TwinCons: Conservation score for uncovering deep sequence similarity and divergence. by Petar I Penev, Claudia Alvarez-Carreño, Eric Smith, Anton S Petrov, Loren Dean Williams

“…TwinCons detects a strong sequence conservation signal between bacterial and archaeal rProteins related by circular permutation. …”
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Naming the untouchable – environmental sequences and niche partitioning as taxonomical evidence in fungi by Faheema Kalsoom Khan, Kerri Kluting, Jeanette Tångrot, Hector Urbina, Tea Ammunet, Shadi Eshghi Sahraei, Martin Rydén, Martin Ryberg, Anna Rosling

“…While environmental sequences cannot be automatically translated to species, they can be used to generate phylogenetically distinct species hypotheses that can be further tested using sequences as ecological evidence. …”
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PolyReg: Autoregressive Building Outline Regularization via Masked Attention Sequence Generation by Longfei Cui, Chao Li, Xin Chen, Xiao Wang, Haizhong Qian

“…High-resolution remote sensing imagery has become the primary data source for obtaining building information. Automatically extracting regularized building outline polygon vectors is crucial for improving vector mapping efficiency and geographic information system applications, but existing deep learning methods struggle to simultaneously achieve accurate detection, high pixel-level coverage, and geometric regularity. …”
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The Automatic Joint Teeth Segmentation in Panoramic Dental Images using Mask Recurrent Convolutional Neural Networks with Residual Feature Extraction: by Raghavendra H. Bhalerao, Abhijeet Ashok Salunke, Shristi Sharan, Kamlesh Kumar, Priyank Rathod, Prince Kumar, Manish Chaturvedi, Nandlal Bharwani, Krupa Shah, Dhruv Patel, Keval Patel, Vikas Warikoo, Manisha Abhijeet Salunke, Shashank Pandya

“…Material and Methods In this study, a sequence of residual blocks are used to construct a 62-layer feature extraction network in lieu of ResNet50/101 in MRCNN. …”
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Evaluation of non-motor symptoms in Parkinson’s disease using multiparametric MRI with the multiplex sequence by He Sui, Zhanhao Mo, Feng Shi, Qing Zhou, Dan Yu, Jiaqi Wang, Lin Liu

“…BackgroundNon-motor symptoms (NMS) in Parkinson’s disease (PD) often precede motor manifestations and are challenging to detect with conventional MRI. This study investigates the use of the Multi-Flip-Angle and Multi-Echo Gradient Echo Sequence (MULTIPLEX) in MRI to detect previously undetectable microstructural changes in brain tissue associated with NMS in PD.MethodsA prospective study was conducted on 37 patients diagnosed with PD. …”
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ULTRASOUND VIDEO ANALYSIS FOR UNDERSTANDING INFANT BREASTFEEDING by G. Monaci, M. Woolridge

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ATM: Adaptive transformer model for reconstruction of remote sensing multi-temporal non-equispaced sequence imagery by Zhi-peng Qi, Zi-yi Zhao, Xing Jin

“…Firstly, the input data is encoded as a sequence of vectors, each representing a local feature of the data. …”
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Development of a Training Station for the Orientation of Dice Parts with Machine Vision by Penko Mitev

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Benchmarking Anomaly Detection Methods for Extracardiac Findings in Cardiac MRI by Edgar Pinto, Patrícia M. Costa, Catarina Silva, Vitor H. Pereira, Jaime C. Fonseca, Sandro Queirós

“…These sequences provide a large field of view, enabling the detection of extracardiac findings (ECFs). …”
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Bottom Detection Method of Side-Scan Sonar Image for AUV Missions by Huapeng Yu, Ziyuan Li, Dailin Li, Tongsheng Shen

“…Next, spatial-temporal matching calculations are performed on each ping port and starboard data, and the accurate coordinates of first bottom returns are obtained through extreme value detection. Finally, automatic and accurate detection of the bottom line is realized according to the smooth processing of the coordinate sequence of first bottom returns. …”
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Violence Detection From Industrial Surveillance Videos Using Deep Learning by Hamza Khan, Xiaohong Yuan, Letu Qingge, Kaushik Roy

“…The integration of Internet of Things (IoT) technology in industrial surveillance and the proliferation of surveillance cameras in smart cities has empowered the development of real-time activity recognition and violence detection systems, respectively. These systems are crucial in enhancing safety measures, improving operational efficiency, reducing accident risks, and providing automatic monitoring in dynamic environments. …”
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SPARTA: Interpretable functional classification of microbiomes and detection of hidden cumulative effects. by Baptiste Ruiz, Arnaud Belcour, Samuel Blanquart, Sylvie Buffet-Bataillon, Isabelle Le Huërou-Luron, Anne Siegel, Yann Le Cunff

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Automated marker detection and time series analysis for through-water photogrammetry by M. Russo, L. Martelli, R. Ravanelli, A. Pini

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Abnormal Behavior Detection Using Trajectory Analysis in Camera Sensor Networks by Yong Wang, Dianhong Wang, Fenxiong Chen

“…First, target trajectories are reconstructed and represented as symbol sequences. Second, the sequences are taken into account using Markov model for building the transition probability matrix which can be used to automatically analyze abnormal behavior. …”
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Detecting Changeover Events on Manufacturing Machines with Machine Learning and NC data by Bastian Engelmann, Anna-Maria Schmitt, Moritz Heusinger, Vladyslav Borysenko, Niklas Niedner, Jan Schmitt

“…Changeover times must be acquired with a high degree of validity for product cost calculations, order sequencing, and work schedules. The novelty of this article is a Machine Learning (ML) approach to automatically detect changeover events in production on manufacturing machines without direct human feedback. …”
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DeepRD:LSTM-based Siamese network for Android repackaged applications detection by Run WANG, Benxiao TANG, Li’na WANG

“…The state-of-art techniques in Android repackaging detection relied on experts to define features,however,these techniques were not only labor-intensive and time-consuming,but also the features were easily guessed by attackers.Moreover,the feature representation of applications which defined by experts cannot perform well to the common types of repackaging detection,which caused a high false negative rate in the real detection scenario.A deep learning-based repackaged applications detection approach was proposed to learn the program semantic features automatically for addressing the above two issues.Firstly,control and data flow analysis were taken for applications to form a sequence feature representation.Secondly,the sequence features were transformed into vectors based on word embedding model to train a Siamese LSTM network for automatically program feature learning.Finally,repackaged applications were detected based on the similarity measurement of learned program features.Experimental results show that the proposed approach achieves a precision of 95.7% and false negative rate of 6.2% in an open sourced dataset AndroZoo.…”
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Time-domain brain: temporal mechanisms for brain functions using time-delay nets, holographic processes, radio communications, and emergent oscillatory sequences by Janet M. Baker, Peter Cariani

“…This sequence corresponds to stages of speech-language processing (sound/speech detection, acoustic-phonetics, phone/clusters, syllables, words/phrases, word sequences/sentences, and concepts/understanding). …”
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Quasi-ADS-B Based UAV Conflict Detection and Resolution to Manned Aircraft by Chin E. Lin, Ya-Hsien Lai

“…A Conflict Detection and Resolution (CD&R) system for manned/unmanned aerial vehicle (UAV) based on Automatic Dependent Surveillance-Broadcast (ADS-B) concept is designed and verified in this paper. …”
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A Credit Card Fraud Detection Algorithm Based on SDT and Federated Learning by Yuxuan Tang, Zhanjun Liu

“…Thanks to the attention mechanism of the Transformer, the model can automatically highlight important features in the data, significantly improving the accuracy of fraud detection. …”
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Improving Object Detection for Time-Lapse Imagery Using Temporal Features in Wildlife Monitoring by Marcus Jenkins, Kirsty A. Franklin, Malcolm A. C. Nicoll, Nik C. Cole, Kevin Ruhomaun, Vikash Tatayah, Michal Mackiewicz

“…Traditional methods, which require extensive fieldwork, are increasingly being supplemented by time-lapse camera-trap imagery combined with an automatic analysis of the image data. The latter usually involves some object detector aimed at detecting relevant targets (commonly animals) in each image, followed by some postprocessing to gather activity and population data. …”
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