced data.
Contrastive Learning with Prototype-Based Negative Mixing for Satellite Telemetry Anomaly Detection

Telemetry data are the most important basis for ground operators to assess the status of satellites in orbit, and telemetry data-based anomaly detection has become a key tool to improve the reliability and safety of spacecrafts. Recent research on anomaly detection focuses on constructing a normal profile of telemetry data using deep learning methods. However, these methods cannot effectively capture the complex correlations between the various dimensions of telemetry data, and thus cannot accurately model the normal profile of telemetry data, resulting in poor anomaly detection performance. This paper presents CLPNM-AD, contrastive learning with prototype-based negative mixing for correlation anomaly detection. The CLPNM-AD framework first employs an augmentation process with random feature corruption to generate augmented samples. Following that, a consistency strategy is employed to capture the prototype of samples, and then prototype-based negative mixing contrastive learning is used to build a normal profile. Finally, a prototype-based anomaly score function is proposed for anomaly decision-making. Experimental results on public datasets and datasets from the actual scientific satellite mission show that CLPNM-AD outperforms the baseline methods, achieves up to 11.5% improvement based on the standard F1 score and is more robust against noise.
OASIS: one resource to widen the reach of research in language studies

Language teachers report having limited direct contact with research and research findings despite generally positive perceptions of research (Borg 2009; Marsden and Kasprowicz 2017; Nassaji 2012). Key reasons teachers give are 1) practical - a lack of time and access, and 2) conceptual - academic papers can be difficult to read (Plavén-Sigray et al. 2017). In turn, researchers worry their research is not (or does not need to be) relevant to pedagogy. This paper reports on one large-scale initiative to bridge some research-pedagogy divides: Open Accessible Summaries in Language Studies (OASIS; oasis-database.org). OASIS aims to facilitate interaction between research and pedagogy, by making research into language learning, language teaching, and multilingualism physically and conceptually accessible. The freely available, one-page OASIS summaries, written in non-technical language, provide information about what the study was about, its importance, what the researcher(s) did and found. Four major international journals now require authors to write these summaries, and many others encourage their authors to write them. We discuss how and why OASIS was established, how the initiative has been received, and how summaries have contributed to professional development activities. We also and highlight some of the challenges we have encountered and discuss future directions.
An Enhanced Deep Learning Model for Obstacle and Traffic Light Detection Based on YOLOv5

Timely detection of dynamic and static obstacles and accurate identification of signal lights using image processing techniques is one of the key technologies for guidance robots and is a necessity to assist blind people with safe travel. Due to the complexity of real-time road conditions, current obstacle and traffic light detection methods generally suffer from missed detection and false detection. In this paper, an improved deep learning model based on YOLOv5 is proposed to address the above problems and to achieve more accurate and faster recognition of different obstacles and traffic lights that the blind may encounter. In this model, a coordinate attention layer is added to the backbone network of YOLOv5 to improve its ability to extract effective features. Then, the feature pyramid network in YOLOv5 is replaced with a weighted bidirectional feature pyramid structure to fuse the extracted feature maps of different sizes and obtain more feature information. Finally, a SIoU loss function is introduced to increase the angle calculation of the frames. The proposed model’s detection performance for pedestrians, vehicles, and traffic lights under different conditions is tested and evaluated using the BDD100K dataset. The results show that the improved model can achieve higher mean average precision and better detection ability, especially for small targets.
Multicriteria Ordered the Profile Clustering Algorithm Based on PROMETHEE and Fuzzy c-Means

The purpose of multicriteria clustering is to locate groups of alternatives that have comparable qualities and have been examined across multiple criteria. An ordered profile clustering is a well-known problem, and the fuzzy c-means clustering (FCM) technique is one of the most broadly used in every field of life. At present, FCM is for the partitioning of information into numerous clusters which are still lacking priority relations. To address the problem of finding ranking in clusters based on multicriteria in the fuzzy environment, we propose a multicriteria ordered clustering algorithm based on the partial net outranking flow of the preference organization for enrichment evaluations method (PROMETHEE) and fuzzy c-means. Lastly, we apply the proposed algorithm to solve a real-world targeted clustering problem regarding the human development indexes. To test the efficacy of the proposed algorithm, a comparative analysis of ordered K-means clustering (OKM) and FCM is carried out with it.
Large-scale neural biomedical entity linking with layer overwriting

Entity linking is the task of linking entity mentions to the database entries corresponding to the entity mentions. Entity linking enables the treatment of superficially different but semantically identical mentions as the same entity. Since millions of concepts are listed in biomedical databases, selecting the correct database entry for each targeted entity is challenging. Simple string matching between the word and each synonym in biomedical databases is insufficient to handle a wide variety of variants of biomedical entities appearing in the biomedical literature. Recent progress in neural approaches is promising for entity linking. Still, existing neural methods require sufficient data, which is difficult to prepare in biomedical entity linking that deals with millions of biomedical concepts. Therefore, we need to develop a new neural method to train entity-linking models over the sparse training data covering a very limited part of the biomedical concepts.We have devised a pure neural model that classifies biomedical entity mentions into millions of biomedical concepts. The classifier employs (1) the layer overwriting that breaks through the performance ceiling during training, (2) training data augmentation using database entries that compensate for the problem of insufficient training data, and (3) the cosine similarity-based loss function that helps distinguish the millions of biomedical concepts. Our system using the proposed classifier was ranked first in the official run of the National NLP Clinical Challenges (n2c2) 2019 Track 3, which targeted linking medical/clinical entity mentions to 434,056 Concept Unique Identifier (CUI) entries. We also applied our system to the MedMentions dataset, which has 3.2M candidate concepts. Experimental results confirmed the same advantages of our proposed method. We further evaluated our system on the NLM-CHEM corpus with 350K candidate concepts, and our system achieved a new state-of-the-art performance on the corpus.https://github.com/tti-coin/bio-linking Contact:makoto.miwa@toyota-ti.ac.jp.
From necessity to opportunity: lessons for integrating phone and in-person data collection

Abstract The Coronavirus disease pandemic has disrupted survey systems globally and especially in low- and middle-income countries. Phone surveys started being implemented at a national scale in many places that previously had limited experience with them. As in-person data collection resumes, the experience gained provides the grounds to reflect on how phone surveys may be incorporated into survey and data systems in low- and middle-income countries. Reviewing evidence and experiences from before and during the pandemic, the paper provides guidance on the scope of and considerations for using phone surveys for agricultural data collection and the integration of phone interviews with in-person data.
EA-ConvNeXt: An Approach to Script Identification in Natural Scenes Based on Edge Flow and Coordinate Attention

In multilingual scene text understanding, script identification is an important prerequisite step for text image recognition. Due to the complex background of text images in natural scenes, severe noise, and common symbols or similar layouts in different language families, the problem of script identification has not been solved. This paper proposes a new script identification method based on ConvNext improvement, namely EA-ConvNext. Firstly, the method of generating an edge flow map from the original image is proposed, which increases the number of scripts and reduces background noise. Then, based on the feature information extracted by the convolutional neural network ConvNeXt, a coordinate attention module is proposed to enhance the description of spatial position feature information in the vertical direction. The public dataset SIW-13 has been expanded, and the Uyghur script image dataset has been added, named SIW-14. The improved method achieved identification rates of 97.3%, 93.5%, and 92.4% on public script identification datasets CVSI-2015, MLe2e, and SIW-13, respectively, and 92.0% on the expanded dataset SIW-14, verifying the superiority of this method.
Improving Distantly Supervised Relation Extraction by Natural Language Inference

To reduce human annotations for relation extraction (RE) tasks, distantly supervised approaches have been proposed, while struggling with low performance. In this work, we propose a novel DSRE-NLI framework, which considers both distant supervision from existing knowledge bases and indirect supervision from pretrained language models for other tasks. DSRE-NLI energizes an off-the-shelf natural language inference (NLI) engine with a semi-automatic relation verbalization (SARV) mechanism to provide indirect supervision and further consolidates the distant annotations to benefit multi-classification RE models. The NLI-based indirect supervision acquires only one relation verbalization template from humans as a semantically general template for each relationship, and then the template set is enriched by high-quality textual patterns automatically mined from the distantly annotated corpus. With two simple and effective data consolidation strategies, the quality of training data is substantially improved. Extensive experiments demonstrate that the proposed framework significantly improves the SOTA performance (up to 7.73% of F1) on distantly supervised RE benchmark datasets. Our code is available at https://github.com/kangISU/DSRE-NLI.
Leveraging Sparse Auto-Encoding and Dynamic Learning Rate for Efficient Cloud Workloads Prediction

Cloud computing provides simple on-demand access to a centralized shared pool of computing resources.Performance and efficient utilization of cloud computing resources require accurate prediction of cloud workloads.This is a challenging problem due to cloud workloads' dynamic nature, making it difficult to predict.Here we leverage deep learning which can with proper training provide accurate bases for the prediction of data center workloads.Deep Learning (DL) models, however, are challenging to train.One challenge is the vast number of hyperparameters needed to define and tune.The performance of a neural network model can be significantly improved by optimizing these hyperparameters.We recognize two of the essential issues to predict data center workloads using deep learning efficiently.First is the high dimensionality which requires removing superfluous information via some form of dimension reduction.Secondly, is the learning rate.Small learning rates can make the time for training very excessive while long learning rates can miss optimal solutions.Our approach is therefore dual-pronged.First, we use Sparse Auto-Encoder (SAE) to retrieve the essential workloads representations from the original high-dimensional historical cloud workloads data.Secondly, we use Gated Recurrent Unit with a Step-Wise Scheduler for the Learning Decay (GRU-SWSLD).The proposed system is demonstrated with data from Google cluster workload traces to predict Central Processing Unit (CPU) usage using the data center's workload traces at several consecutive time steps.Our experimental results reveal that our proposed methodology provides a better tradeoff between accuracy and training time when compared with other models.
Time Series Anomaly Detection Using Transformer-Based GAN With Two-Step Masking

Time series anomaly detection is a task that determines whether an unseen signal is normal or abnormal, and it is a crucial function in various real-world applications.Typical approach is to learn normal data representation using generative models, like Generative Adversarial Network (GAN), to discriminate between normal and abnormal signals.Recently, a few studies actively adopt Transformer to model time series data, but there is no pure Transformer-based GAN framework for time series anomaly detection.As a pioneer work, we propose a new pure Transformer-based GAN framework, called AnoFormer, and its effective training strategy for better representation learning.Specifically, we improve the detection ability of our model by introducing two-step masking strategies.The first step is Random masking: we design a random mask pool to hide parts of the signal randomly.This allows our model to learn the representation of normal data.The second step is Exclusive and Entropy-based Re-masking: we propose a novel refinement step to provide feedback to accurately model the exclusive and uncertain parts in the first step.We empirically demonstrate the effectiveness of re-masking step that generates more normal-like signals robustly.Extensive experiments on various datasets show that AnoFormer significantly outperforms the state-of-the-art methods in time series anomaly detection.
Scalable Empirical Dynamic Modeling With Parallel Computing and Approximate k-NN Search

Empirical Dynamic Modeling (EDM) is a mathematical framework for modeling and predicting non-linear time series data. Although EDM is increasingly adopted in various research fields, its application to large-scale data has been limited due to its high computational cost. This article presents kEDM, a high-performance implementation of EDM for analyzing large-scale time series datasets. kEDM adopts the Kokkos performance-portable programming model to efficiently run on both CPU and GPU while sharing a single code base. We also conduct hardware-specific optimization of performance-critical kernels. kEDM achieved up to 6.58× speedup in pairwise causal inference of real-world biology datasets compared to an existing EDM implementation. Furthermore, we integrate multiple approximate k-NN search algorithms into EDM to enable the analysis of extremely large datasets that were intractable with conventional EDM based on exhaustive k-NN search. EDM-based time series forecast enhanced with approximate k-NN search demonstrated up to 790× speedup compared to conventional Simplex projection with less than 1% increase in MAPE.
CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking With Camera-LiDAR Fusion

3D Multi-object tracking (MOT) ensures consistency during continuous dynamic detection, conducive to subsequent motion planning and navigation tasks in autonomous driving. However, camera-based methods suffer in the case of occlusions and it can be challenging to track the irregular motion of objects for LiDAR-based methods accurately. Some fusion methods work well but do not consider the untrustworthy issue of appearance features under occlusion. At the same time, the false detection problem also significantly affects tracking. As such, we propose a novel camera-LiDAR fusion 3D MOT framework based on Combined Appearance-Motion Optimization (CAMO-MOT), which uses both camera and LiDAR data and significantly reduces tracking failures caused by occlusion and false detection. For occlusion problems, we are the first to propose an occlusion head to select the best object appearance features multiple times effectively, reducing the influence of occlusions. To decrease the impact of false detection in tracking, we design a motion cost matrix based on confidence scores which improve the positioning and object prediction accuracy in 3D space. As existing multi-object tracking methods always evaluate each category separately and do not consider the mismatch between objects of different categories, we also propose to build a multi-category cost to implement multi-object tracking in multi-category scenes. A series of validation experiments are conducted on the KITTI and nuScenes tracking benchmarks. Our proposed method achieves state-of-the-art performance with 79.99% HOTA and the lowest identity switches (IDS) value (23 for Car and 137 for Pedestrian) among all multi-modal MOT methods on the KITTI test dataset. And our method achieves state-of-the-art performance among all algorithms on the nuScenes test dataset with 75.3% AMOTA.