Alignment Approach Research Articles

Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach

Data scarcity hinders the usability of data-dependent algorithms when tackling IoT intrusion detection (IID). To address this, we utilise the data rich network intrusion detection (NID) domain to facilitate more accurate intrusion detection for IID domains. In this paper, a Geometric Graph Alignment (GGA) approach is leveraged to mask the geometric heterogeneities between domains for better intrusion knowledge transfer. Specifically, each intrusion domain is formulated as a graph where vertices and edges represent intrusion categories and category-wise interrelationships, respectively. The overall shape is preserved via a confused discriminator incapable to identify adjacency matrices between different intrusion domain graphs. A rotation avoidance mechanism and a centre point matching mechanism is used to avoid graph misalignment due to rotation and symmetry, respectively. Besides, category-wise semantic knowledge is transferred to act as vertex-level alignment. To exploit the target data, a pseudo-label election mechanism that jointly considers network prediction, geometric property and neighbourhood information is used to produce fine-grained pseudo-label assignment. Upon aligning the intrusion graphs geometrically from different granularities, the transferred intrusion knowledge can boost IID performance. Comprehensive experiments on several intrusion datasets demonstrate state-of-the-art performance of the GGA approach and validate the usefulness of GGA constituting components.

Deep Active Alignment of Knowledge Graph Entities and Schemata

Knowledge graphs (KGs) store rich facts about the real world. In this paper, we study KG alignment, which aims to find alignment between not only entities but also relations and classes in different KGs. Alignment at the entity level can cross-fertilize alignment at the schema level. We propose a new KG alignment approach, called DAAKG, based on deep learning and active learning. With deep learning, it learns the embeddings of entities, relations and classes, and jointly aligns them in a semi-supervised manner. With active learning, it estimates how likely an entity, relation or class pair can be inferred, and selects the best batch for human labeling. We design two approximation algorithms for efficient solution to batch selection. Our experiments on benchmark datasets show the superior accuracy and generalization of DAAKG and validate the effectiveness of all its modules.

Performance evaluation of coarse alignment methods for autonomous underwater vehicles in mooring conditions

The coarse alignment stage of inertial navigation systems (INS) is a step of paramount importance, specially for vehicles that employ the latter as a reference navigation solution, as is the case of autonomous underwater vehicles (AUVs). For such class of vehicles, the alignment has encumbrances that goes beyond those usually reported for terrestrial and aerial systems, as AUVs generally align at non benign environments (e.g., under swaying and mooring conditions) and have limited auxiliary navigation sensors. This work, hence, proposes a comparative analysis of the most representative coarse alignment methods applied to AUVs in mooring condition. The alignment approach herein explored, throughout its optimized and non-optimized versions, is the attitude decomposition-based initial alignment (ADIA) method, and the proposed comparison procedure tests different types of observation vectors resolved in the frames of interest. Experimental tests are conducted, which are based on kinematic data collected from an unmanned marine vehicle representing the typical swaying and attitude oscillations of AUVs in the mooring state, i.e., when subject to wave induced disturbances. Results lead to practical conclusions that can be applied to the AUV initial attitude estimation and are related to autonomy, accuracy and precision of the methods, as well as to their convergence rate and computational effort.

MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM.

Multimodal microscopy experiments that image the same population of cells under different experimental conditions have become a widely used approach in systems and molecular neuroscience. The main obstacle is to align the different imaging modalities to obtain complementary information about the observed cell population (e.g., gene expression and calcium signal). Traditional image registration methods perform poorly when only a small subset of cells are present in both images, as is common in multimodal experiments. We cast multimodal microscopy alignment as a cell subset matching problem. To solve this non-convex problem, we introduce an efficient and globally optimal branch-and-bound algorithm to find subsets of point clouds that are in rotational alignment with each other. In addition, we use complementary information about cell shape and location to compute the matching likelihood of cell pairs in two imaging modalities to further prune the optimization search tree. Finally, we use the maximal set of cells in rigid rotational alignment to seed image deformation fields to obtain a final registration result. Our framework performs better than the state-of-the-art histology alignment approaches regarding matching quality and is faster than manual alignment, providing a viable solution to improve the throughput of multimodal microscopy experiments.

Mitigating Weight Stigma in Long-Term Care With Trauma-Informed Principles

Mitigating Weight Stigma in Long-Term Care With Trauma-Informed Principles

Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection.

Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

Enhancing P300-Based Brain-Computer Interfaces with Hybrid Transfer Learning: A Data Alignment and Fine-Tuning Approach

A new approach is introduced to address the subject dependency problem in P300-based brain-computer interfaces (BCI) by using transfer learning. The occurrence of P300, an event-related potential, is primarily associated with changes in natural neuron activity and elicited in response to infrequent stimuli, which can be monitored non-invasively through an electroencephalogram. However, implementing P300-based BCI in real-time requires many training samples and time-consuming calibration, making it challenging to use in practical applications. To tackle these challenges, the proposed approach harnesses the high-level feature extraction capability of a deep neural network, achieved through fine-tuning. To ensure similar distributions of feature extraction data, the approach of aligning data in Euclidean space is employed, which is then applied to a discriminatively restricted Boltzmann machine with a single layer for P300 detection. The performance of the proposed method on the BCI Competition III dataset II and the BCI competition II dataset II, the state-of-the-art dataset, was evaluated and compared with previous studies. The results showed that robust performance could be achieved using a small number of training samples, demonstrating the effectiveness of the transfer learning approach in P300-based BCI applications.

Team Composition Revisited: Expanding the Team Member Attribute Alignment Approach to Consider Patterns of More Than Two Attributes

The attribute alignment approach to team composition allows researchers to assess variation in team member attributes, which occurs simultaneously within and across individual team members. This approach facilitates the development of theory testing the proposition that individual members are themselves complex systems comprised of multiple attributes and that the configuration of those attributes affects team-level processes and outcomes. Here, we expand this approach, originally developed for two attributes, by describing three ways researchers may capture the alignment of three or more team member attributes: (a) a geometric approach, (b) a physical approach accentuating ideal alignment, and (c) an algebraic approach accentuating the direction (as opposed to magnitude) of alignment. We also provide examples of the research questions each could answer and compare the methods empirically using a synthetic dataset assessing 100 teams of three to seven members across four attributes. Then, we provide a practical guide to selecting an appropriate method when considering team-member attribute patterns by answering several common questions regarding applying attribute alignment. Finally, we provide code ( https://github.com/kjem514/Attribute-Alignment-Code ) and apply this approach to a field data set in our appendices.

MANSOR: A module alignment method based on neighbor information for scientific workflow

SummaryFinding similar scientific workflow modules that can substitute essential components of the privacy workflows from public repositories to create a personalized workflow is growing in popularity. This is a cost‐effective and error‐free strategy for the scientific community. Currently, module alignment approaches heavily depend on syntactic information such as modules' names and types. However, the contextual semantic information of modules, which encompasses inputs, outputs, and datalinks connecting modules, can also convey their functions. Unfortunately, this information is scarcely utilized during the module alignment process. In this work, we propose a module alignment method based on neighbor information for scientific workflow (MANSOR). Specifically, we present a rule‐based attribute similarity computation approach for calculating initial module‐pair similarity in two workflows. The relation similarity is then employed to iteratively fine‐tune the matching degree with uncertainty of module pairs by considering the contextual semantics until reaching a steady state. After pairwise comparison of workflows, the module alignment results are determined based on their module‐pair similarity. Experimental results on the human‐curated corpus of ratings indicate that MANSOR outperforms the existing state‐of‐the‐art approaches with statistical significance.

A Multi-objective approach for position alignment of track geometry measurements

This study aimed to develop a multi-objective approach for reducing the positional errors in geometry measurements of track as a linear asset. Accordingly, we evaluated and compared two alignment methods – recursive segment-wise peak alignment (RSPA) and modified correlation optimised warping (MCOW). Furthermore, a novel rule-based approach was introduced to avoid data loss while aligning the datasets of the measurements of linear assets. A case study was conducted to implement and assess the performance of these methods in reducing the positional errors in track geometry measurements. The results revealed that the rule-based method preserves all the single defects present in the datasets. Furthermore, RSPA outperforms MCOW when aligning peaks, whereas MCOW is more efficient when all the data points in the datasets have equal priority.

Computer Vision Aided mmWave Beam Alignment in V2X Communications

Visual information, captured for example by cameras, can effectively reflect the sizes and locations of the environmental scattering objects, and thereby can be used to infer communications parameters like propagation directions, receiver powers, as well as the blockage status. In this paper, we propose a novel beam alignment framework that leverages images taken by cameras installed at the mobile user. Specifically, we utilize 3D object detection techniques to extract the size and location information of the dynamic vehicles around the mobile user, and design a deep neural network (DNN) to infer the optimal beam pair for transceivers without any pilot signal overhead. Moreover, to avoid performing beam alignment too frequently or too slowly, a beam coherence time (BCT) prediction method is developed based on the vision information. This can effectively improve the transmission rate compared with the beam alignment approach with the fixed BCT. Simulation results show that the proposed vision based beam alignment methods outperform the existing LIDAR and vision based solutions, and demand for much lower hardware cost and communication overhead.

Semi-microscopic Theory for the Current Rectification Phenomenon in Nanogap Molecular Devices.

A semi-microscopic theory is developed for heterogeneous electron transfer (HET) kinetics based on the energy level alignment approach at self-assembled monolayer (SAM) covered metal electrodes. Theory provides the electronic and molecular property-dependent equations for the HET rate constant (k0) and the transfer coefficient (α) for potential. k0 is formulated using the activation free energy as a product of the SAM covered metal work function (WF) and fractional electronic charge exchanged at the transition state (attained through the alignment of the frontier molecular orbital (FMO) energy level of the electroactive group with the WF of metal). k0 is a function of the metal jellium electronic screening length and dielectric and of the molecular self-assembly (through its dipole moment, size, and packing density) and the FMO energies of electroactive groups. The operative potential at the transition state is governed by α, which is a function of molecular spacer length and characteristic electronic-dipolar coupling length. The current rectification phenomenon in nanogap molecular devices is theoretically analyzed using equations for k0 and α for SAM covered source and drain electrodes. Theory unravels the LUMO or HOMO dichotomy for a given metal: (i) for the HOMO assisted ET, the metal with a high WF has a high current rectification ratio (RR), while (ii) for the LUMO assisted ET, the metal with a low WF has a high current RR in asymmetrical devices. Theory predicts the reversal in current rectification by altering the dipole moment of the anchoring molecule, the HOMO/LUMO energy of the electroactive groups, and the nature of the metal. Finally, theory shows qualitative and quantitative coherence with the reported experimental current-potential response of molecular device.

Multi-faceted metagenomic analysis of spacecraft associated surfaces reveal planetary protection relevant microbial composition.

The National Aeronautics and Space Administration (NASA) has been monitoring the microbial burden of spacecraft since the 1970's Viking missions. Originally culture-based and then focused 16S sequencing techniques were used, but we have now applied whole metagenomic sequencing to a variety of cleanroom samples at the Jet Propulsion Lab (JPL), including the Spacecraft Assembly Facility (SAF) with the goals of taxonomic identification and for functional assignment. Our samples included facility pre-filters, cleanroom vacuum debris, and surface wipes. The taxonomic composition was carried out by three different analysis tools to contrast marker, k-mer, and true alignment approaches. Hierarchical clustering analysis of the data separated vacuum particles from other SAF DNA samples. Vacuum particle samples were the most diverse while DNA samples from the ISO (International Standards Organization) compliant facilities and the SAF were the least diverse; all three were dominated by Proteobacteria. Wipe samples had higher diversity and were predominated by Actinobacteria, including human commensals Cutibacterium acnes and Corynebacterium spp. Taxa identified by the three methods were not identical, supporting the use of multiple methods for metagenome characterization. Likewise, functional annotation was performed using multiple methods. Vacuum particles and SAF samples contained strong signals of the tricarboxylic acid cycle and of amino acid biosynthesis, suggesting that many of the identified microorganisms have the ability to grow in nutrient-limited environments. In total, 18 samples generated high quality metagenome assembled genomes (MAG), which were dominated by Moraxella osloensis or Malassezia restricta. One M. osloensis MAG was assembled into a single circular scaffold and gene annotated. This study includes a rigorous quantitative determination of microbial loads and a qualitative dissection of microbial composition. Assembly of multiple specimens led to greater confidence for the identification of particular species and their predicted functional roles.

Compressive Sensing-Based Beam Alignment Schemes for Time-Varying Millimeter-Wave Channels

This paper considers the implementation of compressive sensing (CS) approaches for beam alignment (BA) in multiuser millimeter wave (mmWave) MIMO systems. We particularly consider wideband time-varying channels in the practical low SNR regime. We examine two different time scales for beam-switching in the BA training phase at both the base station (BS) and the user equipment (UE). We also compare different time scales for running the CS algorithm at the UE, with their corresponding overhead and complexity. We propose an overarching trial-based protocol that re- initializes the BA process at particular times. We also propose a new approach to designing the CS sensing matrix (SM), based on a deterministic construction. Rows of our proposed SM are Kronecker product decomposable, making it ideal for the BA problem. We show that when block-based beam switching is employed in combination with running the CS algorithm Every Epoch (CS-EE), our proposed SM gives superior performance compared to the other approaches. Moreover, our proposed overarching trial-based protocol enhances the performance even further. We also show that running the CS algorithm Every Block (CS-EB) outperforms CS-EE at the cost of higher complexity and overhead.

Shapify: Paths to SARS-CoV-2 frameshifting pseudoknot.

Multiple coronaviruses including MERS-CoV causing Middle East Respiratory Syndrome, SARS-CoV causing SARS, and SARS-CoV-2 causing COVID-19, use a mechanism known as -1 programmed ribosomal frameshifting (-1 PRF) to replicate. SARS-CoV-2 possesses a unique RNA pseudoknotted structure that stimulates -1 PRF. Targeting -1 PRF in SARS-CoV-2 to impair viral replication can improve patients' prognoses. Crucial to developing these therapies is understanding the structure of the SARS-CoV-2 -1 PRF pseudoknot. Our goal is to expand knowledge of -1 PRF structural conformations. Following a structural alignment approach, we identify similarities in -1 PRF pseudoknots of SARS-CoV-2, SARS-CoV, and MERS-CoV. We provide in-depth analysis of the SARS-CoV-2 and MERS-CoV -1 PRF pseudoknots, including reference and noteworthy mutated sequences. To better understand the impact of mutations, we provide insight on -1 PRF pseudoknot sequence mutations and their effect on resulting structures. We introduce Shapify, a novel algorithm that given an RNA sequence incorporates structural reactivity (SHAPE) data and partial structure information to output an RNA secondary structure prediction within a biologically sound hierarchical folding approach. Shapify enhances our understanding of SARS-CoV-2 -1 PRF pseudoknot conformations by providing energetically favourable predictions that are relevant to structure-function and may correlate with -1 PRF efficiency. Applied to the SARS-CoV-2 -1 PRF pseudoknot, Shapify unveils previously unknown paths from initial stems to pseudoknotted structures. By contextualizing our work with available experimental data, our structure predictions motivate future RNA structure-function research and can aid 3-D modeling of pseudoknots.

Characterization of the LED filament lamp for luminous intensity calibration

Light-emitting diode (LED) is now the main type of light source in global lighting marketing. However, traditional photometry with the incandescent standard lamp is not sufficient to implement high-level LED photometric measurement, since a considerable spectral mismatch error may exist if the photometer has a large general V(λ) mismatch index . The LED standard lamp is proposed to be the complementary transfer standard for photometry to reduce this measurement uncertainty. National Institute of Metrology, China (NIM) has previously reported a type of LED filament lamp for total luminous flux. In this article, NIM demonstrates another type of LED filament lamp for luminous intensity calibration. The LED filaments are sealed in a T90 cylinder glass bulb as emitters, and have a spectrum similar to the reference white LED spectrum considered in Commission on Illumination technical committee 2–90. The LED lamp uses the E27 screw base as the connector, and is compatible with the traditional alignment approaches. The warm-up time is less than 12 min. The output change of a group of 12 lamps, over nearly two years of storage and 1 h operation every day during the whole long-term test, is found to be ±0.2% or less for most of the lamps. The ageing effect of one lamp over a continuous 6500 h of burning is estimated to be −0.014% per 100 h, and it is significantly lower than 1% per 100 h, the typical drift rate of the traditional incandescent luminous intensity lamp. In the aspect of the coincidence with inverse square law, the LED lamp has a similar performance to the WI41/G lamp, the traditional luminous intensity lamp.

Smart contract-enabled consortium blockchains for the control of supply chain information distortion

Conflicts between supply chain members emerge because individual strategic actions may not be jointly optimal. Efforts to forecast consumer demand represent a source of conflict. The coordination of forecasts requires a powerful incentive alignment approach. This work proposes a smart contract equipped consortium blockchain system that creates an incentive structure that makes coordination with respect to forecasts economically appealing. Distortions of demand information due to uncoordinated forecasting are captured by a bullwhip measure that factors both forecast error and variance. Cooperation under the system is shown to help minimize this bullwhip measure, thus generating new outcomes for the participants that allow for a higher reward. Under a fixed payout structure, the system achieves credibility of continued cooperation, thus promoting an optimally coordinated equilibrium between the retailer and supplier. Blockchain technology represents a novel information system and consensus formation mechanism that can intermediate the behavior of a supply chain network.

Development of a Digital-Preneurship Measurement Instrument: Alignment Approach Through Project-Based Learning

<p style="text-align:justify">The purposes of this research are (a) to make an instrument to measure the achievement of project-based digitalpreneurship learning in students; (b) to describe the quality of the project-based digitalpreneurship learning achievement assessment instrument for vocational high school students; (c) to measure the competence and skills. This study employed to research and development as its methodology, using a combination procedure between Oriondo and Antonio and, Mardapi. The development procedure includes three stages: instrument design, instrument testing, and measurement. A total of 795 students who participated in the study at 5 vocational high schools in Yogyakarta's special region served as responders. Data analysis in this development uses the content validity of the formula Aiken V Index, confirmatory factor analysis establishes the construct's validity, and construct reliability. Measurement instrument development. Research findings that (a) the instrument is ready to be used in the measurement; (b) the quality of the project-based digitalpreneruship learning achievement assessment instrument for vocational high school students is well tested. This is evidenced by the validity test which has a high score >.8; The proof of construct validity in this study using CFA > .5; GOF chi-square value with a critical value of df = 289, X2 = 323, p-value with a critical value of .079, RMSEA with a critical value of RMSEA .019 (≤ .08), SMRM with a critical value of SRMR 0.030 (≤ .05), CFI with a critical value of CFI .990 (> .97), and an NFI with a critical value of NFI .989 ( > .90); (c) the measurement results are identified in the process that needs to be improved again regarding the digitalpreneurship skills of vocational high school students.</p>

Neural text generation in regulatory medical writing.

Background: A steep increase in new drug applications has increased the overhead of writing technical documents such as medication guides. Natural language processing can contribute to reducing this burden. Objective: To generate medication guides from texts that relate to prescription drug labeling information. Materials and Methods: We collected official drug label information from the DailyMed website. We focused on drug labels containing medication guide sections to train and test our model. To construct our training dataset, we aligned "source" text from the document with similar "target" text from the medication guide using three families of alignment techniques: global, manual, and heuristic alignment. The resulting source-target pairs were provided as input to a Pointer Generator Network, an abstractive text summarization model. Results: Global alignment produced the lowest ROUGE scores and relatively poor qualitative results, as running the model frequently resulted in mode collapse. Manual alignment also resulted in mode collapse, albeit higher ROUGE scores than global alignment. Within the family of heuristic alignment approaches, we compared different methods and found BM25-based alignments to produce significantly better summaries (at least 6.8 ROUGE points above the other techniques). This alignment surpassed both the global and manual alignments in terms of ROUGE and qualitative scoring. Conclusion: The results of this study indicate that a heuristic approach to generating inputs for an abstractive summarization model increased ROUGE scores, compared to a global or manual approach when automatically generating biomedical text. Such methods hold the potential to significantly reduce the manual labor burden in medical writing and related disciplines.

Improved Double Pulse Test for Accurate Dynamic Characterization of Medium Voltage SiC Devices

This article presents an improved double pulse test (DPT) for accurate dynamic characterization of the medium voltage (MV) silicon carbide device. The difference between low voltage (LV) and MV DPT setup grounding is first introduced, which results in different measurement considerations. Then, parasitic capacitances in the DPT and their impact on the DPT are discussed considering different grounding points and device connections. Approaches are proposed to minimize the impact of parasitic capacitances on DPT results. In addition, the impact of switching V-I timing alignment on the testing results is discussed, compared to that in the LV DPT; a V-I alignment approach is introduced for the MV DPT. A 10 kV/20 A SiC <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mosfet</small> -based DPT is taken as an example of the improved DPT, and test results show that it can minimize the impact from parasitic and improve the accuracy of the device switching loss estimation.