Online Template Research Articles

Advanced Object Tracking through Conditional Online Updates and Noise Suppression

Object tracking under complex environmental conditions, such as background clutter, occlusion, and rapid motion, presents significant challenges. This paper addresses these issues by proposing a tracking algorithm integrating background suppression, target region enhancement, and an adaptive online template update mechanism to improve tracking accuracy. The proposed method uses the initial bounding box of the target object as a reference template and selectively updates specific regions online to suppress noise and retain critical features. We evaluated the proposed method using the OTB dataset to validate it. The baseline model without the proposed method showed a success rate of 0.417 and a precision of 0.586, while the algorithm with the proposed method achieved improved values of 0.524 and 0.728, respectively. Qualitative evaluations further confirmed the robustness of the proposed method, demonstrating high performance in scenarios with occlusion and complex backgrounds. Rather than updating all regions indiscriminately, the proposed method selectively updates the template using representative values from the target object's information. This selective update mechanism ensures the incorporation of the most relevant and accurate features, enabling the algorithm to adapt to changes in the target's appearance while minimizing noise integration. Emphasizing the feature regions and suppressing noise are also critical for maintaining a clear and precise representation of the target object, reducing the likelihood of confusion by irrelevant background information. Future research will focus on developing balanced update strategies that integrate new information while maintaining stable and reliable target characteristics.

A lightweight target tracking algorithm based on online correction for meta-learning

The traditional Siamese network based object tracking algorithms suffer from high computational complexity, making them difficult to run on embedded devices. Moreover, when faced with long-term tracking tasks, their success rates significantly decline. To address these issues, we propose a lightweight long-term object tracking algorithm called Meta-Master-based Ghost Fast Tracking (MGTtracker),which based on meta-learning. This algorithm integrates the Ghost mechanism to create a lightweight backbone network called G-ResNet, which accurately extracts target features while operating quickly. We design a tiny adaptive weighted fusion feature pyramid network (TiFPN) to enhance feature information fusion and mitigate interference from similar objects. We introduce a lightweight region regression network, the Ghost Decouple Net (GDNet) for target position prediction. Finally, we propose a meta-learning-based online template correction mechanism called Meta-Master to overcome error accumulation in long-term tracking tasks and the difficulty of reacquiring targets after loss. We evaluate the algorithm on public datasets OTB100, VOT2020, VOT2018LT, and LaSOT and deploy it for performance testing on Jetson Xavier NX. Experimental results demonstrate the effectiveness and superiority of the algorithm. Compared to existing classic object tracking algorithms, our approach achieves a faster running speed of 25 FPS on NX, and real-time correction enhances the algorithm’s robustness. Although similar in accuracy and EAO metrics, our algorithm outperforms similar algorithms in speed and effectively addresses the issues of significant cumulative errors and easy target loss during tracking. Code is released at https://github.com/ygh96521/MGTtracker.git.

A template wizard for the cocreation of machine-readable data-reporting to harmonize the evaluation of (nano)materials.

Making research data findable, accessible, interoperable and reusable (FAIR) is typically hampered by a lack of skills in technical aspects of data management by data generators and a lack of resources. We developed a Template Wizard for researchers to easily create templates suitable for consistently capturing data and metadata from their experiments. The templates are easy to use and enable the compilation of machine-readable metadata to accompany data generation and align them to existing community standards and databases, such as eNanoMapper, streamlining the adoption of the FAIR principles. These templates are citable objects and are available as online tools. The Template Wizard is designed to be user friendly and facilitates using and reusing existing templates for new projects or project extensions. The wizard is accompanied by an online template validator, which allows self-evaluation of the template (to ensure mapping to the data schema and machine readability of the captured data) and transformation by an open-source parser into machine-readable formats, compliant with the FAIR principles. The templates are based on extensive collective experience in nanosafety data collection and include over 60 harmonized data entry templates for physicochemical characterization and hazard assessment (cell viability, genotoxicity, environmental organism dose-response tests, omics), as well as exposure and release studies. The templates are generalizable across fields and have already been extended and adapted for microplastics and advanced materials research. The harmonized templates improve the reliability of interlaboratory comparisons, data reuse and meta-analyses and can facilitate the safety evaluation and regulation process for (nano) materials.

LogCSS: Log anomaly detection based on BERT-CNN with context-semantics-statistics features

With the development of artificial intelligence, deep-learning-based log anomaly detection proves to be an important research topic. In this paper, we propose LogCSS, a novel log anomaly detection framework based on the Context-Semantics-Statistics Convolutional Neural Network (CSSCNN). It is the first model that uses BERT (Bidirectional Encoder Representation from Transformers) and CNN (Convolutional Neural Network) to extract the semantic, temporal, and correlational features of the logs. We combine the features with the statistic information of log templates for the classification model to improve the accuracy. We also propose a technique, DOOT (Deals with the Out-Of-Templates), for online template matching. The experimental research shows that our framework improves the average F1 score of the six best algorithms in the industry by more than 5% on the open-source dataset HDFS, and improves the average F1 score of the six best algorithms in the industry by more than 8% on the BGL dataset, LogCSS also performs better than other similar methods on our own constructed dataset.

A Quality Improvement Project of Acute Red Eye Consultations in Primary Care: Improving the Identification of Red Flags.

Aim The acute red eye or conjunctival injectionis the most common ophthalmic presentation in primary care.The aim of this audit was to improve red eye consultations by increasing the number of red flag features documented in the history of patients. Method The National Institute of Clinical Excellence (NICE) Clinical Knowledge Summary (CKS) outlines red flag features, which should be documented for an acute red eye consultation. Two interventions were implemented to attempt to improve consultations. The first involved creating a template in the Egton Medical Information Systems. The second involved training doctors in the recognition of red flag features and identifying sight-threatening conditions. Red eye consultations and red flag features documented were recorded in the two months before and after the interventions. Results All documentation of red flag features assessed in this project increased post intervention. However, lateralisation, visual changes, and eye pain were commonly asked prior to the intervention and did not show a statistically significant difference. They showed an increase from 90% (19/21) to 100% (19/19), 71% (15/21) to 84% (16/19), and 67% (14/21) to 79% (15/19), respectively. After the interventions, significant increases in asking about headaches (14% (3/21) to 74% (14/19), pupil changes (5% (1/19) to 58% (11/19)), and method of injury (high velocity 10% (2/21) to 84% (16/19), foreign body 14% (3/21) to 84% (16/19), chemical 10% (2/21) to 84% (16/19) were observed (p<0.05). Photophobia inquiries also significantly increased (14% (3/19) to 79% (15/19), P<0.05). Conclusion The number of red flag features identified and documented for acute red eye consultations increased with the introduction of an online template and a teaching session.

Template pool updating and feature combination object tracking

In the target tracking task, most Siamese network target tracking algorithms only use the template information of the first frame and do not make good use of the feature information of the subsequent frames, which shows poor robustness and target drift in dealing with target occlusion and appearance change in complex scenes. In this regard, an online template pool update module is established based on the SiamFC++ algorithm to store the information of reliable template frames, and an accuracy and necessity judgment mechanism of template update is proposed to improve the reliability of the final template. In addition, a feature combination module is proposed to capture the information of different scale regions and obtain more scale information through an asymmetric dilated convolution layer. Experiments and analysis are conducted on OTB2015 and VOT2018 datasets, and the proposed algorithm improves the accuracy and success rate by 1.7% and 1.7%, respectively, on OTB2015 compared with the SiamFC++ algorithm; the accuracy and average overlap rate improve by 0.8% and 6.1%, respectively, on VOT2018, and the robustness decreases from 0.169 to 0.140, and the tracker more stable.

Implementation of the evidence‐based risk assessment for the re‐evaluation of Bisphenol A: preparatory work on cross‐sectional studies.

EFSA is implementing evidence-based risk assessments for the re-evaluation of the safety of bisphenol A (BPA) for human health. The aim of this work was to carry out some preparatory work for the working group on BPA re-evaluation, with particular reference to the preparation of individual summary tables of human studies with cross-sectional design, and a narrative and factual summary of the above studies when grouped by health outcome category and by effect/endpoint. The first stage of the work was conducted using DistillerSR, the web-based systematic review software used by EFSA which enabled direct access/download to the published documents and provided an on-line template for data extraction and reporting. A total of 161 published articles were considered and data extracted from these into the online templates. Using these data, with additional reference back to the published articles, a narrative report was produced that sub-divided the studies into seven key toxicological areas: reproduction, neurotoxicity, immune system toxicity, metabolic effects, cardiovascular effects, carcinogenicity and general toxicity.

Online Template Attacks: Revisited

An online template attack (OTA) is a powerful technique previously used to attack elliptic curve scalar multiplication algorithms. This attack has only been analyzed in the realm of power consumption and EM side channels, where the signals leak related to the value being processed. However, microarchitecture signals have no such feature, invalidating some assumptions from previous OTA works.In this paper, we revisit previous OTA descriptions, proposing a generic framework and evaluation metrics for any side-channel signal. Our analysis reveals OTA features not previously considered, increasing its application scenarios and requiring a fresh countermeasure analysis to prevent it.In this regard, we demonstrate that OTAs can work in the backward direction, allowing to mount an augmented projective coordinates attack with respect to the proposal by Naccache, Smart and Stern (Eurocrypt 2004). This demonstrates that randomizing the initial targeted algorithm state does not prevent the attack as believed in previous works.We analyze three libraries libgcrypt, mbedTLS, and wolfSSL using two microarchitecture side channels. For the libgcrypt case, we target its EdDSA implementation using Curve25519 twist curve. We obtain similar results for mbedTLS and wolfSSL with curve secp256r1. For each library, we execute extensive attack instances that are able to recover the complete scalar in all cases using a single trace.This work demonstrates that microarchitecture online template attacks are also very powerful in this scenario, recovering secret information without knowing a leakage model. This highlights the importance of developing secure-by-default implementations, instead of fix-on-demand ones.

Getting it right first time (GIRFT): A closed loop study of hip arthroplasty documentation in a trauma and orthopaedic department

IntroductionThe objectives of this study conducted in the University Hospital of North Tees, UK were to: (i) Identify if the current hip arthroplasty documentation met good compliance with the Getting It Right First Time (GIRFT) hip arthroplasty guidance (ii) Improve current documentation with a transition from hand-written notes to an online typed personalised operative hip arthroplasty template (iii) Improve the quality of documentation and adherence to GIRFT guidance in hip arthroplasty MethodsWe led a team of 7 doctors to review University Hospital of North Tees compliance against 24 criteria laid out by GIRFT. After examining 20 operative records retrospectively at random from a spread of orthopaedic consultants in the department, it was shown that there was poor compliance against GIRFT guidelines. We proposed a pragmatic solution of incorporating a pre-populated hip arthroplasty online template adhering to GIRFT guidance into our local ‘Trackcare’ system. Following that, we closed the audit loop by prospectively reviewing 20 operative notes. ResultsOur initial results showed that poor compliance ranging 0%–100% over the 24 criteria. The findings of the 24 criteria with the online hip arthroplasty template in place showed a significant improvement between 80 and 100% compliance over the 24 criteria. ConclusionThe majority of the issues identified are modifiable risks factors which were amenable to some simple pragmatic solutions. A review of a single surgeon template has shown that it is simple to use, has excellent compliance (has pre-populated 24 criteria), takes 6–7 min to complete the operative notes, easily auditable and thus appears promising in minimising medico-legal claims for surgeons and the Trust.

A Low-Cost High-Speed Object Tracking VLSI System Based on Unified Textural and Dynamic Compressive Features

Real-time object tracking is demanded in versatile embedded computer vision applications. To this end, a low-cost high-speed VLSI system is proposed for object tracking, based on robust and computationally simple unified textural and dynamic compressive sensing features and simple elliptic matching with fast online template updating capability. The system introduces a memory-centric architectural paradigm, multiple-level pipelines and parallel processing circuits to achieve high frame rate while consuming as few hardware resources as possible. We implemented an FPGA prototype of the proposed VLSI tracking system. Under a 100 MHz clock frequency, the prototype achieves over 600 frame/s processing speed under <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$320\times240$ </tex-math></inline-formula> image resolution and obtains robust tracking results.

Wide-Area Search Tracking for Siamese Region Proposal Network

With the introduction of deep learning technology into the field of visual tracking, the accuracy and robustness of visual tracking have greatly improved. Recently, trackers based on Siamese region proposal networks have attracted a great deal of attention because of their favorable performance, especially when faced with challenges of heavy target deformation and out-of-plane rotation. However, strategies of local-area search and using a fixed template degrade the performance of such networks in the long-term tracking. In this paper, we focus on a wide-area search tracking approach with adaptive template updating for a Siamese region proposal network. By embedding a correlation filter module into the Siamese region proposal networks, a structure of moving anchors distribution is designed to make the anchor regression centered around the target. Through adaptive reliability evaluation and online template update, the discriminative performance of the model improves greatly. Furthermore, in order to address the problem of being misled by false positives, a multi-trajectory tracking mechanism is joined to consistently boost the classification ability. Experiments on OTB100 and VOT short-term benchmarks and UAV123 long-term benchmark show that our tracker significantly outperforms the original algorithm and achieves comparable performance compared with other state-of-the-art trackers.

Improving Communication Between Nursing Staff and On-Call Residents via a Standardized Paging Protocol.

Clear and efficient communication between nursing staff and medical providers is an essential component of healthcare delivery. At McLean Hospital, there is an inconsistency in utilization of alphanumeric paging, with many individuals communicating primarily via numeric-only pages that can cause difficulty in triaging importance of pages and lead to disruptions in care. This quality improvement project sought to improve communication between nursing staff and residents by decreasing the number of difficult to triage pages sent to the psychiatrist-on-call at a stand-alone academic psychiatric hospital. Pages were analyzed during two discrete month-long periods before and after the implementation of a standardized paging protocol, which included an updated online template asking the individual sending the page to include specific information (urgency of page, identifying information of patient, contact information, and name of sender) and dissemination of information on its use. The implementation of this protocol resulted in a statistically significant decrease in the percentage of pages that were difficult to triage (22.1 to 15.0%; p < 0.05). Examining specific units in the hospital revealed significant variation of change, with as much as 40% reduction to as large as an 11% increase in difficult to triage pages. The decrease in the percentage of difficult to triage pages suggests that a standard paging protocol can improve delivery of patient care by minimizing interruptions with low-priority pages and may improve quality of communication between nursing staff and physicians on-call, ultimately improving quality of care provided and bettering the resident learning environment.

Power Analysis on NTRU Prime

This paper applies a variety of power analysis techniques to several implementations of NTRU Prime, a Round 2 submission to the NIST PQC Standardization Project. The techniques include vertical correlation power analysis, horizontal indepth correlation power analysis, online template attacks, and chosen-input simple power analysis. The implementations include the reference one, the one optimized using smladx, and three protected ones. Adversaries in this study can fully recover private keys with one single trace of short observation span, with few template traces from a fully controlled device similar to the target and no a priori power model, or sometimes even with the naked eye. The techniques target the constant-time generic polynomial multiplications in the product scanning method. Though in this work they focus on the decapsulation, they also work on the key generation and encapsulation of NTRU Prime. Moreover, they apply to the ideal-lattice-based cryptosystems where each private-key coefficient comes from a small set of possibilities.

Learning Visual Similarity for Inspecting Defective Railway Fasteners

Vision-based automatic railway fastener inspection, instead of manual operation, remains a great challenge. Even though many supervised learning-based methods have been developed, expensive training labels and imbalanced data are the main obstacles to leverage the performance of the fastener inspection task. To tackle the problems, we present a novel vision-based fastener inspection system (VFIS) which is inspired by few-shot learning. VFIS can automatically collect and annotate a large number of fastener samples by using the proposed online template matching-based classification method, and it only requires a very small number of annotated fastener templates. Moreover, we employ a similarity-based deep network to solve the problem of the imbalanced dataset. The comprehensive experiments are conducted on a large scale fastener dataset. VFIS yields competitive performance on both fastener localization and fastener classification. Specifically, an average detection rate of 99.36% is achieved for fastener localization, and an average accuracy of 92.69% is achieved for fastener classification. Moreover, for identifying defective fasteners, our proposed method achieves an average precision of 92.63% and an average recall of 92.88%.

Online template induction for machine-generated emails

In emails, information abounds. Whether it be a bill reminder, a hotel confirmation, or a shipping notification, our emails contain useful bits of information that enable a number of applications. Most of this email traffic is machine-generated, sent from a business to a human. These business-to-consumer emails are typically instantiated from a set of email templates, and discovering these templates is a key step in enabling a variety of intelligent experiences. Existing email information extraction systems typically separate information extraction into two steps: an offline template discovery process (called template induction) that is periodically run on a sample of emails, and an online email annotation process that applies discovered templates to emails as they arrive. Since information extraction requires an email's template to be known, any delay in discovering a newly created template causes missed extractions, lowering the overall extraction coverage. In this paper, we present a novel system called Crusher that discovers templates completely online, reducing template discovery delay from a week (for the existing MapReduce-based batch system) to minutes. Furthermore, Crusher has a resource consumption footprint that is significantly smaller than the existing batch system. We also report on the surprising lesson we learned that conventional stream processing systems do not present a good framework on which to build Crusher. Crusher delivers an order of magnitude more throughput than a prototype built using a stream processing engine. We hope that these lessons help designers of stream processing systems accommodate a broader range of applications like online template induction in the future.

An Efficient Hardware Architecture for Template Matching-Based Spike Sorting.

This paper presents an efficient hardware architecture for the design and implementation of a spike sorting system using online template matching. Over the past decade, various spike sorting algorithms have been proposed; however, due to their computational complexity, they may not be suitable for implantable devices that have stringent area and power consumption requirements. We first developed a software-based spike sorting system in both floating-point and fixed-point representations. Then, we used our developed software-based spike sorting system for: 1) studying various neural signal processing algorithms and assessing their feasibility for efficient hardware implementations; and 2) offline processing of previously recorded neural data and extracting the threshold data and spike templates for configuring our spike sorting hardware architecture. The characteristics and implementation results of the designed spike sorting system on a Xilinx Artix-7 A200TFBG676-2 field-programmable gate array are presented. The application-specific integrated circuit (ASIC) implementation of the designed spike sorting system is estimated to occupy 0.3 mm2. Postlayout synthesis and simulation shows that the ASIC implementation will dissipate 64 nW from a 0.25-V supply, while operating at a 24-kHz frequency in a standard 45-nm CMOS technology. Compared to the previously published work, our ASIC implementation consumes 96.8% less power, while maintaining a comparable sorting accuracy. Moreover, our design can run at a higher clock frequency and uses fewer hardware resources, while achieving a 168% reduction in output data rate when comparing the raw data sampling rate and the sorted spike output rate and, yet, offers comparable spike sorting accuracy.

Question answering over knowledge graphs

The gap between unstructured natural language and structured data makes it challenging to build a system that supports using natural language to query large knowledge graphs. Many existing methods construct a structured query for the input question based on a syntactic parser. Once the input question is parsed incorrectly, a false structured query will be generated, which may result in false or incomplete answers. The problem gets worse especially for complex questions. In this paper, we propose a novel systematic method to understand natural language questions by using a large number of binary templates rather than semantic parsers. As sufficient templates are critical in the procedure, we present a low-cost approach that can build a huge number of templates automatically. To reduce the search space, we carefully devise an index to facilitate the online template decomposition. Moreover, we design effective strategies to perform the two-level disambiguations (i.e., entity-level ambiguity and structure-level ambiguity) by considering the query semantics. Extensive experiments over several benchmarks demonstrate that our proposed approach is effective as it significantly outperforms state-of-the-art methods in terms of both precision and recall.

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching.

In this paper, we propose a novel matching based tracker by investigating the relationship between template matching and the recent popular correlation filter based trackers (CFTs). Compared to the correlation operation in CFTs, a sophisticated similarity metric termed mutual buddies similarity is proposed to exploit the relationship of multiple reciprocal nearest neighbors for target matching. By doing so, our tracker obtains powerful discriminative ability on distinguishing target and background as demonstrated by both empirical and theoretical analyses. Besides, instead of utilizing single template with the improper updating scheme in CFTs, we design a novel online template updating strategy named memory, which aims to select a certain amount of representative and reliable tracking results in history to construct the current stable and expressive template set. This scheme is beneficial for the proposed tracker to comprehensively understand the target appearance variations, recall some stable results. Both qualitative and quantitative evaluations on two benchmarks suggest that the proposed tracking method performs favorably against some recently developed CFTs and other competitive trackers.

Online template attacks

Template attacks are a special kind of side-channel attacks that work in two stages. In a first stage, the attacker builds up a database of template traces collected from a device which is identical to the attacked device, but under the attacker’s control. In the second stage, traces from the target device are compared to the template traces to recover the secret key. In the context of attacking elliptic curve scalar multiplication with template attacks, one can interleave template generation and template matching and reduce the amount of template traces. This paper enhances the power of this technique by defining and applying the concept of online template attacks, a general attack technique with minimal assumptions for an attacker, who has very very limited control over the template device. We show that online template attacks need only one power consumption trace of a scalar multiplication on the target device; they are thus suitable not only against ECDSA and static elliptic curve Diffie–Hellman (ECDH), but also against elliptic curve scalar multiplication in ephemeral ECDH. In addition, online template attacks need only one template trace per scalar bit and they can be applied to a broad variety of scalar multiplication algorithms. To demonstrate the power of online template attacks, we recover scalar bits of a scalar multiplication using the double-and-add-always algorithm on a twisted Edwards curve running on a smartcard with an ATmega163 CPU.

A method to integrate an institutional database with multiple practice EHRs.

248 Background: Survivorship care plans (SCP) are endorsed or required for accreditation by many organizations including ASCO, NCCN and ACoS. SCP elements include a treatment summary and follow up plan. Information for these elements is usually found in a practice’s electronic health record (EHR), but is not always readily identifiable. Practitioners get information from multiple EHRs across practices and hospitals. Barriers include EHRs not readily accessible to all staff or don’t communicate with each other. No simple means exists for the required data points to auto-populate from practice-based databases. Methods: We approached this by combining Cancer Registry data with an online template to populate a SCP, utilizing the Electronic Registry System, Inc. (ERS), a Cancer Registry software company, and a survivorship template provided by a free online open source company Journey Forward (JF). The Registry is told when a patient is scheduled for a survivorship appointment. A file is created of the patient’s demographics, specific cancer characteristics, and treatment summary. This is sent to the practitioner through a shared drive or secure email. The tumor site-specific JF template is then selected by practice staff who import the Registry file created from ERS. Information required for the SCP is auto-populated in the template during this process. Results: The remainder of the template contains recommendations for follow up and is completed by a physician or designated practitioner working with their EHR. Supplemental tumor site-specific education materials are available in the JF Library. Patient-specific concerns such as cancer recurrence or treatment side-effects can be addressed as well. Any of these prepared documents may be incorporated into the SCP follow up recommendation section with a mouse-click. The printed SCP is given to the patient and forwarded to care providers. A notice of SCP given to the patient is then sent to the Registry which closes the loop. Conclusions: This process utilizes the Cancer Registry, allows specific input from patient care providers, and eliminates the necessity to access multiple EHRs to complete SCPs.